From fd7cfb5b72c8b179ff8421f831451a18477d1257 Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Thu, 26 Oct 2023 15:34:50 +0200 Subject: [PATCH] added video links --- .../_build/.doctrees/environment.pickle | Bin 539987 -> 540160 bytes .../_build/.doctrees/week41.doctree | Bin 786098 -> 786912 bytes .../_build/.doctrees/week42.doctree | Bin 613070 -> 614224 bytes .../_build/.doctrees/week43.doctree | Bin 1069738 -> 1489272 bytes .../_build/html/_sources/week41.ipynb | 428 +- .../_build/html/_sources/week42.ipynb | 779 +- .../_build/html/_sources/week43.ipynb | 576 +- doc/LectureNotes/_build/html/searchindex.js | 2 +- doc/LectureNotes/_build/html/week41.html | 128 +- doc/LectureNotes/_build/html/week42.html | 189 +- doc/LectureNotes/_build/html/week43.html | 7865 +++++- .../_build/jupyter_execute/week41.ipynb | 560 +- .../_build/jupyter_execute/week41.py | 4 + .../_build/jupyter_execute/week42.ipynb | 981 +- .../_build/jupyter_execute/week42.py | 4 + 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zY{>9T$nY!x%piXBt4>#<{7mT%E>E-$Ncc)Lo?-e%Nsx7h={mzSond;;FfC`8jx$Wd zfElEVIRmoAS3t5Err8YBYldkx!*rTq8U<`1%?#6KhUqfHG?`&~#3+LdGfamWra|Bn zq=%Ufvdb{tWtiqNOm7*cwaiN48Y~D{{uFL!;ZyiIC3&9R3{qstGrtADSn_N;7r$8Y zyqtcq6b_F{2OS7t5F* zBtgS6<`?J}$(Yjr_6A%E^lJ}YSh!T^*N=eeOZU@-#Y~fav6%TP{bDim?@8+1Kw{?M zqqwlZ*-5u7a6g60s(ov)I}xs@&~a=uQ#SmfMH5{O04FFb)?EOLJOZTQ6^=O>=TFBUn^&@UD_ z<0N-jiEOK)1z%Ld#zey74;Ja{HekU$0eD0-N7CxV%Uo3py)x<9rK1aa>l<@gw zl0__hUPBUyg%71+RKkZ6d6eEpB8MX5DltTXVwD&oYY7%ZB|e~UUhMADcnN#)d=Yy+ z+L&NjtDexFTFR--hxfQg?D+M~dDuPYYS<`aabM?+89=+jZ0x\n", @@ -12,8 +14,10 @@ }, { "cell_type": "markdown", - "id": "f46bd6b4", - "metadata": {}, + "id": "d981139a", + "metadata": { + "editable": true + }, "source": [ "# Week 42 Constructing a Neural Network code with introduction to Tensor flow\n", "**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and Facility for Rare Isotope Beams, Michigan State University\n", @@ -23,8 +27,10 @@ }, { "cell_type": "markdown", - "id": "8c0fa4d7", - "metadata": {}, + "id": "d3d2fdcb", + "metadata": { + "editable": true + }, "source": [ "## Plan for week 42\n", "\n", @@ -46,6 +52,10 @@ "\n", " * These lecture notes\n", "\n", + " * [Video of lecture](https://youtu.be/0q5-PhovchQ)\n", + "\n", + " * [Whiteboard notes](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesOct19.pdf)\n", + "\n", " * [Aurelien Geron's chapters 10-11](https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf)\n", "\n", " * For a more in depth discussion on neural networks we recommend Goodfellow et al chapters 6 and 7. \n", @@ -63,16 +73,20 @@ }, { "cell_type": "markdown", - "id": "89b6b637", - "metadata": {}, + "id": "8e0c0ad3", + "metadata": { + "editable": true + }, "source": [ "## Lecture Thursday October 19" ] }, { "cell_type": "markdown", - "id": "3a32ad82", - "metadata": {}, + "id": "6d072b79", + "metadata": { + "editable": true + }, "source": [ "## Review of the back propagation algorithm\n", "\n", @@ -84,8 +98,10 @@ }, { "cell_type": "markdown", - "id": "4f9291ee", - "metadata": {}, + "id": "6a5894d2", + "metadata": { + "editable": true + }, "source": [ "## Setting up the Back propagation algorithm\n", "\n", @@ -105,8 +121,10 @@ }, { "cell_type": "markdown", - "id": "7753981f", - "metadata": {}, + "id": "47296efd", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\delta_j^L = f'(z_j^L)\\frac{\\partial {\\cal C}}{\\partial (a_j^L)}.\n", @@ -115,16 +133,20 @@ }, { "cell_type": "markdown", - "id": "8b093c71", - "metadata": {}, + "id": "598d3a19", + "metadata": { + "editable": true + }, "source": [ "Then we compute the back propagate error for each $l=L-1,L-2,\\dots,2$ as" ] }, { "cell_type": "markdown", - "id": "96ca25bd", - "metadata": {}, + "id": "7077d9c2", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\delta_j^l = \\sum_k \\delta_k^{l+1}w_{kj}^{l+1}f'(z_j^l).\n", @@ -133,16 +155,20 @@ }, { "cell_type": "markdown", - "id": "a156d8bd", - "metadata": {}, + "id": "c62043b3", + "metadata": { + "editable": true + }, "source": [ "Finally, we update the weights and the biases using gradient descent for each $l=L-1,L-2,\\dots,2$ and update the weights and biases according to the rules" ] }, { "cell_type": "markdown", - "id": "f35c8afe", - "metadata": {}, + "id": "3307a4bc", + "metadata": { + "editable": true + }, "source": [ "$$\n", "w_{jk}^l\\leftarrow = w_{jk}^l- \\eta \\delta_j^la_k^{l-1},\n", @@ -151,8 +177,10 @@ }, { "cell_type": "markdown", - "id": "ffa6d322", - "metadata": {}, + "id": "50db23f1", + "metadata": { + "editable": true + }, "source": [ "$$\n", "b_j^l \\leftarrow b_j^l-\\eta \\frac{\\partial {\\cal C}}{\\partial b_j^l}=b_j^l-\\eta \\delta_j^l,\n", @@ -161,8 +189,10 @@ }, { "cell_type": "markdown", - "id": "7b6e59f6", - "metadata": {}, + "id": "0cf89ca4", + "metadata": { + "editable": true + }, "source": [ "The parameter $\\eta$ is the learning parameter discussed in connection with the gradient descent methods.\n", "Here it is convenient to use stochastic gradient descent (see the examples below) with mini-batches with an outer loop that steps through multiple epochs of training." @@ -170,8 +200,10 @@ }, { "cell_type": "markdown", - "id": "e93ff00c", - "metadata": {}, + "id": "d5374d6f", + "metadata": { + "editable": true + }, "source": [ "## Setting up a Multi-layer perceptron model for classification\n", "\n", @@ -196,8 +228,10 @@ }, { "cell_type": "markdown", - "id": "3c437395", - "metadata": {}, + "id": "fc8ce130", + "metadata": { + "editable": true + }, "source": [ "$$\n", "P(y = 0 \\mid \\boldsymbol{x}, \\boldsymbol{\\theta}) = \\frac{1}{1 + \\exp{(- \\boldsymbol{x}})} ,\n", @@ -206,16 +240,20 @@ }, { "cell_type": "markdown", - "id": "3d7b1140", - "metadata": {}, + "id": "8eaf0c3c", + "metadata": { + "editable": true + }, "source": [ "and" ] }, { "cell_type": "markdown", - "id": "e3df5aec", - "metadata": {}, + "id": "3caeb6b3", + "metadata": { + "editable": true + }, "source": [ "$$\n", "P(y = 1 \\mid \\boldsymbol{x}, \\boldsymbol{\\theta}) = 1 - P(y = 0 \\mid \\boldsymbol{x}, \\boldsymbol{\\theta}) ,\n", @@ -224,8 +262,10 @@ }, { "cell_type": "markdown", - "id": "63345646", - "metadata": {}, + "id": "cb5b4f3d", + "metadata": { + "editable": true + }, "source": [ "where $y \\in \\{0, 1\\}$ and $\\boldsymbol{\\theta}$ represents the weights and biases\n", "of our network." @@ -233,8 +273,10 @@ }, { "cell_type": "markdown", - "id": "6ac465b3", - "metadata": {}, + "id": "6edbd945", + "metadata": { + "editable": true + }, "source": [ "## Defining the cost function\n", "\n", @@ -243,8 +285,10 @@ }, { "cell_type": "markdown", - "id": "cf06b4a0", - "metadata": {}, + "id": "3e039295", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\mathcal{C}(\\boldsymbol{\\theta}) = - \\ln P(\\mathcal{D} \\mid \\boldsymbol{\\theta}) = - \\sum_{i=1}^n\n", @@ -254,8 +298,10 @@ }, { "cell_type": "markdown", - "id": "719f761f", - "metadata": {}, + "id": "60d3b57c", + "metadata": { + "editable": true + }, "source": [ "This last equality means that we can interpret our *cost* function as a sum over the *loss* function\n", "for each point in the dataset $\\mathcal{L}_i(\\boldsymbol{\\theta})$. \n", @@ -277,8 +323,10 @@ }, { "cell_type": "markdown", - "id": "342de1d9", - "metadata": {}, + "id": "9045875f", + "metadata": { + "editable": true + }, "source": [ "$$\n", "P(y_{ic} = 1 \\mid \\boldsymbol{x}_i, \\boldsymbol{\\theta}) = \\frac{\\exp{((\\boldsymbol{a}_i^{hidden})^T \\boldsymbol{w}_c)}}\n", @@ -288,8 +336,10 @@ }, { "cell_type": "markdown", - "id": "211a69ba", - "metadata": {}, + "id": "1d37a3a2", + "metadata": { + "editable": true + }, "source": [ "which reduces to the logistic function in the binary case. \n", "The likelihood of this $C$-class classifier\n", @@ -298,8 +348,10 @@ }, { "cell_type": "markdown", - "id": "5f0cd5a2", - "metadata": {}, + "id": "429c3549", + "metadata": { + "editable": true + }, "source": [ "$$\n", "P(\\mathcal{D} \\mid \\boldsymbol{\\theta}) = \\prod_{i=1}^n \\prod_{c=0}^{C-1} [P(y_{ic} = 1)]^{y_{ic}} .\n", @@ -308,16 +360,20 @@ }, { "cell_type": "markdown", - "id": "fe018e32", - "metadata": {}, + "id": "cde118d9", + "metadata": { + "editable": true + }, "source": [ "Again we take the negative log-likelihood to define our cost function:" ] }, { "cell_type": "markdown", - "id": "9d48faca", - "metadata": {}, + "id": "16740280", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\mathcal{C}(\\boldsymbol{\\theta}) = - \\log{P(\\mathcal{D} \\mid \\boldsymbol{\\theta})}.\n", @@ -326,8 +382,10 @@ }, { "cell_type": "markdown", - "id": "897c8b0c", - "metadata": {}, + "id": "a4b60c6f", + "metadata": { + "editable": true + }, "source": [ "See the logistic regression lectures for a full definition of the cost function.\n", "\n", @@ -336,8 +394,10 @@ }, { "cell_type": "markdown", - "id": "68347a7f", - "metadata": {}, + "id": "36cce044", + "metadata": { + "editable": true + }, "source": [ "## Example: binary classification problem\n", "\n", @@ -346,8 +406,10 @@ }, { "cell_type": "markdown", - "id": "8425d868", - "metadata": {}, + "id": "d2cc5185", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\mathcal{C}(\\boldsymbol{\\beta}) = - \\sum_{i=1}^n \\left(y_i\\log{p(y_i \\vert x_i,\\boldsymbol{\\beta})}+(1-y_i)\\log{1-p(y_i \\vert x_i,\\boldsymbol{\\beta})}\\right),\n", @@ -356,16 +418,20 @@ }, { "cell_type": "markdown", - "id": "9108d4ac", - "metadata": {}, + "id": "6f62ac34", + "metadata": { + "editable": true + }, "source": [ "where we had defined the logistic (sigmoid) function" ] }, { "cell_type": "markdown", - "id": "77e0ec3b", - "metadata": {}, + "id": "980d2595", + "metadata": { + "editable": true + }, "source": [ "$$\n", "p(y_i =1\\vert x_i,\\boldsymbol{\\beta})=\\frac{\\exp{(\\beta_0+\\beta_1 x_i)}}{1+\\exp{(\\beta_0+\\beta_1 x_i)}},\n", @@ -374,16 +440,20 @@ }, { "cell_type": "markdown", - "id": "64ed867c", - "metadata": {}, + "id": "07f96bba", + "metadata": { + "editable": true + }, "source": [ "and" ] }, { "cell_type": "markdown", - "id": "51819578", - "metadata": {}, + "id": "ab7ef463", + "metadata": { + "editable": true + }, "source": [ "$$\n", "p(y_i =0\\vert x_i,\\boldsymbol{\\beta})=1-p(y_i =1\\vert x_i,\\boldsymbol{\\beta}).\n", @@ -392,8 +462,10 @@ }, { "cell_type": "markdown", - "id": "db6532a5", - "metadata": {}, + "id": "712f14c5", + "metadata": { + "editable": true + }, "source": [ "The parameters $\\boldsymbol{\\beta}$ were defined using a minimization method like gradient descent or Newton-Raphson's method. \n", "\n", @@ -403,8 +475,10 @@ }, { "cell_type": "markdown", - "id": "24e5e213", - "metadata": {}, + "id": "efb3f21c", + "metadata": { + "editable": true + }, "source": [ "$$\n", "a_i^l = y_i = \\frac{\\exp{(z_i^l)}}{1+\\exp{(z_i^l)}},\n", @@ -413,16 +487,20 @@ }, { "cell_type": "markdown", - "id": "d398c961", - "metadata": {}, + "id": "661dd5e4", + "metadata": { + "editable": true + }, "source": [ "with" ] }, { "cell_type": "markdown", - "id": "236d161c", - "metadata": {}, + "id": "545879f3", + "metadata": { + "editable": true + }, "source": [ "$$\n", "z_i^l = \\sum_{j}w_{ij}^l a_j^{l-1}+b_i^l,\n", @@ -431,8 +509,10 @@ }, { "cell_type": "markdown", - "id": "25e3004d", - "metadata": {}, + "id": "20187a39", + "metadata": { + "editable": true + }, "source": [ "where the superscript $l-1$ indicates that these are the outputs from layer $l-1$.\n", "Our cost function at the final layer $l=L$ is now" @@ -440,8 +520,10 @@ }, { "cell_type": "markdown", - "id": "9440c725", - "metadata": {}, + "id": "ecd3c551", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\mathcal{C}(\\boldsymbol{W}) = - \\sum_{i=1}^n \\left(t_i\\log{a_i^L}+(1-t_i)\\log{(1-a_i^L)}\\right),\n", @@ -450,16 +532,20 @@ }, { "cell_type": "markdown", - "id": "782f5282", - "metadata": {}, + "id": "03d1bd2b", + "metadata": { + "editable": true + }, "source": [ "where we have defined the targets $t_i$. The derivatives of the cost function with respect to the output $a_i^L$ are then easily calculated and we get" ] }, { "cell_type": "markdown", - "id": "0e8498a5", - "metadata": {}, + "id": "1baaf3b0", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\frac{\\partial \\mathcal{C}(\\boldsymbol{W})}{\\partial a_i^L} = \\frac{a_i^L-t_i}{a_i^L(1-a_i^L)}.\n", @@ -468,16 +554,20 @@ }, { "cell_type": "markdown", - "id": "68398b35", - "metadata": {}, + "id": "9114b454", + "metadata": { + "editable": true + }, "source": [ "In case we use another activation function than the logistic one, we need to evaluate other derivatives." ] }, { "cell_type": "markdown", - "id": "19887152", - "metadata": {}, + "id": "19b41dd4", + "metadata": { + "editable": true + }, "source": [ "## The Softmax function\n", "In case we employ the more general case given by the Softmax equation, we need to evaluate the derivative of the activation function with respect to the activation $z_i^l$, that is we need" @@ -485,8 +575,10 @@ }, { "cell_type": "markdown", - "id": "80e8dc5d", - "metadata": {}, + "id": "bc1b97c5", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\frac{\\partial f(z_i^l)}{\\partial w_{jk}^l} =\n", @@ -496,16 +588,20 @@ }, { "cell_type": "markdown", - "id": "68d33776", - "metadata": {}, + "id": "52f2e768", + "metadata": { + "editable": true + }, "source": [ "For the Softmax function we have" ] }, { "cell_type": "markdown", - "id": "3c86943c", - "metadata": {}, + "id": "1a60c363", + "metadata": { + "editable": true + }, "source": [ "$$\n", "f(z_i^l) = \\frac{\\exp{(z_i^l)}}{\\sum_{m=1}^K\\exp{(z_m^l)}}.\n", @@ -514,16 +610,20 @@ }, { "cell_type": "markdown", - "id": "efe53876", - "metadata": {}, + "id": "93eb34b6", + "metadata": { + "editable": true + }, "source": [ "Its derivative with respect to $z_j^l$ gives" ] }, { "cell_type": "markdown", - "id": "fce5b9b2", - "metadata": {}, + "id": "aa26229f", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\frac{\\partial f(z_i^l)}{\\partial z_j^l}= f(z_i^l)\\left(\\delta_{ij}-f(z_j^l)\\right),\n", @@ -532,16 +632,20 @@ }, { "cell_type": "markdown", - "id": "97210471", - "metadata": {}, + "id": "1f075e8c", + "metadata": { + "editable": true + }, "source": [ "which in case of the simply binary model reduces to having $i=j$." ] }, { "cell_type": "markdown", - "id": "4f515591", - "metadata": {}, + "id": "b0cb8b0e", + "metadata": { + "editable": true + }, "source": [ "## Developing a code for doing neural networks with back propagation\n", "\n", @@ -562,8 +666,10 @@ }, { "cell_type": "markdown", - "id": "ec34f212", - "metadata": {}, + "id": "c4cd71b6", + "metadata": { + "editable": true + }, "source": [ "## Collect and pre-process data\n", "\n", @@ -610,8 +716,11 @@ { "cell_type": "code", "execution_count": 1, - "id": "e389e60e", - "metadata": {}, + "id": "ca43227b", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "%matplotlib inline\n", @@ -662,8 +771,10 @@ }, { "cell_type": "markdown", - "id": "9a264b82", - "metadata": {}, + "id": "79f4798f", + "metadata": { + "editable": true + }, "source": [ "## Train and test datasets\n", "\n", @@ -681,8 +792,11 @@ { "cell_type": "code", "execution_count": 2, - "id": "8750ea41", - "metadata": {}, + "id": "38e01634", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", @@ -716,8 +830,10 @@ }, { "cell_type": "markdown", - "id": "d3897eca", - "metadata": {}, + "id": "faca5ec2", + "metadata": { + "editable": true + }, "source": [ "## Define model and architecture\n", "\n", @@ -758,8 +874,10 @@ }, { "cell_type": "markdown", - "id": "ad593a03", - "metadata": {}, + "id": "e720f042", + "metadata": { + "editable": true + }, "source": [ "## Layers\n", "\n", @@ -796,8 +914,10 @@ }, { "cell_type": "markdown", - "id": "e37b3844", - "metadata": {}, + "id": "b4a3815d", + "metadata": { + "editable": true + }, "source": [ "## Weights and biases\n", "\n", @@ -815,8 +935,11 @@ { "cell_type": "code", "execution_count": 3, - "id": "3d909fc7", - "metadata": {}, + "id": "5c7ae6ce", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "# building our neural network\n", @@ -838,8 +961,10 @@ }, { "cell_type": "markdown", - "id": "b89c2d9f", - "metadata": {}, + "id": "bc289dbd", + "metadata": { + "editable": true + }, "source": [ "## Feed-forward pass\n", "\n", @@ -864,8 +989,10 @@ }, { "cell_type": "markdown", - "id": "435c0ced", - "metadata": {}, + "id": "3e93f012", + "metadata": { + "editable": true + }, "source": [ "## Matrix multiplications\n", "\n", @@ -899,8 +1026,11 @@ { "cell_type": "code", "execution_count": 4, - "id": "3037d7ab", - "metadata": {}, + "id": "31084597", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "# setup the feed-forward pass, subscript h = hidden layer\n", @@ -942,8 +1072,10 @@ }, { "cell_type": "markdown", - "id": "61c33a3f", - "metadata": {}, + "id": "93ca9a82", + "metadata": { + "editable": true + }, "source": [ "## Choose cost function and optimizer\n", "\n", @@ -971,8 +1103,10 @@ }, { "cell_type": "markdown", - "id": "665f44ff", - "metadata": {}, + "id": "59ad4e01", + "metadata": { + "editable": true + }, "source": [ "## Optimizing the cost function\n", "\n", @@ -1007,8 +1141,10 @@ }, { "cell_type": "markdown", - "id": "2d0168d0", - "metadata": {}, + "id": "d017d149", + "metadata": { + "editable": true + }, "source": [ "## Regularization\n", "\n", @@ -1039,8 +1175,10 @@ }, { "cell_type": "markdown", - "id": "9c0a8db3", - "metadata": {}, + "id": "3b624b6e", + "metadata": { + "editable": true + }, "source": [ "## Matrix multiplication\n", "\n", @@ -1078,8 +1216,11 @@ { "cell_type": "code", "execution_count": 5, - "id": "0bf3739e", - "metadata": {}, + "id": "39eabb7a", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "# to categorical turns our integer vector into a onehot representation\n", @@ -1154,8 +1295,10 @@ }, { "cell_type": "markdown", - "id": "33e198f3", - "metadata": {}, + "id": "22c14a38", + "metadata": { + "editable": true + }, "source": [ "## Improving performance\n", "\n", @@ -1173,8 +1316,10 @@ }, { "cell_type": "markdown", - "id": "932f6c5e", - "metadata": {}, + "id": "33d33cf6", + "metadata": { + "editable": true + }, "source": [ "## Full object-oriented implementation\n", "\n", @@ -1185,8 +1330,11 @@ { "cell_type": "code", "execution_count": 6, - "id": "91e351de", - "metadata": {}, + "id": "a5009498", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "class NeuralNetwork:\n", @@ -1292,8 +1440,10 @@ }, { "cell_type": "markdown", - "id": "e8c2feb6", - "metadata": {}, + "id": "68639daa", + "metadata": { + "editable": true + }, "source": [ "## Evaluate model performance on test data\n", "\n", @@ -1309,8 +1459,11 @@ { "cell_type": "code", "execution_count": 7, - "id": "1534af1b", - "metadata": {}, + "id": "487c6612", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "epochs = 100\n", @@ -1333,8 +1486,10 @@ }, { "cell_type": "markdown", - "id": "85627e28", - "metadata": {}, + "id": "c3b10024", + "metadata": { + "editable": true + }, "source": [ "## Adjust hyperparameters\n", "\n", @@ -1345,8 +1500,11 @@ { "cell_type": "code", "execution_count": 8, - "id": "19382903", - "metadata": {}, + "id": "7ab55f7a", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "eta_vals = np.logspace(-5, 1, 7)\n", @@ -1373,8 +1531,10 @@ }, { "cell_type": "markdown", - "id": "12bc42df", - "metadata": {}, + "id": "aa9d91e2", + "metadata": { + "editable": true + }, "source": [ "## Visualization" ] @@ -1382,8 +1542,11 @@ { "cell_type": "code", "execution_count": 9, - "id": "ec0dc239", - "metadata": {}, + "id": "ce6b84ae", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "# visual representation of grid search\n", @@ -1423,8 +1586,10 @@ }, { "cell_type": "markdown", - "id": "4dd39506", - "metadata": {}, + "id": "1d50ccf4", + "metadata": { + "editable": true + }, "source": [ "## scikit-learn implementation\n", "\n", @@ -1444,8 +1609,11 @@ { "cell_type": "code", "execution_count": 10, - "id": "d9dbb807", - "metadata": {}, + "id": "05cc9271", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "from sklearn.neural_network import MLPClassifier\n", @@ -1468,8 +1636,10 @@ }, { "cell_type": "markdown", - "id": "214af3ab", - "metadata": {}, + "id": "9f51a74d", + "metadata": { + "editable": true + }, "source": [ "## Visualization" ] @@ -1477,8 +1647,11 @@ { "cell_type": "code", "execution_count": 11, - "id": "d57415ac", - "metadata": {}, + "id": "38a896b8", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "# optional\n", @@ -1519,8 +1692,10 @@ }, { "cell_type": "markdown", - "id": "fe7af77c", - "metadata": {}, + "id": "f8ee3eb3", + "metadata": { + "editable": true + }, "source": [ "## Testing our code for the XOR, OR and AND gates\n", "\n", @@ -1544,8 +1719,10 @@ }, { "cell_type": "markdown", - "id": "7e12b1cf", - "metadata": {}, + "id": "5c0e406c", + "metadata": { + "editable": true + }, "source": [ "## The AND and XOR Gates\n", "\n", @@ -1580,8 +1757,10 @@ }, { "cell_type": "markdown", - "id": "4b5002b4", - "metadata": {}, + "id": "f52ee7dd", + "metadata": { + "editable": true + }, "source": [ "## Representing the Data Sets\n", "\n", @@ -1590,8 +1769,10 @@ }, { "cell_type": "markdown", - "id": "a44df1a3", - "metadata": {}, + "id": "f2634e6f", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\boldsymbol{X}=\\begin{bmatrix} 0 & 0 \\\\\n", @@ -1603,16 +1784,20 @@ }, { "cell_type": "markdown", - "id": "acdb4e08", - "metadata": {}, + "id": "a39715fe", + "metadata": { + "editable": true + }, "source": [ "while the vector of outputs is $\\boldsymbol{y}^T=[0,1,1,0]$ for the XOR gate, $\\boldsymbol{y}^T=[0,0,0,1]$ for the AND gate and $\\boldsymbol{y}^T=[0,1,1,1]$ for the OR gate." ] }, { "cell_type": "markdown", - "id": "0567fd0f", - "metadata": {}, + "id": "8bff01b2", + "metadata": { + "editable": true + }, "source": [ "## Setting up the Neural Network\n", "\n", @@ -1622,8 +1807,11 @@ { "cell_type": "code", "execution_count": 12, - "id": "412401df", - "metadata": {}, + "id": "9d94da1e", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "\"\"\"\n", @@ -1695,16 +1883,20 @@ }, { "cell_type": "markdown", - "id": "53c52dab", - "metadata": {}, + "id": "fdbce6ba", + "metadata": { + "editable": true + }, "source": [ "Not an impressive result, but this was our first forward pass with randomly assigned weights. Let us now add the full network with the back-propagation algorithm discussed above." ] }, { "cell_type": "markdown", - "id": "7d192a02", - "metadata": {}, + "id": "f5cbb06d", + "metadata": { + "editable": true + }, "source": [ "## The Code using Scikit-Learn" ] @@ -1712,8 +1904,11 @@ { "cell_type": "code", "execution_count": 13, - "id": "766d5af6", - "metadata": {}, + "id": "edf69ad3", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "# import necessary packages\n", @@ -1777,8 +1972,10 @@ }, { "cell_type": "markdown", - "id": "9b182ae1", - "metadata": {}, + "id": "d5e5d8a0", + "metadata": { + "editable": true + }, "source": [ "## Building neural networks in Tensorflow and Keras\n", "\n", @@ -1793,8 +1990,10 @@ }, { "cell_type": "markdown", - "id": "60683ec5", - "metadata": {}, + "id": "8326a878", + "metadata": { + "editable": true + }, "source": [ "## Tensorflow\n", "\n", @@ -1826,8 +2025,11 @@ { "cell_type": "code", "execution_count": 14, - "id": "8a0c6901", - "metadata": {}, + "id": "dd988cfc", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "pip3 install tensorflow" @@ -1835,8 +2037,10 @@ }, { "cell_type": "markdown", - "id": "b66e0227", - "metadata": {}, + "id": "d2f8d6f3", + "metadata": { + "editable": true + }, "source": [ "and/or if you use **anaconda**, just write (or install from the graphical user interface)\n", "(current release of CPU-only TensorFlow)" @@ -1845,8 +2049,11 @@ { "cell_type": "code", "execution_count": 15, - "id": "df994f58", - "metadata": {}, + "id": "fdaffccc", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "conda create -n tf tensorflow\n", @@ -1855,8 +2062,10 @@ }, { "cell_type": "markdown", - "id": "b9005559", - "metadata": {}, + "id": "80db034a", + "metadata": { + "editable": true + }, "source": [ "To install the current release of GPU TensorFlow" ] @@ -1864,8 +2073,11 @@ { "cell_type": "code", "execution_count": 16, - "id": "7287b5eb", - "metadata": {}, + "id": "e632c541", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "conda create -n tf-gpu tensorflow-gpu\n", @@ -1874,8 +2086,10 @@ }, { "cell_type": "markdown", - "id": "c066b083", - "metadata": {}, + "id": "605ac1fd", + "metadata": { + "editable": true + }, "source": [ "## Using Keras\n", "\n", @@ -1887,8 +2101,11 @@ { "cell_type": "code", "execution_count": 17, - "id": "6582adea", - "metadata": {}, + "id": "11e69ce0", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "conda install keras" @@ -1896,8 +2113,10 @@ }, { "cell_type": "markdown", - "id": "7d305596", - "metadata": {}, + "id": "176bf3ac", + "metadata": { + "editable": true + }, "source": [ "You can look up the [instructions here](https://keras.io/) for more information.\n", "\n", @@ -1906,8 +2125,10 @@ }, { "cell_type": "markdown", - "id": "a4508850", - "metadata": {}, + "id": "7a085449", + "metadata": { + "editable": true + }, "source": [ "## Collect and pre-process data\n", "\n", @@ -1917,8 +2138,11 @@ { "cell_type": "code", "execution_count": 18, - "id": "5f2256f6", - "metadata": {}, + "id": "d8bae540", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "# import necessary packages\n", @@ -1969,8 +2193,11 @@ { "cell_type": "code", "execution_count": 19, - "id": "a5dfa0e9", - "metadata": {}, + "id": "5608d691", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "from tensorflow.keras.layers import Input\n", @@ -1995,8 +2222,11 @@ { "cell_type": "code", "execution_count": 20, - "id": "dd935ce0", - "metadata": {}, + "id": "7bce7422", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "\n", @@ -2022,8 +2252,11 @@ { "cell_type": "code", "execution_count": 21, - "id": "67158cb2", - "metadata": {}, + "id": "65a68468", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "DNN_keras = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)\n", @@ -2046,8 +2279,11 @@ { "cell_type": "code", "execution_count": 22, - "id": "86d74ee3", - "metadata": {}, + "id": "45ae200f", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "# optional\n", @@ -2085,8 +2321,10 @@ }, { "cell_type": "markdown", - "id": "563d3f68", - "metadata": {}, + "id": "b955ad39", + "metadata": { + "editable": true + }, "source": [ "## The Breast Cancer Data, now with Keras" ] @@ -2094,8 +2332,11 @@ { "cell_type": "code", "execution_count": 23, - "id": "34e6467a", - "metadata": {}, + "id": "8ed2e257", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "\n", @@ -2268,8 +2509,10 @@ }, { "cell_type": "markdown", - "id": "09879108", - "metadata": {}, + "id": "107bab25", + "metadata": { + "editable": true + }, "source": [ "## Fine-tuning neural network hyperparameters\n", "\n", @@ -2294,8 +2537,10 @@ }, { "cell_type": "markdown", - "id": "f8ec1769", - "metadata": {}, + "id": "53ad43ce", + "metadata": { + "editable": true + }, "source": [ "## Hidden layers\n", "\n", @@ -2315,8 +2560,10 @@ }, { "cell_type": "markdown", - "id": "43cc1fe5", - "metadata": {}, + "id": "6f613497", + "metadata": { + "editable": true + }, "source": [ "## Which activation function should I use?\n", "\n", @@ -2344,8 +2591,10 @@ }, { "cell_type": "markdown", - "id": "a9cbce9f", - "metadata": {}, + "id": "91843fee", + "metadata": { + "editable": true + }, "source": [ "## Is the Logistic activation function (Sigmoid) our choice?\n", "\n", @@ -2374,8 +2623,10 @@ }, { "cell_type": "markdown", - "id": "2dfb3f9a", - "metadata": {}, + "id": "83535426", + "metadata": { + "editable": true + }, "source": [ "## The derivative of the Logistic funtion\n", "\n", @@ -2410,8 +2661,10 @@ }, { "cell_type": "markdown", - "id": "f806c047", - "metadata": {}, + "id": "37bdca60", + "metadata": { + "editable": true + }, "source": [ "## The RELU function family\n", "\n", @@ -2433,8 +2686,10 @@ }, { "cell_type": "markdown", - "id": "ef9e2a08", - "metadata": {}, + "id": "11ae19d2", + "metadata": { + "editable": true + }, "source": [ "$$\n", "ELU(z) = \\left\\{\\begin{array}{cc} \\alpha\\left( \\exp{(z)}-1\\right) & z < 0,\\\\ z & z \\ge 0.\\end{array}\\right.\n", @@ -2443,8 +2698,10 @@ }, { "cell_type": "markdown", - "id": "2e2750d8", - "metadata": {}, + "id": "f3a54f08", + "metadata": { + "editable": true + }, "source": [ "## Which activation function should we use?\n", "\n", @@ -2464,8 +2721,10 @@ }, { "cell_type": "markdown", - "id": "4e566f13", - "metadata": {}, + "id": "4dd226db", + "metadata": { + "editable": true + }, "source": [ "## More on activation functions, output layers\n", "\n", @@ -2482,8 +2741,10 @@ }, { "cell_type": "markdown", - "id": "03205666", - "metadata": {}, + "id": "f9521d8f", + "metadata": { + "editable": true + }, "source": [ "## Batch Normalization\n", "\n", @@ -2502,8 +2763,10 @@ }, { "cell_type": "markdown", - "id": "ad7c3e53", - "metadata": {}, + "id": "080c7f12", + "metadata": { + "editable": true + }, "source": [ "## Dropout\n", "\n", @@ -2518,8 +2781,10 @@ }, { "cell_type": "markdown", - "id": "c3b98a7c", - "metadata": {}, + "id": "963e7d21", + "metadata": { + "editable": true + }, "source": [ "## Gradient Clipping\n", "\n", @@ -2535,8 +2800,10 @@ }, { "cell_type": "markdown", - "id": "e7f21477", - "metadata": {}, + "id": "0b69f45e", + "metadata": { + "editable": true + }, "source": [ "## A very nice website on Neural Networks\n", "\n", @@ -2545,8 +2812,10 @@ }, { "cell_type": "markdown", - "id": "54968291", - "metadata": {}, + "id": "3585dfbf", + "metadata": { + "editable": true + }, "source": [ "## A top-down perspective on Neural networks\n", "\n", @@ -2588,8 +2857,10 @@ }, { "cell_type": "markdown", - "id": "4500b85e", - "metadata": {}, + "id": "dd7c4575", + "metadata": { + "editable": true + }, "source": [ "## Limitations of supervised learning with deep networks\n", "\n", @@ -2615,25 +2886,7 @@ ] } ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.10" - } - }, + "metadata": {}, "nbformat": 4, "nbformat_minor": 5 } diff --git a/doc/LectureNotes/_build/html/_sources/week43.ipynb b/doc/LectureNotes/_build/html/_sources/week43.ipynb index 4e2246cb2..94b84df7a 100644 --- a/doc/LectureNotes/_build/html/_sources/week43.ipynb +++ b/doc/LectureNotes/_build/html/_sources/week43.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "082f351e", + "id": "42ce64ba", "metadata": { "editable": true }, @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "a99ac46a", + "id": "c987b86b", "metadata": { "editable": true }, @@ -29,7 +29,7 @@ }, { "cell_type": "markdown", - "id": "a65014fa", + "id": "0dc0d6ca", "metadata": { "editable": true }, @@ -54,6 +54,8 @@ "\n", " * Solving differential equations with Neural Networks and intro to **Tensorflow** with examples.\n", "\n", + " * [Video of lecture](https://youtu.be/_-AwbBh4G-8)\n", + "\n", " * Readings and Videos:\n", "\n", " * These lecture notes\n", @@ -75,7 +77,7 @@ }, { "cell_type": "markdown", - "id": "491f86f0", + "id": "f4d3253b", "metadata": { "editable": true }, @@ -89,7 +91,7 @@ }, { "cell_type": "markdown", - "id": "9878a552", + "id": "9744596b", "metadata": { "editable": true }, @@ -106,7 +108,7 @@ }, { "cell_type": "markdown", - "id": "acd9eaf4", + "id": "bd189f93", "metadata": { "editable": true }, @@ -116,7 +118,7 @@ }, { "cell_type": "markdown", - "id": "ac6985dd", + "id": "e901e3d4", "metadata": { "editable": true }, @@ -153,7 +155,7 @@ }, { "cell_type": "markdown", - "id": "c75ca508", + "id": "fca381f9", "metadata": { "editable": true }, @@ -191,7 +193,7 @@ }, { "cell_type": "markdown", - "id": "6b4c09b9", + "id": "f0252121", "metadata": { "editable": true }, @@ -203,7 +205,7 @@ }, { "cell_type": "markdown", - "id": "fe419b85", + "id": "f59fc5ee", "metadata": { "editable": true }, @@ -218,7 +220,7 @@ }, { "cell_type": "markdown", - "id": "79ddb224", + "id": "1d74ec2a", "metadata": { "editable": true }, @@ -248,7 +250,7 @@ }, { "cell_type": "markdown", - "id": "5b9b5e41", + "id": "8e6565f3", "metadata": { "editable": true }, @@ -260,7 +262,7 @@ }, { "cell_type": "markdown", - "id": "c319cc25", + "id": "7f55beb5", "metadata": { "editable": true }, @@ -273,7 +275,7 @@ }, { "cell_type": "markdown", - "id": "edf4b6c5", + "id": "ba515e4f", "metadata": { "editable": true }, @@ -283,7 +285,7 @@ }, { "cell_type": "markdown", - "id": "521c408b", + "id": "1007d026", "metadata": { "editable": true }, @@ -298,7 +300,7 @@ }, { "cell_type": "markdown", - "id": "22aa491a", + "id": "b1ee686a", "metadata": { "editable": true }, @@ -308,7 +310,7 @@ }, { "cell_type": "markdown", - "id": "f0ae234a", + "id": "2820cfdd", "metadata": { "editable": true }, @@ -321,7 +323,7 @@ }, { "cell_type": "markdown", - "id": "5c980f5b", + "id": "5b5f6d64", "metadata": { "editable": true }, @@ -331,7 +333,7 @@ }, { "cell_type": "markdown", - "id": "82fb504e", + "id": "0814e6d9", "metadata": { "editable": true }, @@ -346,7 +348,7 @@ }, { "cell_type": "markdown", - "id": "cfd54537", + "id": "9da10b99", "metadata": { "editable": true }, @@ -356,7 +358,7 @@ }, { "cell_type": "markdown", - "id": "2f823c42", + "id": "c0f3ede6", "metadata": { "editable": true }, @@ -371,7 +373,7 @@ }, { "cell_type": "markdown", - "id": "7cad12ae", + "id": "f00bc3ad", "metadata": { "editable": true }, @@ -381,7 +383,7 @@ }, { "cell_type": "markdown", - "id": "0e9795a8", + "id": "da749419", "metadata": { "editable": true }, @@ -394,7 +396,7 @@ }, { "cell_type": "markdown", - "id": "4ee23a12", + "id": "2e9135f9", "metadata": { "editable": true }, @@ -404,7 +406,7 @@ }, { "cell_type": "markdown", - "id": "1c156334", + "id": "a0d879df", "metadata": { "editable": true }, @@ -420,7 +422,7 @@ }, { "cell_type": "markdown", - "id": "24433060", + "id": "988a9a20", "metadata": { "editable": true }, @@ -430,7 +432,7 @@ }, { "cell_type": "markdown", - "id": "e2664fe2", + "id": "354ad4af", "metadata": { "editable": true }, @@ -443,7 +445,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "4966b41e", + "id": "1f04aac4", "metadata": { "collapsed": false, "editable": true @@ -513,7 +515,7 @@ }, { "cell_type": "markdown", - "id": "2d227b7a", + "id": "6e12253c", "metadata": { "editable": true }, @@ -523,7 +525,7 @@ }, { "cell_type": "markdown", - "id": "4a614a2f", + "id": "4ca80040", "metadata": { "editable": true }, @@ -534,7 +536,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "a2401c4c", + "id": "1b657a96", "metadata": { "collapsed": false, "editable": true @@ -599,7 +601,7 @@ }, { "cell_type": "markdown", - "id": "2b0654f9", + "id": "9f870fcf", "metadata": { "editable": true }, @@ -618,7 +620,7 @@ }, { "cell_type": "markdown", - "id": "36d7ed90", + "id": "75623483", "metadata": { "editable": true }, @@ -640,7 +642,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "f3149e44", + "id": "7884cf44", "metadata": { "collapsed": false, "editable": true @@ -781,7 +783,7 @@ }, { "cell_type": "markdown", - "id": "3b714e5a", + "id": "a9747db3", "metadata": { "editable": true }, @@ -797,7 +799,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "a187e1f4", + "id": "b394ebc2", "metadata": { "collapsed": false, "editable": true @@ -810,7 +812,7 @@ }, { "cell_type": "markdown", - "id": "14693dc1", + "id": "734ce228", "metadata": { "editable": true }, @@ -822,7 +824,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "6f791e05", + "id": "51396f90", "metadata": { "collapsed": false, "editable": true @@ -844,7 +846,7 @@ }, { "cell_type": "markdown", - "id": "02dca912", + "id": "44646fc6", "metadata": { "editable": true }, @@ -860,7 +862,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "8e150ca8", + "id": "912285f7", "metadata": { "collapsed": false, "editable": true @@ -898,7 +900,7 @@ }, { "cell_type": "markdown", - "id": "a2ba6469", + "id": "9297b955", "metadata": { "editable": true }, @@ -911,7 +913,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "2fd8a7d1", + "id": "39943643", "metadata": { "collapsed": false, "editable": true @@ -932,7 +934,7 @@ }, { "cell_type": "markdown", - "id": "811b2be7", + "id": "53f4d1d2", "metadata": { "editable": true }, @@ -948,7 +950,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "12fb8899", + "id": "90222b1a", "metadata": { "collapsed": false, "editable": true @@ -1006,7 +1008,7 @@ }, { "cell_type": "markdown", - "id": "9cd08f7a", + "id": "f3dd3611", "metadata": { "editable": true }, @@ -1021,7 +1023,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "3b609fd5", + "id": "ded43dca", "metadata": { "collapsed": false, "editable": true @@ -1042,7 +1044,7 @@ }, { "cell_type": "markdown", - "id": "7b55fa66", + "id": "771c53a1", "metadata": { "editable": true }, @@ -1066,7 +1068,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "c0f9ff63", + "id": "56ef870b", "metadata": { "collapsed": false, "editable": true @@ -1538,7 +1540,7 @@ }, { "cell_type": "markdown", - "id": "e406c59d", + "id": "cefab2ac", "metadata": { "editable": true }, @@ -1550,7 +1552,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "0c6d743d", + "id": "6d5b49a1", "metadata": { "collapsed": false, "editable": true @@ -1594,7 +1596,7 @@ }, { "cell_type": "markdown", - "id": "150ef783", + "id": "f857ae2b", "metadata": { "editable": true }, @@ -1610,7 +1612,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "585ea4aa", + "id": "2a92f970", "metadata": { "collapsed": false, "editable": true @@ -1625,7 +1627,7 @@ }, { "cell_type": "markdown", - "id": "169932be", + "id": "28236066", "metadata": { "editable": true }, @@ -1636,7 +1638,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "7559a5a7", + "id": "b3c6c87e", "metadata": { "collapsed": false, "editable": true @@ -1651,7 +1653,7 @@ }, { "cell_type": "markdown", - "id": "ee912b2a", + "id": "f5b6209f", "metadata": { "editable": true }, @@ -1667,7 +1669,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "b576d527", + "id": "59d5742b", "metadata": { "collapsed": false, "editable": true @@ -1681,7 +1683,7 @@ }, { "cell_type": "markdown", - "id": "e98b0dd4", + "id": "13e834ae", "metadata": { "editable": true }, @@ -1696,7 +1698,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "2508ac64", + "id": "ab390311", "metadata": { "collapsed": false, "editable": true @@ -1722,7 +1724,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "3b12683c", + "id": "f2d67c2e", "metadata": { "collapsed": false, "editable": true @@ -1737,7 +1739,7 @@ }, { "cell_type": "markdown", - "id": "e79ca9e9", + "id": "e350f60e", "metadata": { "editable": true }, @@ -1748,7 +1750,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "2238df76", + "id": "211a328d", "metadata": { "collapsed": false, "editable": true @@ -1763,7 +1765,7 @@ }, { "cell_type": "markdown", - "id": "f698a332", + "id": "8425a8fb", "metadata": { "editable": true }, @@ -1774,7 +1776,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "467be649", + "id": "1070e9eb", "metadata": { "collapsed": false, "editable": true @@ -1794,7 +1796,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "65fb9800", + "id": "c7171327", "metadata": { "collapsed": false, "editable": true @@ -1809,7 +1811,7 @@ }, { "cell_type": "markdown", - "id": "93cdd2a4", + "id": "9c37cc8a", "metadata": { "editable": true }, @@ -1824,7 +1826,7 @@ { "cell_type": "code", "execution_count": 20, - "id": "df4b428c", + "id": "f33e3872", "metadata": { "collapsed": false, "editable": true @@ -1861,7 +1863,7 @@ }, { "cell_type": "markdown", - "id": "00f7684a", + "id": "98281e67", "metadata": { "editable": true }, @@ -1874,7 +1876,7 @@ { "cell_type": "code", "execution_count": 21, - "id": "8bec151f", + "id": "24031566", "metadata": { "collapsed": false, "editable": true @@ -1897,7 +1899,7 @@ }, { "cell_type": "markdown", - "id": "c32f02fd", + "id": "8302830f", "metadata": { "editable": true }, @@ -1907,7 +1909,7 @@ }, { "cell_type": "markdown", - "id": "3434c341", + "id": "9003b71a", "metadata": { "editable": true }, @@ -1917,7 +1919,7 @@ }, { "cell_type": "markdown", - "id": "aea3ed79", + "id": "4446e61e", "metadata": { "editable": true }, @@ -1946,7 +1948,7 @@ }, { "cell_type": "markdown", - "id": "9aa79782", + "id": "de6e80a8", "metadata": { "editable": true }, @@ -1996,7 +1998,7 @@ { "cell_type": "code", "execution_count": 22, - "id": "cade684e", + "id": "011b021a", "metadata": { "collapsed": false, "editable": true @@ -2049,7 +2051,7 @@ }, { "cell_type": "markdown", - "id": "380d9d9d", + "id": "4de43fe8", "metadata": { "editable": true }, @@ -2070,7 +2072,7 @@ { "cell_type": "code", "execution_count": 23, - "id": "73cf1180", + "id": "d9281e06", "metadata": { "collapsed": false, "editable": true @@ -2108,7 +2110,7 @@ }, { "cell_type": "markdown", - "id": "8a01257a", + "id": "5d86b543", "metadata": { "editable": true }, @@ -2152,7 +2154,7 @@ }, { "cell_type": "markdown", - "id": "54e95850", + "id": "02df2616", "metadata": { "editable": true }, @@ -2192,7 +2194,7 @@ }, { "cell_type": "markdown", - "id": "62c1a1b0", + "id": "0a17cfeb", "metadata": { "editable": true }, @@ -2213,7 +2215,7 @@ { "cell_type": "code", "execution_count": 24, - "id": "aa587add", + "id": "94dd542a", "metadata": { "collapsed": false, "editable": true @@ -2239,7 +2241,7 @@ }, { "cell_type": "markdown", - "id": "d4249afa", + "id": "7c13f0a8", "metadata": { "editable": true }, @@ -2267,7 +2269,7 @@ }, { "cell_type": "markdown", - "id": "b699e117", + "id": "d1bae3d4", "metadata": { "editable": true }, @@ -2304,7 +2306,7 @@ { "cell_type": "code", "execution_count": 25, - "id": "8a4dec51", + "id": "20c03478", "metadata": { "collapsed": false, "editable": true @@ -2350,7 +2352,7 @@ }, { "cell_type": "markdown", - "id": "c2fbafa2", + "id": "42c28741", "metadata": { "editable": true }, @@ -2381,7 +2383,7 @@ }, { "cell_type": "markdown", - "id": "297dd16a", + "id": "62954b00", "metadata": { "editable": true }, @@ -2419,7 +2421,7 @@ }, { "cell_type": "markdown", - "id": "9a4bdbd5", + "id": "9f94aff8", "metadata": { "editable": true }, @@ -2453,7 +2455,7 @@ }, { "cell_type": "markdown", - "id": "aa585f10", + "id": "bf73f8e5", "metadata": { "editable": true }, @@ -2494,7 +2496,7 @@ { "cell_type": "code", "execution_count": 26, - "id": "21ec5e9c", + "id": "9ffb0561", "metadata": { "collapsed": false, "editable": true @@ -2573,7 +2575,7 @@ }, { "cell_type": "markdown", - "id": "fe383853", + "id": "3b19f8e5", "metadata": { "editable": true }, @@ -2594,7 +2596,7 @@ }, { "cell_type": "markdown", - "id": "48afb81d", + "id": "443db3c1", "metadata": { "editable": true }, @@ -2608,7 +2610,7 @@ { "cell_type": "code", "execution_count": 27, - "id": "223c5ea5", + "id": "5a7db6a1", "metadata": { "collapsed": false, "editable": true @@ -2718,7 +2720,7 @@ }, { "cell_type": "markdown", - "id": "8e4de167", + "id": "fe73ada1", "metadata": { "editable": true }, @@ -2737,7 +2739,7 @@ { "cell_type": "code", "execution_count": 28, - "id": "e299478f", + "id": "3c71da93", "metadata": { "collapsed": false, "editable": true @@ -2764,7 +2766,7 @@ }, { "cell_type": "markdown", - "id": "1c58f8b5", + "id": "c6d594b6", "metadata": { "editable": true }, @@ -2778,7 +2780,7 @@ { "cell_type": "code", "execution_count": 29, - "id": "e2e095b5", + "id": "bb8c3746", "metadata": { "collapsed": false, "editable": true @@ -2809,7 +2811,7 @@ }, { "cell_type": "markdown", - "id": "9bcbed3e", + "id": "c460a08f", "metadata": { "editable": true }, @@ -2820,7 +2822,7 @@ { "cell_type": "code", "execution_count": 30, - "id": "70cd9e5f", + "id": "ae408ce7", "metadata": { "collapsed": false, "editable": true @@ -2864,7 +2866,7 @@ }, { "cell_type": "markdown", - "id": "014fa73f", + "id": "255351d8", "metadata": { "editable": true }, @@ -2887,7 +2889,7 @@ { "cell_type": "code", "execution_count": 31, - "id": "f8e412f7", + "id": "45102bf4", "metadata": { "collapsed": false, "editable": true @@ -2914,7 +2916,7 @@ }, { "cell_type": "markdown", - "id": "d05866a6", + "id": "9ed166d7", "metadata": { "editable": true }, @@ -2925,7 +2927,7 @@ { "cell_type": "code", "execution_count": 32, - "id": "1af34a20", + "id": "e9fb271f", "metadata": { "collapsed": false, "editable": true @@ -2970,7 +2972,7 @@ }, { "cell_type": "markdown", - "id": "372abcde", + "id": "6cbb4072", "metadata": { "editable": true }, @@ -2988,7 +2990,7 @@ }, { "cell_type": "markdown", - "id": "6b3fc4a5", + "id": "2702932a", "metadata": { "editable": true }, @@ -3023,7 +3025,7 @@ { "cell_type": "code", "execution_count": 33, - "id": "5f88d47b", + "id": "a66fc7b6", "metadata": { "collapsed": false, "editable": true @@ -3035,7 +3037,7 @@ }, { "cell_type": "markdown", - "id": "fe89b2bd", + "id": "3c99a292", "metadata": { "editable": true }, @@ -3047,7 +3049,7 @@ { "cell_type": "code", "execution_count": 34, - "id": "51009ec1", + "id": "71e9f24b", "metadata": { "collapsed": false, "editable": true @@ -3060,7 +3062,7 @@ }, { "cell_type": "markdown", - "id": "6b00853a", + "id": "a623588f", "metadata": { "editable": true }, @@ -3071,7 +3073,7 @@ { "cell_type": "code", "execution_count": 35, - "id": "27d84846", + "id": "fa19ee75", "metadata": { "collapsed": false, "editable": true @@ -3084,7 +3086,7 @@ }, { "cell_type": "markdown", - "id": "efb95ce2", + "id": "de5fec4a", "metadata": { "editable": true }, @@ -3099,7 +3101,7 @@ { "cell_type": "code", "execution_count": 36, - "id": "ff0d76a6", + "id": "edb61bdb", "metadata": { "collapsed": false, "editable": true @@ -3111,7 +3113,7 @@ }, { "cell_type": "markdown", - "id": "16ceb812", + "id": "fd72dabb", "metadata": { "editable": true }, @@ -3123,7 +3125,7 @@ }, { "cell_type": "markdown", - "id": "d1be0e7f", + "id": "b8262ab5", "metadata": { "editable": true }, @@ -3136,7 +3138,7 @@ { "cell_type": "code", "execution_count": 37, - "id": "3f300c00", + "id": "0b88258b", "metadata": { "collapsed": false, "editable": true @@ -3191,7 +3193,7 @@ { "cell_type": "code", "execution_count": 38, - "id": "fa0d45c3", + "id": "5000582f", "metadata": { "collapsed": false, "editable": true @@ -3220,7 +3222,7 @@ { "cell_type": "code", "execution_count": 39, - "id": "8cdc8534", + "id": "a473cac3", "metadata": { "collapsed": false, "editable": true @@ -3250,7 +3252,7 @@ { "cell_type": "code", "execution_count": 40, - "id": "2fbe350d", + "id": "041ff977", "metadata": { "collapsed": false, "editable": true @@ -3277,7 +3279,7 @@ { "cell_type": "code", "execution_count": 41, - "id": "4eae49aa", + "id": "1821d39d", "metadata": { "collapsed": false, "editable": true @@ -3319,7 +3321,7 @@ }, { "cell_type": "markdown", - "id": "43439e76", + "id": "c8b43dbc", "metadata": { "editable": true }, @@ -3330,7 +3332,7 @@ { "cell_type": "code", "execution_count": 42, - "id": "dabd1a04", + "id": "f902c476", "metadata": { "collapsed": false, "editable": true @@ -3507,7 +3509,7 @@ }, { "cell_type": "markdown", - "id": "23e4f61b", + "id": "fa673f3a", "metadata": { "editable": true }, @@ -3535,7 +3537,7 @@ }, { "cell_type": "markdown", - "id": "c9bfa99f", + "id": "6c4727a9", "metadata": { "editable": true }, @@ -3558,7 +3560,7 @@ }, { "cell_type": "markdown", - "id": "7c5e58d3", + "id": "f24d5df1", "metadata": { "editable": true }, @@ -3589,7 +3591,7 @@ }, { "cell_type": "markdown", - "id": "6b470b84", + "id": "80cd048d", "metadata": { "editable": true }, @@ -3621,7 +3623,7 @@ }, { "cell_type": "markdown", - "id": "370e81e1", + "id": "6863dde1", "metadata": { "editable": true }, @@ -3659,7 +3661,7 @@ }, { "cell_type": "markdown", - "id": "0bdbe74e", + "id": "95f03af7", "metadata": { "editable": true }, @@ -3684,7 +3686,7 @@ }, { "cell_type": "markdown", - "id": "cfa436f9", + "id": "620cac32", "metadata": { "editable": true }, @@ -3696,7 +3698,7 @@ }, { "cell_type": "markdown", - "id": "1b53aede", + "id": "2dd91f71", "metadata": { "editable": true }, @@ -3719,7 +3721,7 @@ }, { "cell_type": "markdown", - "id": "3d2e808b", + "id": "bfa3871c", "metadata": { "editable": true }, @@ -3739,7 +3741,7 @@ }, { "cell_type": "markdown", - "id": "19b958c3", + "id": "9315a3fc", "metadata": { "editable": true }, @@ -3761,7 +3763,7 @@ }, { "cell_type": "markdown", - "id": "1f4fd499", + "id": "85eb5069", "metadata": { "editable": true }, @@ -3779,7 +3781,7 @@ }, { "cell_type": "markdown", - "id": "99d0d4c8", + "id": "d2d08622", "metadata": { "editable": true }, @@ -3798,7 +3800,7 @@ }, { "cell_type": "markdown", - "id": "ee14cc3a", + "id": "566bcca7", "metadata": { "editable": true }, @@ -3810,7 +3812,7 @@ }, { "cell_type": "markdown", - "id": "3f5a367a", + "id": "aedf225e", "metadata": { "editable": true }, @@ -3855,7 +3857,7 @@ }, { "cell_type": "markdown", - "id": "687050b9", + "id": "8723e9ba", "metadata": { "editable": true }, @@ -3885,7 +3887,7 @@ }, { "cell_type": "markdown", - "id": "0ccb8f74", + "id": "b641b576", "metadata": { "editable": true }, @@ -3912,7 +3914,7 @@ }, { "cell_type": "markdown", - "id": "97d510ab", + "id": "e919d2c8", "metadata": { "editable": true }, @@ -3926,7 +3928,7 @@ }, { "cell_type": "markdown", - "id": "fe825851", + "id": "12b84451", "metadata": { "editable": true }, @@ -3943,7 +3945,7 @@ }, { "cell_type": "markdown", - "id": "780538d0", + "id": "88407501", "metadata": { "editable": true }, @@ -3959,7 +3961,7 @@ }, { "cell_type": "markdown", - "id": "f373c843", + "id": "034cef5e", "metadata": { "editable": true }, @@ -3971,7 +3973,7 @@ }, { "cell_type": "markdown", - "id": "72db62ef", + "id": "86693b23", "metadata": { "editable": true }, @@ -3989,7 +3991,7 @@ }, { "cell_type": "markdown", - "id": "9f423f32", + "id": "5c8e1418", "metadata": { "editable": true }, @@ -4010,7 +4012,7 @@ }, { "cell_type": "markdown", - "id": "54daf83b", + "id": "4ca42ad5", "metadata": { "editable": true }, @@ -4027,7 +4029,7 @@ }, { "cell_type": "markdown", - "id": "30a7ebea", + "id": "a0808029", "metadata": { "editable": true }, @@ -4039,7 +4041,7 @@ }, { "cell_type": "markdown", - "id": "a3c57c9f", + "id": "8e242ee9", "metadata": { "editable": true }, @@ -4050,7 +4052,7 @@ }, { "cell_type": "markdown", - "id": "f5f5d8ac", + "id": "6cb613c0", "metadata": { "editable": true }, @@ -4067,7 +4069,7 @@ }, { "cell_type": "markdown", - "id": "1ce283c1", + "id": "ff0ace15", "metadata": { "editable": true }, @@ -4078,7 +4080,7 @@ }, { "cell_type": "markdown", - "id": "cd9ff0af", + "id": "d77df59b", "metadata": { "editable": true }, @@ -4094,7 +4096,7 @@ }, { "cell_type": "markdown", - "id": "28c63a03", + "id": "b34fce14", "metadata": { "editable": true }, @@ -4106,7 +4108,7 @@ }, { "cell_type": "markdown", - "id": "4269d121", + "id": "290ed299", "metadata": { "editable": true }, @@ -4123,7 +4125,7 @@ }, { "cell_type": "markdown", - "id": "6b0e72c3", + "id": "d386881a", "metadata": { "editable": true }, @@ -4135,7 +4137,7 @@ }, { "cell_type": "markdown", - "id": "2295a2d8", + "id": "cf4aca15", "metadata": { "editable": true }, @@ -4153,7 +4155,7 @@ }, { "cell_type": "markdown", - "id": "e5e41afe", + "id": "ee5531c2", "metadata": { "editable": true }, @@ -4163,7 +4165,7 @@ }, { "cell_type": "markdown", - "id": "2150df23", + "id": "64a52bc0", "metadata": { "editable": true }, @@ -4175,7 +4177,7 @@ }, { "cell_type": "markdown", - "id": "0903b255", + "id": "e0154c0f", "metadata": { "editable": true }, @@ -4192,7 +4194,7 @@ }, { "cell_type": "markdown", - "id": "fe2da1fe", + "id": "334e7f10", "metadata": { "editable": true }, @@ -4204,7 +4206,7 @@ }, { "cell_type": "markdown", - "id": "75fea33a", + "id": "66c1d55c", "metadata": { "editable": true }, @@ -4215,7 +4217,7 @@ }, { "cell_type": "markdown", - "id": "297320fa", + "id": "f03c7338", "metadata": { "editable": true }, @@ -4227,7 +4229,7 @@ }, { "cell_type": "markdown", - "id": "7905db70", + "id": "ca114c87", "metadata": { "editable": true }, @@ -4237,7 +4239,7 @@ }, { "cell_type": "markdown", - "id": "7dbc7af5", + "id": "801219a0", "metadata": { "editable": true }, @@ -4257,7 +4259,7 @@ }, { "cell_type": "markdown", - "id": "52a2f0e5", + "id": "569486c4", "metadata": { "editable": true }, @@ -4274,7 +4276,7 @@ }, { "cell_type": "markdown", - "id": "fc2672cb", + "id": "575831d8", "metadata": { "editable": true }, @@ -4293,7 +4295,7 @@ }, { "cell_type": "markdown", - "id": "273c2869", + "id": "f8682123", "metadata": { "editable": true }, @@ -4305,7 +4307,7 @@ }, { "cell_type": "markdown", - "id": "8fae3b2f", + "id": "e62b1005", "metadata": { "editable": true }, @@ -4315,7 +4317,7 @@ }, { "cell_type": "markdown", - "id": "dc98cc83", + "id": "4e33439d", "metadata": { "editable": true }, @@ -4332,7 +4334,7 @@ }, { "cell_type": "markdown", - "id": "dbd04093", + "id": "35159fc5", "metadata": { "editable": true }, @@ -4342,7 +4344,7 @@ }, { "cell_type": "markdown", - "id": "4fed45e1", + "id": "03f74ead", "metadata": { "editable": true }, @@ -4358,7 +4360,7 @@ }, { "cell_type": "markdown", - "id": "5708fcb2", + "id": "3599c2de", "metadata": { "editable": true }, @@ -4370,7 +4372,7 @@ }, { "cell_type": "markdown", - "id": "7997d84c", + "id": "5c50baf7", "metadata": { "editable": true }, @@ -4382,7 +4384,7 @@ }, { "cell_type": "markdown", - "id": "78213356", + "id": "636cb74c", "metadata": { "editable": true }, @@ -4394,7 +4396,7 @@ }, { "cell_type": "markdown", - "id": "27349563", + "id": "b4b301c2", "metadata": { "editable": true }, @@ -4404,7 +4406,7 @@ }, { "cell_type": "markdown", - "id": "2b61bb07", + "id": "5d873576", "metadata": { "editable": true }, @@ -4416,7 +4418,7 @@ }, { "cell_type": "markdown", - "id": "5fe5b8e2", + "id": "b744fd57", "metadata": { "editable": true }, @@ -4433,7 +4435,7 @@ }, { "cell_type": "markdown", - "id": "2de1c60a", + "id": "63290336", "metadata": { "editable": true }, @@ -4443,7 +4445,7 @@ }, { "cell_type": "markdown", - "id": "b65d6687", + "id": "f8790d32", "metadata": { "editable": true }, @@ -4455,7 +4457,7 @@ }, { "cell_type": "markdown", - "id": "ca8a5a81", + "id": "10ab609a", "metadata": { "editable": true }, @@ -4469,7 +4471,7 @@ }, { "cell_type": "markdown", - "id": "b8614294", + "id": "06e2efca", "metadata": { "editable": true }, @@ -4485,7 +4487,7 @@ }, { "cell_type": "markdown", - "id": "76363167", + "id": "bc8a8e04", "metadata": { "editable": true }, @@ -4497,7 +4499,7 @@ }, { "cell_type": "markdown", - "id": "802de3e1", + "id": "6564e278", "metadata": { "editable": true }, @@ -4519,7 +4521,7 @@ }, { "cell_type": "markdown", - "id": "de92874a", + "id": "f62120f5", "metadata": { "editable": true }, @@ -4531,7 +4533,7 @@ }, { "cell_type": "markdown", - "id": "b5209d15", + "id": "4f875005", "metadata": { "editable": true }, @@ -4554,7 +4556,7 @@ }, { "cell_type": "markdown", - "id": "ad9f3dfb", + "id": "8748bf88", "metadata": { "editable": true }, @@ -4570,7 +4572,7 @@ }, { "cell_type": "markdown", - "id": "ecf5c2d9", + "id": "ce8b1154", "metadata": { "editable": true }, @@ -4582,7 +4584,7 @@ }, { "cell_type": "markdown", - "id": "525fe8e7", + "id": "f6ae91fa", "metadata": { "editable": true }, @@ -4606,7 +4608,7 @@ }, { "cell_type": "markdown", - "id": "6938f171", + "id": "f33f3a2e", "metadata": { "editable": true }, @@ -4618,7 +4620,7 @@ }, { "cell_type": "markdown", - "id": "592ce735", + "id": "d90fb703", "metadata": { "editable": true }, @@ -4639,7 +4641,7 @@ }, { "cell_type": "markdown", - "id": "4e757d4b", + "id": "0e4df2e9", "metadata": { "editable": true }, @@ -4651,7 +4653,7 @@ }, { "cell_type": "markdown", - "id": "b949460f", + "id": "85cc6666", "metadata": { "editable": true }, @@ -4670,7 +4672,7 @@ }, { "cell_type": "markdown", - "id": "7dbdf166", + "id": "c4876b89", "metadata": { "editable": true }, @@ -4680,7 +4682,7 @@ }, { "cell_type": "markdown", - "id": "64247852", + "id": "7e2cf00e", "metadata": { "editable": true }, @@ -4694,7 +4696,7 @@ }, { "cell_type": "markdown", - "id": "5b12d0ef", + "id": "a49b485d", "metadata": { "editable": true }, @@ -4706,7 +4708,7 @@ }, { "cell_type": "markdown", - "id": "22b5cd55", + "id": "7c5a4275", "metadata": { "editable": true }, @@ -4718,7 +4720,7 @@ }, { "cell_type": "markdown", - "id": "1adf129e", + "id": "ee92110b", "metadata": { "editable": true }, @@ -4735,7 +4737,7 @@ }, { "cell_type": "markdown", - "id": "380767da", + "id": "c76e461e", "metadata": { "editable": true }, @@ -4747,7 +4749,7 @@ }, { "cell_type": "markdown", - "id": "c8f1251c", + "id": "df9bc42a", "metadata": { "editable": true }, @@ -4769,7 +4771,7 @@ }, { "cell_type": "markdown", - "id": "3d864d42", + "id": "418dcc35", "metadata": { "editable": true }, @@ -4784,7 +4786,7 @@ }, { "cell_type": "markdown", - "id": "53e11ad4", + "id": "145ede1b", "metadata": { "editable": true }, @@ -4795,7 +4797,7 @@ { "cell_type": "code", "execution_count": 43, - "id": "87126ea3", + "id": "98b44c55", "metadata": { "collapsed": false, "editable": true @@ -4950,7 +4952,7 @@ }, { "cell_type": "markdown", - "id": "85cdbffb", + "id": "47df9d6f", "metadata": { "editable": true }, @@ -4965,7 +4967,7 @@ { "cell_type": "code", "execution_count": 44, - "id": "ea147bd8", + "id": "1415bdf5", "metadata": { "collapsed": false, "editable": true @@ -5134,7 +5136,7 @@ }, { "cell_type": "markdown", - "id": "a9c208db", + "id": "b480dee1", "metadata": { "editable": true }, @@ -5147,7 +5149,7 @@ }, { "cell_type": "markdown", - "id": "5c9d7463", + "id": "3736290c", "metadata": { "editable": true }, @@ -5164,7 +5166,7 @@ }, { "cell_type": "markdown", - "id": "959a791d", + "id": "9fe04f20", "metadata": { "editable": true }, @@ -5180,7 +5182,7 @@ }, { "cell_type": "markdown", - "id": "4d9192ef", + "id": "8090a04f", "metadata": { "editable": true }, @@ -5193,7 +5195,7 @@ }, { "cell_type": "markdown", - "id": "cd3254e9", + 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Linear Regression","14. Building a Feed Forward Neural Network","15. Solving Differential Equations with Deep Learning","16. Convolutional Neural Networks","17. Recurrent neural networks: Overarching view","4. Ridge and Lasso Regression","5. Resampling Methods","6. Logistic Regression","8. Support Vector Machines, overarching aims","9. Decision trees, overarching aims","10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","11. Basic ideas of the Principal Component Analysis (PCA)","13. Neural networks","7. Optimization, the central part of any Machine Learning algortithm","12. Clustering and Unsupervised Learning","Exercises week 34","Exercises week 35","Exercises week 36","Exercises week 37","Exercises week 38","Exercises week 39","Exercises week 41","Exercises week 42","Exercises weeks 43 and 44","Applied Data Analysis and Machine Learning","2. Linear Algebra, Handling of Arrays and more Python Features","Project 1 on Machine Learning, deadline October 9 (midnight), 2023","Project 2 on Machine Learning, deadline November 13 (Midnight)","Teaching schedule with links to material","1. Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks","Week 34: Introduction to the course, Logistics and Practicalities","Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression","Week 36: Statistical interpretation of Linear Regression and Resampling techniques","Week 37: Statistical interpretations and Resampling Methods","Week 38: Logistic Regression and Optimization","Week 39: Optimization and Gradient Methods","Week 40: Gradient descent methods (continued) and start Neural networks","Week 41 Neural networks and constructing a neural network code","Week 42 Constructing a Neural Network code with introduction to Tensor flow","Week 43: Deep Learning: Constructing a Neural Network code and solving differential 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Linear Regression","14. Building a Feed Forward Neural Network","15. Solving Differential Equations with Deep Learning","16. Convolutional Neural Networks","17. Recurrent neural networks: Overarching view","4. Ridge and Lasso Regression","5. Resampling Methods","6. Logistic Regression","8. Support Vector Machines, overarching aims","9. Decision trees, overarching aims","10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","11. Basic ideas of the Principal Component Analysis (PCA)","13. Neural networks","7. Optimization, the central part of any Machine Learning algortithm","12. Clustering and Unsupervised Learning","Exercises week 34","Exercises week 35","Exercises week 36","Exercises week 37","Exercises week 38","Exercises week 39","Exercises week 41","Exercises week 42","Exercises weeks 43 and 44","Applied Data Analysis and Machine Learning","2. Linear Algebra, Handling of Arrays and more Python Features","Project 1 on Machine Learning, deadline October 9 (midnight), 2023","Project 2 on Machine Learning, deadline November 13 (Midnight)","Teaching schedule with links to material","1. Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks","Week 34: Introduction to the course, Logistics and Practicalities","Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression","Week 36: Statistical interpretation of Linear Regression and Resampling techniques","Week 37: Statistical interpretations and Resampling Methods","Week 38: Logistic Regression and Optimization","Week 39: Optimization and Gradient Methods","Week 40: Gradient descent methods (continued) and start Neural networks","Week 41 Neural networks and constructing a neural network code","Week 42 Constructing a Neural Network code with introduction to Tensor flow","Week 43: Deep Learning: Constructing a Neural Network code and solving differential 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\ No newline at end of file diff --git a/doc/LectureNotes/_build/html/week41.html b/doc/LectureNotes/_build/html/week41.html index ccc684f57..821431d81 100644 --- a/doc/LectureNotes/_build/html/week41.html +++ b/doc/LectureNotes/_build/html/week41.html @@ -1149,6 +1149,8 @@ doconce format html week41.do.txt --no_mako -->

  • Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model.

  • Building our own Feed-forward Neural Network

    • +

  • Video of lecture notes

    • +

  • Whiteboard notes

  • Readings and Videos:

    • These lecture notes

      • @@ -2805,7 +2807,7 @@ the Hadamard product, meaning element-wise multiplication.

      Old accuracy on training data: 0.1440501043841336
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -3141,7 +3143,7 @@ Lambda =  10.0
 Accuracy score on test set:  0.19166666666666668
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -3150,7 +3152,7 @@ Lambda =  1e-05
 Accuracy score on test set:  0.10555555555555556
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -3159,7 +3161,7 @@ Lambda =  0.0001
 Accuracy score on test set:  0.08611111111111111
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -3168,7 +3170,7 @@ Lambda =  0.001
 Accuracy score on test set:  0.10555555555555556
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -3177,7 +3179,7 @@ Lambda =  0.01
 Accuracy score on test set:  0.08888888888888889
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -3186,7 +3188,7 @@ Lambda =  0.1
 Accuracy score on test set:  0.08611111111111111
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -3195,7 +3197,7 @@ Lambda =  1.0
 Accuracy score on test set:  0.08888888888888889
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -3204,11 +3206,11 @@ Lambda =  10.0
 Accuracy score on test set:  0.09166666666666666
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -3217,11 +3219,11 @@ Lambda =  1e-05
 Accuracy score on test set:  0.07777777777777778
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -3230,11 +3232,11 @@ Lambda =  0.0001
 Accuracy score on test set:  0.07777777777777778
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -3243,11 +3245,11 @@ Lambda =  0.001
 Accuracy score on test set:  0.07777777777777778
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -3256,11 +3258,11 @@ Lambda =  0.01
 Accuracy score on test set:  0.07777777777777778
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -3269,7 +3271,7 @@ Lambda =  0.1
 Accuracy score on test set:  0.07777777777777778
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -3278,11 +3280,11 @@ Lambda =  1.0
 Accuracy score on test set:  0.10555555555555556
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -3291,11 +3293,11 @@ Lambda =  10.0
 Accuracy score on test set:  0.07777777777777778
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -3304,11 +3306,11 @@ Lambda =  1e-05
 Accuracy score on test set:  0.07777777777777778
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -3317,11 +3319,11 @@ Lambda =  0.0001
 Accuracy score on test set:  0.07777777777777778
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -3330,11 +3332,11 @@ Lambda =  0.001
 Accuracy score on test set:  0.07777777777777778
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -3343,11 +3345,11 @@ Lambda =  0.01
 Accuracy score on test set:  0.07777777777777778
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -3356,11 +3358,11 @@ Lambda =  0.1
 Accuracy score on test set:  0.07777777777777778
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -3369,11 +3371,11 @@ Lambda =  1.0
 Accuracy score on test set:  0.07777777777777778
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -3426,15 +3428,15 @@ Accuracy score on test set:  0.07777777777777778
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -3698,9 +3700,8 @@ Accuracy score on test set:  0.9888888888888889
 
      Learning rate  =  0.01
 Lambda =  1.0
 Accuracy score on test set:  0.9722222222222222
-
      
-
      
-
      Learning rate  =  0.01
+
+Learning rate  =  0.01
 Lambda =  10.0
 Accuracy score on test set:  0.9527777777777777
 
      
@@ -3752,13 +3753,13 @@ Accuracy score on test set:  0.10555555555555556
 Learning rate  =  1.0
 Lambda =  0.01
 Accuracy score on test set:  0.17777777777777778
-
-Learning rate  =  1.0
-Lambda =  0.1
-Accuracy score on test set:  0.08333333333333333
 
      Learning rate  =  1.0
+Lambda =  0.1
+Accuracy score on test set:  0.08333333333333333
+
+Learning rate  =  1.0
 Lambda =  1.0
 Accuracy score on test set:  0.08888888888888889
      @@ -4221,9 +4222,8 @@ Accuracy score on data set: 0.5 Learning rate = 1.0 Lambda = 10.0 Accuracy score on data set: 0.5 -
      -
      -
      Learning rate  =  10.0
+
+Learning rate  =  10.0
 Lambda =  1e-05
 Accuracy score on data set:  0.5
 
@@ -4274,7 +4274,7 @@ Accuracy score on data set:  0.5
   warnings.warn(
      -_images/week41_211_3.png +_images/week41_211_2.png diff --git a/doc/LectureNotes/_build/html/week42.html b/doc/LectureNotes/_build/html/week42.html index 5ce3c33c4..a35a496a6 100644 --- a/doc/LectureNotes/_build/html/week42.html +++ b/doc/LectureNotes/_build/html/week42.html @@ -990,6 +990,8 @@ doconce format html week42.do.txt --no_mako -->

    • Readings and Videos:

      • These lecture notes

        • +

      • Video of lecture

        • +

      • Whiteboard notes

      • Aurelien Geron’s chapters 10-11

      • For a more in depth discussion on neural networks we recommend Goodfellow et al chapters 6 and 7.

      • Neural Networks demystified

        • @@ -1685,7 +1687,7 @@ the Hadamard product, meaning element-wise multiplication.

        Old accuracy on training data: 0.1440501043841336
        -
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
        
 
        
@@ -2021,7 +2023,7 @@ Lambda =  10.0
 Accuracy score on test set:  0.19166666666666668
        -
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
        
 
        
@@ -2030,7 +2032,7 @@ Lambda =  1e-05
 Accuracy score on test set:  0.10555555555555556
        -
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
        
 
        
@@ -2039,7 +2041,7 @@ Lambda =  0.0001
 Accuracy score on test set:  0.08611111111111111
        -
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
        
 
        
@@ -2048,7 +2050,7 @@ Lambda =  0.001
 Accuracy score on test set:  0.10555555555555556
        -
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
        
 
        
@@ -2057,7 +2059,7 @@ Lambda =  0.01
 Accuracy score on test set:  0.08888888888888889
        -
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
        
 
        
@@ -2066,7 +2068,7 @@ Lambda =  0.1
 Accuracy score on test set:  0.08611111111111111
        -
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
        
 
        
@@ -2075,7 +2077,7 @@ Lambda =  1.0
 Accuracy score on test set:  0.08888888888888889
        -
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
        
 
        
@@ -2084,11 +2086,11 @@ Lambda =  10.0
 Accuracy score on test set:  0.09166666666666666
        -
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
        
 
        
@@ -2097,11 +2099,11 @@ Lambda =  1e-05
 Accuracy score on test set:  0.07777777777777778
        -
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
        
 
        
@@ -2110,11 +2112,11 @@ Lambda =  0.0001
 Accuracy score on test set:  0.07777777777777778
        -
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
        
 
        
@@ -2123,11 +2125,11 @@ Lambda =  0.001
 Accuracy score on test set:  0.07777777777777778
        -
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
        
 
        
@@ -2136,11 +2138,11 @@ Lambda =  0.01
 Accuracy score on test set:  0.07777777777777778
        -
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
        
 
        
@@ -2149,7 +2151,7 @@ Lambda =  0.1
 Accuracy score on test set:  0.07777777777777778
        -
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
        
 
        
@@ -2158,11 +2160,11 @@ Lambda =  1.0
 Accuracy score on test set:  0.10555555555555556
        -
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
        
 
        
@@ -2171,11 +2173,11 @@ Lambda =  10.0
 Accuracy score on test set:  0.07777777777777778
        -
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
        
 
        
@@ -2184,11 +2186,11 @@ Lambda =  1e-05
 Accuracy score on test set:  0.07777777777777778
        -
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
        
 
        
@@ -2197,11 +2199,11 @@ Lambda =  0.0001
 Accuracy score on test set:  0.07777777777777778
        -
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
        
 
        
@@ -2210,11 +2212,11 @@ Lambda =  0.001
 Accuracy score on test set:  0.07777777777777778
        -
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
        
 
        
@@ -2223,11 +2225,11 @@ Lambda =  0.01
 Accuracy score on test set:  0.07777777777777778
        -
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
        
 
        
@@ -2236,11 +2238,11 @@ Lambda =  0.1
 Accuracy score on test set:  0.07777777777777778
        -
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
        
 
        
@@ -2249,11 +2251,11 @@ Lambda =  1.0
 Accuracy score on test set:  0.07777777777777778
        -
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
        
 
        
@@ -2306,15 +2308,15 @@ Accuracy score on test set:  0.07777777777777778
        -
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
        /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
        
 
        
@@ -2618,40 +2620,39 @@ Accuracy score on test set:  0.8666666666666667
 Learning rate  =  1.0
 Lambda =  1e-05
 Accuracy score on test set:  0.08611111111111111
+
+Learning rate  =  1.0
+Lambda =  0.0001
+Accuracy score on test set:  0.10555555555555556
 
        Learning rate  =  1.0
-Lambda =  0.0001
+Lambda =  0.001
 Accuracy score on test set:  0.10555555555555556
 
 Learning rate  =  1.0
-Lambda =  0.001
-Accuracy score on test set:  0.10555555555555556
-
        -
        -
        Learning rate  =  1.0
 Lambda =  0.01
 Accuracy score on test set:  0.17777777777777778
 
 Learning rate  =  1.0
 Lambda =  0.1
 Accuracy score on test set:  0.08333333333333333
-
-Learning rate  =  1.0
-Lambda =  1.0
-Accuracy score on test set:  0.08888888888888889
 
        Learning rate  =  1.0
+Lambda =  1.0
+Accuracy score on test set:  0.08888888888888889
+
+Learning rate  =  1.0
 Lambda =  10.0
 Accuracy score on test set:  0.09444444444444444
-
-Learning rate  =  10.0
-Lambda =  1e-05
-Accuracy score on test set:  0.17222222222222222
 
        Learning rate  =  10.0
+Lambda =  1e-05
+Accuracy score on test set:  0.17222222222222222
+
+Learning rate  =  10.0
 Lambda =  0.0001
 Accuracy score on test set:  0.11666666666666667
 
@@ -3096,8 +3097,34 @@ Accuracy score on data set:  0.5
 Learning rate  =  1.0
 Lambda =  1.0
 Accuracy score on data set:  0.5
-
-Learning rate  =  1.0
+
        +
        +
        Learning rate  = 
+
        +
        +
        /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
        +
        +
         1.0
 Lambda =  10.0
 Accuracy score on data set:  0.5
 
@@ -3130,29 +3157,7 @@ Lambda =  10.0
 Accuracy score on data set:  0.5
        -
        /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
        -
        -_images/week42_88_2.png +_images/week42_88_4.png
        diff --git a/doc/LectureNotes/_build/html/week43.html b/doc/LectureNotes/_build/html/week43.html index 0641a2c9b..5a4b18c24 100644 --- a/doc/LectureNotes/_build/html/week43.html +++ b/doc/LectureNotes/_build/html/week43.html @@ -1534,6 +1534,7 @@ doconce format html week43.do.txt --no_mako -->

        • Building our own Feed-forward Neural Network and discussion of project 2, continuation from last week

        • Solving differential equations with Neural Networks and intro to Tensorflow with examples.

          • +

        • Video of lecture

        • Readings and Videos:

          • These lecture notes

            • @@ -1861,8 +1862,9 @@ Accuracy score on data set: 0.5 Learning rate = 1e-05 Lambda = 1.0 Accuracy score on data set: 0.5 - -Learning rate = 1e-05 + + +
            Learning rate  =  1e-05
 Lambda =  10.0
 Accuracy score on data set:  0.5
 
@@ -2057,7 +2059,7 @@ Accuracy score on data set:  0.5
   warnings.warn(
            -_images/week43_30_2.png +_images/week43_30_3.png @@ -6364,9 +6366,6 @@ case.

            Adam: Eta=0.001, Lambda=0
            -
            
-
            -
              [----------------------------------------] 0.000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
            @@ -10645,17 +10644,1768 @@ case.

              [===============>------------------------] 41.30% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888
            -
            Exception ignored in: <function Socket.__del__ at 0x1022cc0d0>
-Traceback (most recent call last):
-  File "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/zmq/sugar/socket.py", line 112, in __del__
-    warn(
-ResourceWarning: unclosed socket <zmq.Socket(zmq.PUSH) at 0x1635f5340>
+
              [===============>------------------------] 41.40% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881 
 
            
 
            
-
                                                                                                                                           
+
              [===============>------------------------] 41.50% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888 
 
            
 
            
-
              [=======================================>] 100.0% 
+
              [===============>------------------------] 41.60% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881 
+
            
+
            
+
              [===============>------------------------] 41.70% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888 
+
            
+
            
+
              [===============>------------------------] 41.80% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881 
+
            
+
            
+
              [===============>------------------------] 41.90% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [===============>------------------------] 42.00% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881 
+
            
+
            
+
              [===============>------------------------] 42.10% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [===============>------------------------] 42.20% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888 
+
            
+
            
+
              [===============>------------------------] 42.30% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888 
+
            
+
            
+
              [===============>------------------------] 42.40% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888 
+
            
+
            
+
              [================>-----------------------] 42.50% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888 
+
            
+
            
+
              [================>-----------------------] 42.60% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888 
+
            
+
            
+
              [================>-----------------------] 42.70% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888 
+
            
+
            
+
              [================>-----------------------] 42.80% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888 
+
            
+
            
+
              [================>-----------------------] 42.90% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888 
+
            
+
            
+
              [================>-----------------------] 43.00% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881 
+
            
+
            
+
              [================>-----------------------] 43.10% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888 
+
            
+
            
+
              [================>-----------------------] 43.20% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881 
+
            
+
            
+
              [================>-----------------------] 43.30% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888 
+
            
+
            
+
              [================>-----------------------] 43.40% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881 
+
            
+
            
+
              [================>-----------------------] 43.50% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888 
+
            
+
            
+
              [================>-----------------------] 43.60% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881 
+
            
+
            
+
              [================>-----------------------] 43.70% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [================>-----------------------] 43.80% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881 
+
            
+
            
+
              [================>-----------------------] 43.90% | train_error: 0.243 | train_acc: 0.988 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [================>-----------------------] 44.00% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881 
+
            
+
            
+
              [================>-----------------------] 44.10% | train_error: 0.243 | train_acc: 0.988 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [================>-----------------------] 44.20% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881 
+
            
+
            
+
              [================>-----------------------] 44.30% | train_error: 0.243 | train_acc: 0.988 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [================>-----------------------] 44.40% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888 
+
            
+
            
+
              [================>-----------------------] 44.50% | train_error: 0.243 | train_acc: 0.988 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [================>-----------------------] 44.60% | train_error: 0.243 | train_acc: 0.988 | val_error: 2.32 | val_acc: 0.888 
+
            
+
            
+
              [================>-----------------------] 44.70% | train_error: 0.243 | train_acc: 0.988 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [================>-----------------------] 44.80% | train_error: 0.243 | train_acc: 0.988 | val_error: 2.32 | val_acc: 0.888 
+
            
+
            
+
              [================>-----------------------] 44.90% | train_error: 0.243 | train_acc: 0.988 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [=================>----------------------] 45.00% | train_error: 0.243 | train_acc: 0.988 | val_error: 2.32 | val_acc: 0.888 
+
            
+
            
+
              [=================>----------------------] 45.10% | train_error: 0.243 | train_acc: 0.988 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [=================>----------------------] 45.20% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [=================>----------------------] 45.30% | train_error: 0.243 | train_acc: 0.988 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [=================>----------------------] 45.40% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [=================>----------------------] 45.50% | train_error: 0.243 | train_acc: 0.988 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [=================>----------------------] 45.60% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [=================>----------------------] 45.70% | train_error: 0.243 | train_acc: 0.988 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [=================>----------------------] 45.80% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [=================>----------------------] 45.90% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [=================>----------------------] 46.00% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [=================>----------------------] 46.10% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [=================>----------------------] 46.20% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [=================>----------------------] 46.30% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [=================>----------------------] 46.40% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [=================>----------------------] 46.50% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [=================>----------------------] 46.60% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [=================>----------------------] 46.70% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [=================>----------------------] 46.80% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [=================>----------------------] 46.90% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [=================>----------------------] 47.00% | train_error: 0.146 | train_acc: 0.993 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [=================>----------------------] 47.10% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [=================>----------------------] 47.20% | train_error: 0.146 | train_acc: 0.993 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [=================>----------------------] 47.30% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [=================>----------------------] 47.40% | train_error: 0.146 | train_acc: 0.993 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [==================>---------------------] 47.50% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [==================>---------------------] 47.60% | train_error: 0.146 | train_acc: 0.993 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [==================>---------------------] 47.70% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [==================>---------------------] 47.80% | train_error: 0.146 | train_acc: 0.993 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [==================>---------------------] 47.90% | train_error: 0.146 | train_acc: 0.993 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [==================>---------------------] 48.00% | train_error: 0.146 | train_acc: 0.993 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [==================>---------------------] 48.10% | train_error: 0.146 | train_acc: 0.993 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [==================>---------------------] 48.20% | train_error: 0.146 | train_acc: 0.993 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [==================>---------------------] 48.30% | train_error: 0.146 | train_acc: 0.993 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [==================>---------------------] 48.40% | train_error: 0.146 | train_acc: 0.993 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [==================>---------------------] 48.50% | train_error: 0.146 | train_acc: 0.993 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [==================>---------------------] 48.60% | train_error: 0.146 | train_acc: 0.993 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [==================>---------------------] 48.70% | train_error: 0.146 | train_acc: 0.993 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [==================>---------------------] 48.80% | train_error: 0.146 | train_acc: 0.993 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [==================>---------------------] 48.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [==================>---------------------] 49.00% | train_error: 0.146 | train_acc: 0.993 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [==================>---------------------] 49.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [==================>---------------------] 49.20% | train_error: 0.146 | train_acc: 0.993 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [==================>---------------------] 49.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [==================>---------------------] 49.40% | train_error: 0.146 | train_acc: 0.993 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [==================>---------------------] 49.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [==================>---------------------] 49.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [==================>---------------------] 49.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [==================>---------------------] 49.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [==================>---------------------] 49.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [===================>--------------------] 50.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [===================>--------------------] 50.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [===================>--------------------] 50.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [===================>--------------------] 50.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [===================>--------------------] 50.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [===================>--------------------] 50.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [===================>--------------------] 50.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [===================>--------------------] 50.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [===================>--------------------] 50.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [===================>--------------------] 50.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [===================>--------------------] 51.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [===================>--------------------] 51.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [===================>--------------------] 51.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [===================>--------------------] 51.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [===================>--------------------] 51.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [===================>--------------------] 51.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [===================>--------------------] 51.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [===================>--------------------] 51.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [===================>--------------------] 51.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [===================>--------------------] 51.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [===================>--------------------] 52.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.17 | val_acc: 0.895 
+
            
+
            
+
              [===================>--------------------] 52.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [===================>--------------------] 52.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [===================>--------------------] 52.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [===================>--------------------] 52.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [====================>-------------------] 52.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [====================>-------------------] 52.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [====================>-------------------] 52.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [====================>-------------------] 52.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [====================>-------------------] 52.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [====================>-------------------] 53.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [====================>-------------------] 53.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [====================>-------------------] 53.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [====================>-------------------] 53.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [====================>-------------------] 53.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [====================>-------------------] 53.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [====================>-------------------] 53.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [====================>-------------------] 53.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [====================>-------------------] 53.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [====================>-------------------] 53.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [====================>-------------------] 54.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [====================>-------------------] 54.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [====================>-------------------] 54.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [====================>-------------------] 54.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [====================>-------------------] 54.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [====================>-------------------] 54.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [====================>-------------------] 54.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [====================>-------------------] 54.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [====================>-------------------] 54.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [====================>-------------------] 54.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=====================>------------------] 55.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [=====================>------------------] 55.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=====================>------------------] 55.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [=====================>------------------] 55.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=====================>------------------] 55.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [=====================>------------------] 55.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=====================>------------------] 55.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [=====================>------------------] 55.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=====================>------------------] 55.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [=====================>------------------] 55.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=====================>------------------] 56.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [=====================>------------------] 56.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=====================>------------------] 56.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [=====================>------------------] 56.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=====================>------------------] 56.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [=====================>------------------] 56.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=====================>------------------] 56.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [=====================>------------------] 56.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=====================>------------------] 56.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [=====================>------------------] 56.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=====================>------------------] 57.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [=====================>------------------] 57.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=====================>------------------] 57.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902 
+
            
+
            
+
              [=====================>------------------] 57.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=====================>------------------] 57.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [======================>-----------------] 57.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [======================>-----------------] 57.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [======================>-----------------] 57.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [======================>-----------------] 57.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [======================>-----------------] 57.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [======================>-----------------] 58.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [======================>-----------------] 58.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [======================>-----------------] 58.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [======================>-----------------] 58.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [======================>-----------------] 58.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [======================>-----------------] 58.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [======================>-----------------] 58.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [======================>-----------------] 58.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [======================>-----------------] 58.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [======================>-----------------] 58.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [======================>-----------------] 59.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [======================>-----------------] 59.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [======================>-----------------] 59.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [======================>-----------------] 59.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [======================>-----------------] 59.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [======================>-----------------] 59.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [======================>-----------------] 59.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [======================>-----------------] 59.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [======================>-----------------] 59.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [======================>-----------------] 59.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=======================>----------------] 60.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=======================>----------------] 60.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=======================>----------------] 60.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=======================>----------------] 60.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=======================>----------------] 60.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=======================>----------------] 60.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=======================>----------------] 60.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=======================>----------------] 60.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=======================>----------------] 60.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=======================>----------------] 60.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=======================>----------------] 61.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=======================>----------------] 61.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=======================>----------------] 61.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=======================>----------------] 61.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=======================>----------------] 61.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=======================>----------------] 61.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=======================>----------------] 61.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=======================>----------------] 61.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=======================>----------------] 61.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=======================>----------------] 61.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=======================>----------------] 62.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=======================>----------------] 62.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=======================>----------------] 62.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=======================>----------------] 62.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=======================>----------------] 62.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [========================>---------------] 62.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [========================>---------------] 62.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [========================>---------------] 62.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [========================>---------------] 62.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [========================>---------------] 62.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [========================>---------------] 63.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [========================>---------------] 63.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [========================>---------------] 63.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [========================>---------------] 63.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [========================>---------------] 63.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [========================>---------------] 63.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [========================>---------------] 63.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [========================>---------------] 63.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [========================>---------------] 63.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [========================>---------------] 63.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [========================>---------------] 64.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [========================>---------------] 64.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [========================>---------------] 64.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [========================>---------------] 64.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [========================>---------------] 64.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [========================>---------------] 64.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [========================>---------------] 64.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [========================>---------------] 64.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [========================>---------------] 64.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [========================>---------------] 64.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=========================>--------------] 65.00% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=========================>--------------] 65.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=========================>--------------] 65.20% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=========================>--------------] 65.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=========================>--------------] 65.40% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=========================>--------------] 65.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=========================>--------------] 65.60% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=========================>--------------] 65.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=========================>--------------] 65.80% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=========================>--------------] 65.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=========================>--------------] 66.00% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=========================>--------------] 66.10% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=========================>--------------] 66.20% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=========================>--------------] 66.30% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=========================>--------------] 66.40% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=========================>--------------] 66.50% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=========================>--------------] 66.60% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=========================>--------------] 66.70% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=========================>--------------] 66.80% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=========================>--------------] 66.90% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=========================>--------------] 67.00% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=========================>--------------] 67.10% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=========================>--------------] 67.20% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [=========================>--------------] 67.30% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=========================>--------------] 67.40% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [==========================>-------------] 67.50% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==========================>-------------] 67.60% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [==========================>-------------] 67.70% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==========================>-------------] 67.80% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [==========================>-------------] 67.90% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==========================>-------------] 68.00% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [==========================>-------------] 68.10% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==========================>-------------] 68.20% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [==========================>-------------] 68.30% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==========================>-------------] 68.40% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [==========================>-------------] 68.50% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==========================>-------------] 68.60% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [==========================>-------------] 68.70% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==========================>-------------] 68.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [==========================>-------------] 68.90% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==========================>-------------] 69.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [==========================>-------------] 69.10% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==========================>-------------] 69.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [==========================>-------------] 69.30% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==========================>-------------] 69.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [==========================>-------------] 69.50% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==========================>-------------] 69.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [==========================>-------------] 69.70% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==========================>-------------] 69.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909 
+
            
+
            
+
              [==========================>-------------] 69.90% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===========================>------------] 70.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===========================>------------] 70.10% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===========================>------------] 70.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===========================>------------] 70.30% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===========================>------------] 70.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===========================>------------] 70.50% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===========================>------------] 70.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===========================>------------] 70.70% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===========================>------------] 70.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===========================>------------] 70.90% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===========================>------------] 71.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===========================>------------] 71.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===========================>------------] 71.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===========================>------------] 71.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===========================>------------] 71.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===========================>------------] 71.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===========================>------------] 71.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===========================>------------] 71.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===========================>------------] 71.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===========================>------------] 71.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===========================>------------] 72.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===========================>------------] 72.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===========================>------------] 72.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===========================>------------] 72.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===========================>------------] 72.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [============================>-----------] 72.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [============================>-----------] 72.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [============================>-----------] 72.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [============================>-----------] 72.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [============================>-----------] 72.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [============================>-----------] 73.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [============================>-----------] 73.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [============================>-----------] 73.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [============================>-----------] 73.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [============================>-----------] 73.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [============================>-----------] 73.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [============================>-----------] 73.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [============================>-----------] 73.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [============================>-----------] 73.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [============================>-----------] 73.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [============================>-----------] 74.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [============================>-----------] 74.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [============================>-----------] 74.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [============================>-----------] 74.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [============================>-----------] 74.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [============================>-----------] 74.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [============================>-----------] 74.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [============================>-----------] 74.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [============================>-----------] 74.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [============================>-----------] 74.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=============================>----------] 75.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=============================>----------] 75.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=============================>----------] 75.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=============================>----------] 75.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=============================>----------] 75.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=============================>----------] 75.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=============================>----------] 75.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=============================>----------] 75.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=============================>----------] 75.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=============================>----------] 75.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=============================>----------] 76.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=============================>----------] 76.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=============================>----------] 76.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=============================>----------] 76.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=============================>----------] 76.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=============================>----------] 76.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=============================>----------] 76.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=============================>----------] 76.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=============================>----------] 76.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=============================>----------] 76.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=============================>----------] 77.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=============================>----------] 77.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [=============================>----------] 77.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=============================>----------] 77.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [=============================>----------] 77.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==============================>---------] 77.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [==============================>---------] 77.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==============================>---------] 77.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [==============================>---------] 77.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==============================>---------] 77.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [==============================>---------] 78.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==============================>---------] 78.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [==============================>---------] 78.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==============================>---------] 78.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [==============================>---------] 78.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==============================>---------] 78.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [==============================>---------] 78.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==============================>---------] 78.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [==============================>---------] 78.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==============================>---------] 78.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [==============================>---------] 79.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==============================>---------] 79.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [==============================>---------] 79.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==============================>---------] 79.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [==============================>---------] 79.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==============================>---------] 79.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [==============================>---------] 79.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==============================>---------] 79.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [==============================>---------] 79.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==============================>---------] 79.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [===============================>--------] 80.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===============================>--------] 80.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [===============================>--------] 80.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===============================>--------] 80.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [===============================>--------] 80.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===============================>--------] 80.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [===============================>--------] 80.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===============================>--------] 80.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [===============================>--------] 80.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===============================>--------] 80.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [===============================>--------] 81.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===============================>--------] 81.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [===============================>--------] 81.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===============================>--------] 81.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [===============================>--------] 81.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===============================>--------] 81.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [===============================>--------] 81.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===============================>--------] 81.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [===============================>--------] 81.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===============================>--------] 81.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [===============================>--------] 82.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===============================>--------] 82.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [===============================>--------] 82.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===============================>--------] 82.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [===============================>--------] 82.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [================================>-------] 82.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [================================>-------] 82.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [================================>-------] 82.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [================================>-------] 82.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [================================>-------] 82.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [================================>-------] 83.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [================================>-------] 83.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [================================>-------] 83.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [================================>-------] 83.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [================================>-------] 83.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [================================>-------] 83.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [================================>-------] 83.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [================================>-------] 83.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [================================>-------] 83.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [================================>-------] 83.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [================================>-------] 84.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [================================>-------] 84.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [================================>-------] 84.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [================================>-------] 84.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [================================>-------] 84.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [================================>-------] 84.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [================================>-------] 84.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [================================>-------] 84.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [================================>-------] 84.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [================================>-------] 84.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [=================================>------] 85.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=================================>------] 85.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [=================================>------] 85.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=================================>------] 85.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [=================================>------] 85.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=================================>------] 85.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [=================================>------] 85.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=================================>------] 85.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [=================================>------] 85.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=================================>------] 85.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [=================================>------] 86.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=================================>------] 86.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [=================================>------] 86.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=================================>------] 86.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [=================================>------] 86.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=================================>------] 86.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [=================================>------] 86.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=================================>------] 86.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [=================================>------] 86.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=================================>------] 86.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [=================================>------] 87.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=================================>------] 87.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [=================================>------] 87.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=================================>------] 87.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [=================================>------] 87.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==================================>-----] 87.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [==================================>-----] 87.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==================================>-----] 87.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [==================================>-----] 87.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==================================>-----] 87.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [==================================>-----] 88.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==================================>-----] 88.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [==================================>-----] 88.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==================================>-----] 88.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [==================================>-----] 88.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==================================>-----] 88.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [==================================>-----] 88.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==================================>-----] 88.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [==================================>-----] 88.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==================================>-----] 88.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [==================================>-----] 89.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==================================>-----] 89.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [==================================>-----] 89.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==================================>-----] 89.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [==================================>-----] 89.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==================================>-----] 89.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [==================================>-----] 89.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==================================>-----] 89.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [==================================>-----] 89.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [==================================>-----] 89.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923 
+
            
+
            
+
              [===================================>----] 90.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===================================>----] 90.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===================================>----] 90.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===================================>----] 90.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===================================>----] 90.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===================================>----] 90.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===================================>----] 90.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===================================>----] 90.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===================================>----] 90.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===================================>----] 90.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===================================>----] 91.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===================================>----] 91.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===================================>----] 91.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===================================>----] 91.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===================================>----] 91.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===================================>----] 91.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===================================>----] 91.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===================================>----] 91.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===================================>----] 91.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===================================>----] 91.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===================================>----] 92.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===================================>----] 92.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===================================>----] 92.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===================================>----] 92.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [===================================>----] 92.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [====================================>---] 92.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [====================================>---] 92.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [====================================>---] 92.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [====================================>---] 92.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [====================================>---] 92.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [====================================>---] 93.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [====================================>---] 93.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [====================================>---] 93.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [====================================>---] 93.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [====================================>---] 93.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [====================================>---] 93.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [====================================>---] 93.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [====================================>---] 93.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [====================================>---] 93.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [====================================>---] 93.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [====================================>---] 94.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [====================================>---] 94.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [====================================>---] 94.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [====================================>---] 94.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [====================================>---] 94.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [====================================>---] 94.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [====================================>---] 94.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [====================================>---] 94.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [====================================>---] 94.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [====================================>---] 94.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=====================================>--] 95.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=====================================>--] 95.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=====================================>--] 95.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=====================================>--] 95.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=====================================>--] 95.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=====================================>--] 95.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=====================================>--] 95.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=====================================>--] 95.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=====================================>--] 95.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=====================================>--] 95.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=====================================>--] 96.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=====================================>--] 96.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=====================================>--] 96.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=====================================>--] 96.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=====================================>--] 96.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=====================================>--] 96.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=====================================>--] 96.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=====================================>--] 96.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=====================================>--] 96.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=====================================>--] 96.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=====================================>--] 97.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=====================================>--] 97.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=====================================>--] 97.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=====================================>--] 97.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [=====================================>--] 97.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [======================================>-] 97.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [======================================>-] 97.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [======================================>-] 97.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [======================================>-] 97.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [======================================>-] 97.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [======================================>-] 98.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [======================================>-] 98.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [======================================>-] 98.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [======================================>-] 98.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [======================================>-] 98.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [======================================>-] 98.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [======================================>-] 98.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [======================================>-] 98.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [======================================>-] 98.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [======================================>-] 98.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [======================================>-] 99.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [======================================>-] 99.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [======================================>-] 99.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [======================================>-] 99.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [======================================>-] 99.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [======================================>-] 99.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [======================================>-] 99.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [======================================>-] 99.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [======================================>-] 99.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
              [======================================>-] 99.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
+
            
+
            
+
                                                                                                                                            
+
            
+
            
+
              [=======================================>] 100.0% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916 
 
            
 
            
 
            
@@ -10697,6 +12447,3017 @@ digits between the range of 0 to 9.
            +
            +
            Adam: Eta=0.0001, Lambda=0
+
            +
            +
              [----------------------------------------] 0.000% | train_error: 1.83 | train_acc: 0.823 
+
            +
            +
              [----------------------------------------] 0.1000% | train_error: 1.83 | train_acc: 0.823 
+
            +
            +
              [----------------------------------------] 0.2000% | train_error: 1.83 | train_acc: 0.823 
+
            +
            +
              [----------------------------------------] 0.3000% | train_error: 1.83 | train_acc: 0.823 
+
            +
            +
              [----------------------------------------] 0.4000% | train_error: 1.83 | train_acc: 0.823 
+
            +
            +
              [----------------------------------------] 0.5000% | train_error: 1.83 | train_acc: 0.823 
+
            +
            +
              [----------------------------------------] 0.6000% | train_error: 1.83 | train_acc: 0.824 
+
            +
            +
              [----------------------------------------] 0.7000% | train_error: 1.83 | train_acc: 0.824 
+
            +
            +
              [----------------------------------------] 0.8000% | train_error: 1.83 | train_acc: 0.824 
+
            +
            +
              [----------------------------------------] 0.9000% | train_error: 1.83 | train_acc: 0.824 
+
            +
            +
              [----------------------------------------] 1.000% | train_error: 1.83 | train_acc: 0.824 
+
            +
            +
              [----------------------------------------] 1.100% | train_error: 1.83 | train_acc: 0.824 
+
            +
            +
              [----------------------------------------] 1.200% | train_error: 1.83 | train_acc: 0.824 
+
            +
            +
              [----------------------------------------] 1.300% | train_error: 1.83 | train_acc: 0.824 
+
            +
            +
              [----------------------------------------] 1.400% | train_error: 1.83 | train_acc: 0.824 
+
            +
            +
              [----------------------------------------] 1.500% | train_error: 1.82 | train_acc: 0.824 
+
            +
            +
              [----------------------------------------] 1.600% | train_error: 1.83 | train_acc: 0.824 
+
            +
            +
              [----------------------------------------] 1.700% | train_error: 1.83 | train_acc: 0.824 
+
            +
            +
              [----------------------------------------] 1.800% | train_error: 1.83 | train_acc: 0.824 
+
            +
            +
              [----------------------------------------] 1.900% | train_error: 1.82 | train_acc: 0.824 
+
            +
            +
              [----------------------------------------] 2.000% | train_error: 1.82 | train_acc: 0.824 
+
            +
            +
              [----------------------------------------] 2.100% | train_error: 1.82 | train_acc: 0.824 
+
            +
            +
              [----------------------------------------] 2.200% | train_error: 1.82 | train_acc: 0.824 
+
            +
            +
              [----------------------------------------] 2.300% | train_error: 1.82 | train_acc: 0.824 
+
            +
            +
              [----------------------------------------] 2.400% | train_error: 1.82 | train_acc: 0.824 
+
            +
            +
              [>---------------------------------------] 2.500% | train_error: 1.82 | train_acc: 0.824 
+
            +
            +
              [>---------------------------------------] 2.600% | train_error: 1.83 | train_acc: 0.824 
+
            +
            +
              [>---------------------------------------] 2.700% | train_error: 1.83 | train_acc: 0.824 
+
            +
            +
              [>---------------------------------------] 2.800% | train_error: 1.83 | train_acc: 0.824 
+
            +
            +
              [>---------------------------------------] 2.900% | train_error: 1.82 | train_acc: 0.824 
+
            +
            +
              [>---------------------------------------] 3.000% | train_error: 1.82 | train_acc: 0.824 
+
            +
            +
              [>---------------------------------------] 3.100% | train_error: 1.82 | train_acc: 0.824 
+
            +
            +
              [>---------------------------------------] 3.200% | train_error: 1.82 | train_acc: 0.824 
+
            +
            +
              [>---------------------------------------] 3.300% | train_error: 1.82 | train_acc: 0.824 
+
            +
            +
              [>---------------------------------------] 3.400% | train_error: 1.82 | train_acc: 0.824 
+
            +
            +
              [>---------------------------------------] 3.500% | train_error: 1.82 | train_acc: 0.824 
+
            +
            +
              [>---------------------------------------] 3.600% | train_error: 1.82 | train_acc: 0.824 
+
            +
            +
              [>---------------------------------------] 3.700% | train_error: 1.82 | train_acc: 0.824 
+
            +
            +
              [>---------------------------------------] 3.800% | train_error: 1.82 | train_acc: 0.824 
+
            +
            +
              [>---------------------------------------] 3.900% | train_error: 1.82 | train_acc: 0.824 
+
            +
            +
              [>---------------------------------------] 4.000% | train_error: 1.82 | train_acc: 0.824 
+
            +
            +
              [>---------------------------------------] 4.100% | train_error: 1.83 | train_acc: 0.824 
+
            +
            +
              [>---------------------------------------] 4.200% | train_error: 1.83 | train_acc: 0.824 
+
            +
            +
              [>---------------------------------------] 4.300% | train_error: 1.83 | train_acc: 0.824 
+
            +
            +
              [>---------------------------------------] 4.400% | train_error: 1.83 | train_acc: 0.823 
+
            +
            +
              [>---------------------------------------] 4.500% | train_error: 1.83 | train_acc: 0.823 
+
            +
            +
              [>---------------------------------------] 4.600% | train_error: 1.83 | train_acc: 0.823 
+
            +
            +
              [>---------------------------------------] 4.700% | train_error: 1.83 | train_acc: 0.823 
+
            +
            +
              [>---------------------------------------] 4.800% | train_error: 1.83 | train_acc: 0.823 
+
            +
            +
              [>---------------------------------------] 4.900% | train_error: 1.83 | train_acc: 0.823 
+
            +
            +
              [=>--------------------------------------] 5.000% | train_error: 1.83 | train_acc: 0.823 
+
            +
            +
              [=>--------------------------------------] 5.100% | train_error: 1.83 | train_acc: 0.823 
+
            +
            +
              [=>--------------------------------------] 5.200% | train_error: 1.83 | train_acc: 0.823 
+
            +
            +
              [=>--------------------------------------] 5.300% | train_error: 1.83 | train_acc: 0.823 
+
            +
            +
              [=>--------------------------------------] 5.400% | train_error: 1.83 | train_acc: 0.823 
+
            +
            +
              [=>--------------------------------------] 5.500% | train_error: 1.84 | train_acc: 0.823 
+
            +
            +
              [=>--------------------------------------] 5.600% | train_error: 1.84 | train_acc: 0.823 
+
            +
            +
              [=>--------------------------------------] 5.700% | train_error: 1.84 | train_acc: 0.822 
+
            +
            +
              [=>--------------------------------------] 5.800% | train_error: 1.84 | train_acc: 0.822 
+
            +
            +
              [=>--------------------------------------] 5.900% | train_error: 1.84 | train_acc: 0.822 
+
            +
            +
              [=>--------------------------------------] 6.000% | train_error: 1.84 | train_acc: 0.823 
+
            +
            +
              [=>--------------------------------------] 6.100% | train_error: 1.84 | train_acc: 0.823 
+
            +
            +
              [=>--------------------------------------] 6.200% | train_error: 1.84 | train_acc: 0.823 
+
            +
            +
              [=>--------------------------------------] 6.300% | train_error: 1.84 | train_acc: 0.823 
+
            +
            +
              [=>--------------------------------------] 6.400% | train_error: 1.84 | train_acc: 0.823 
+
            +
            +
              [=>--------------------------------------] 6.500% | train_error: 1.84 | train_acc: 0.823 
+
            +
            +
              [=>--------------------------------------] 6.600% | train_error: 1.84 | train_acc: 0.823 
+
            +
            +
              [=>--------------------------------------] 6.700% | train_error: 1.84 | train_acc: 0.823 
+
            +
            +
              [=>--------------------------------------] 6.800% | train_error: 1.84 | train_acc: 0.822 
+
            +
            +
              [=>--------------------------------------] 6.900% | train_error: 1.85 | train_acc: 0.822 
+
            +
            +
              [=>--------------------------------------] 7.000% | train_error: 1.85 | train_acc: 0.822 
+
            +
            +
              [=>--------------------------------------] 7.100% | train_error: 1.85 | train_acc: 0.821 
+
            +
            +
              [=>--------------------------------------] 7.200% | train_error: 1.85 | train_acc: 0.821 
+
            +
            +
              [=>--------------------------------------] 7.300% | train_error: 1.85 | train_acc: 0.821 
+
            +
            +
              [=>--------------------------------------] 7.400% | train_error: 1.85 | train_acc: 0.821 
+
            +
            +
              [==>-------------------------------------] 7.500% | train_error: 1.86 | train_acc: 0.821 
+
            +
            +
              [==>-------------------------------------] 7.600% | train_error: 1.86 | train_acc: 0.820 
+
            +
            +
              [==>-------------------------------------] 7.700% | train_error: 1.86 | train_acc: 0.820 
+
            +
            +
              [==>-------------------------------------] 7.800% | train_error: 1.86 | train_acc: 0.820 
+
            +
            +
              [==>-------------------------------------] 7.900% | train_error: 1.86 | train_acc: 0.820 
+
            +
            +
              [==>-------------------------------------] 8.000% | train_error: 1.86 | train_acc: 0.820 
+
            +
            +
              [==>-------------------------------------] 8.100% | train_error: 1.86 | train_acc: 0.820 
+
            +
            +
              [==>-------------------------------------] 8.200% | train_error: 1.86 | train_acc: 0.820 
+
            +
            +
              [==>-------------------------------------] 8.300% | train_error: 1.86 | train_acc: 0.820 
+
            +
            +
              [==>-------------------------------------] 8.400% | train_error: 1.87 | train_acc: 0.820 
+
            +
            +
              [==>-------------------------------------] 8.500% | train_error: 1.86 | train_acc: 0.820 
+
            +
            +
              [==>-------------------------------------] 8.600% | train_error: 1.86 | train_acc: 0.820 
+
            +
            +
              [==>-------------------------------------] 8.700% | train_error: 1.86 | train_acc: 0.820 
+
            +
            +
              [==>-------------------------------------] 8.800% | train_error: 1.86 | train_acc: 0.820 
+
            +
            +
              [==>-------------------------------------] 8.900% | train_error: 1.87 | train_acc: 0.820 
+
            +
            +
              [==>-------------------------------------] 9.000% | train_error: 1.87 | train_acc: 0.820 
+
            +
            +
              [==>-------------------------------------] 9.100% | train_error: 1.87 | train_acc: 0.819 
+
            +
            +
              [==>-------------------------------------] 9.200% | train_error: 1.87 | train_acc: 0.819 
+
            +
            +
              [==>-------------------------------------] 9.300% | train_error: 1.87 | train_acc: 0.819 
+
            +
            +
              [==>-------------------------------------] 9.400% | train_error: 1.87 | train_acc: 0.819 
+
            +
            +
              [==>-------------------------------------] 9.500% | train_error: 1.87 | train_acc: 0.819 
+
            +
            +
              [==>-------------------------------------] 9.600% | train_error: 1.87 | train_acc: 0.819 
+
            +
            +
              [==>-------------------------------------] 9.700% | train_error: 1.87 | train_acc: 0.819 
+
            +
            +
              [==>-------------------------------------] 9.800% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [==>-------------------------------------] 9.900% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [===>------------------------------------] 10.00% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [===>------------------------------------] 10.10% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [===>------------------------------------] 10.20% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [===>------------------------------------] 10.30% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [===>------------------------------------] 10.40% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [===>------------------------------------] 10.50% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [===>------------------------------------] 10.60% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [===>------------------------------------] 10.70% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [===>------------------------------------] 10.80% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [===>------------------------------------] 10.90% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [===>------------------------------------] 11.00% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [===>------------------------------------] 11.10% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [===>------------------------------------] 11.20% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [===>------------------------------------] 11.30% | train_error: 1.88 | train_acc: 0.818 
+
            +
            +
              [===>------------------------------------] 11.40% | train_error: 1.88 | train_acc: 0.818 
+
            +
            +
              [===>------------------------------------] 11.50% | train_error: 1.88 | train_acc: 0.818 
+
            +
            +
              [===>------------------------------------] 11.60% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [===>------------------------------------] 11.70% | train_error: 1.88 | train_acc: 0.818 
+
            +
            +
              [===>------------------------------------] 11.80% | train_error: 1.88 | train_acc: 0.818 
+
            +
            +
              [===>------------------------------------] 11.90% | train_error: 1.88 | train_acc: 0.818 
+
            +
            +
              [===>------------------------------------] 12.00% | train_error: 1.88 | train_acc: 0.818 
+
            +
            +
              [===>------------------------------------] 12.10% | train_error: 1.88 | train_acc: 0.818 
+
            +
            +
              [===>------------------------------------] 12.20% | train_error: 1.88 | train_acc: 0.818 
+
            +
            +
              [===>------------------------------------] 12.30% | train_error: 1.88 | train_acc: 0.818 
+
            +
            +
              [===>------------------------------------] 12.40% | train_error: 1.88 | train_acc: 0.818 
+
            +
            +
              [====>-----------------------------------] 12.50% | train_error: 1.88 | train_acc: 0.818 
+
            +
            +
              [====>-----------------------------------] 12.60% | train_error: 1.88 | train_acc: 0.818 
+
            +
            +
              [====>-----------------------------------] 12.70% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [====>-----------------------------------] 12.80% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [====>-----------------------------------] 12.90% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [====>-----------------------------------] 13.00% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [====>-----------------------------------] 13.10% | train_error: 1.87 | train_acc: 0.819 
+
            +
            +
              [====>-----------------------------------] 13.20% | train_error: 1.87 | train_acc: 0.819 
+
            +
            +
              [====>-----------------------------------] 13.30% | train_error: 1.87 | train_acc: 0.819 
+
            +
            +
              [====>-----------------------------------] 13.40% | train_error: 1.87 | train_acc: 0.819 
+
            +
            +
              [====>-----------------------------------] 13.50% | train_error: 1.87 | train_acc: 0.820 
+
            +
            +
              [====>-----------------------------------] 13.60% | train_error: 1.87 | train_acc: 0.820 
+
            +
            +
              [====>-----------------------------------] 13.70% | train_error: 1.87 | train_acc: 0.820 
+
            +
            +
              [====>-----------------------------------] 13.80% | train_error: 1.86 | train_acc: 0.820 
+
            +
            +
              [====>-----------------------------------] 13.90% | train_error: 1.86 | train_acc: 0.820 
+
            +
            +
              [====>-----------------------------------] 14.00% | train_error: 1.87 | train_acc: 0.820 
+
            +
            +
              [====>-----------------------------------] 14.10% | train_error: 1.87 | train_acc: 0.820 
+
            +
            +
              [====>-----------------------------------] 14.20% | train_error: 1.86 | train_acc: 0.820 
+
            +
            +
              [====>-----------------------------------] 14.30% | train_error: 1.86 | train_acc: 0.820 
+
            +
            +
              [====>-----------------------------------] 14.40% | train_error: 1.86 | train_acc: 0.820 
+
            +
            +
              [====>-----------------------------------] 14.50% | train_error: 1.86 | train_acc: 0.820 
+
            +
            +
              [====>-----------------------------------] 14.60% | train_error: 1.87 | train_acc: 0.820 
+
            +
            +
              [====>-----------------------------------] 14.70% | train_error: 1.87 | train_acc: 0.820 
+
            +
            +
              [====>-----------------------------------] 14.80% | train_error: 1.87 | train_acc: 0.820 
+
            +
            +
              [====>-----------------------------------] 14.90% | train_error: 1.87 | train_acc: 0.820 
+
            +
            +
              [=====>----------------------------------] 15.00% | train_error: 1.86 | train_acc: 0.820 
+
            +
            +
              [=====>----------------------------------] 15.10% | train_error: 1.86 | train_acc: 0.820 
+
            +
            +
              [=====>----------------------------------] 15.20% | train_error: 1.86 | train_acc: 0.820 
+
            +
            +
              [=====>----------------------------------] 15.30% | train_error: 1.86 | train_acc: 0.820 
+
            +
            +
              [=====>----------------------------------] 15.40% | train_error: 1.87 | train_acc: 0.820 
+
            +
            +
              [=====>----------------------------------] 15.50% | train_error: 1.87 | train_acc: 0.820 
+
            +
            +
              [=====>----------------------------------] 15.60% | train_error: 1.87 | train_acc: 0.820 
+
            +
            +
              [=====>----------------------------------] 15.70% | train_error: 1.87 | train_acc: 0.820 
+
            +
            +
              [=====>----------------------------------] 15.80% | train_error: 1.87 | train_acc: 0.820 
+
            +
            +
              [=====>----------------------------------] 15.90% | train_error: 1.87 | train_acc: 0.820 
+
            +
            +
              [=====>----------------------------------] 16.00% | train_error: 1.87 | train_acc: 0.819 
+
            +
            +
              [=====>----------------------------------] 16.10% | train_error: 1.87 | train_acc: 0.819 
+
            +
            +
              [=====>----------------------------------] 16.20% | train_error: 1.87 | train_acc: 0.819 
+
            +
            +
              [=====>----------------------------------] 16.30% | train_error: 1.87 | train_acc: 0.819 
+
            +
            +
              [=====>----------------------------------] 16.40% | train_error: 1.87 | train_acc: 0.819 
+
            +
            +
              [=====>----------------------------------] 16.50% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [=====>----------------------------------] 16.60% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [=====>----------------------------------] 16.70% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [=====>----------------------------------] 16.80% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [=====>----------------------------------] 16.90% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [=====>----------------------------------] 17.00% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [=====>----------------------------------] 17.10% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [=====>----------------------------------] 17.20% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [=====>----------------------------------] 17.30% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [=====>----------------------------------] 17.40% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [======>---------------------------------] 17.50% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [======>---------------------------------] 17.60% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [======>---------------------------------] 17.70% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [======>---------------------------------] 17.80% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [======>---------------------------------] 17.90% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [======>---------------------------------] 18.00% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [======>---------------------------------] 18.10% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [======>---------------------------------] 18.20% | train_error: 1.87 | train_acc: 0.820 
+
            +
            +
              [======>---------------------------------] 18.30% | train_error: 1.87 | train_acc: 0.820 
+
            +
            +
              [======>---------------------------------] 18.40% | train_error: 1.87 | train_acc: 0.819 
+
            +
            +
              [======>---------------------------------] 18.50% | train_error: 1.87 | train_acc: 0.820 
+
            +
            +
              [======>---------------------------------] 18.60% | train_error: 1.87 | train_acc: 0.819 
+
            +
            +
              [======>---------------------------------] 18.70% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [======>---------------------------------] 18.80% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [======>---------------------------------] 18.90% | train_error: 1.87 | train_acc: 0.819 
+
            +
            +
              [======>---------------------------------] 19.00% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [======>---------------------------------] 19.10% | train_error: 1.87 | train_acc: 0.819 
+
            +
            +
              [======>---------------------------------] 19.20% | train_error: 1.87 | train_acc: 0.819 
+
            +
            +
              [======>---------------------------------] 19.30% | train_error: 1.87 | train_acc: 0.819 
+
            +
            +
              [======>---------------------------------] 19.40% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [======>---------------------------------] 19.50% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [======>---------------------------------] 19.60% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [======>---------------------------------] 19.70% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [======>---------------------------------] 19.80% | train_error: 1.88 | train_acc: 0.818 
+
            +
            +
              [======>---------------------------------] 19.90% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [=======>--------------------------------] 20.00% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [=======>--------------------------------] 20.10% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [=======>--------------------------------] 20.20% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [=======>--------------------------------] 20.30% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [=======>--------------------------------] 20.40% | train_error: 1.88 | train_acc: 0.818 
+
            +
            +
              [=======>--------------------------------] 20.50% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [=======>--------------------------------] 20.60% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [=======>--------------------------------] 20.70% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [=======>--------------------------------] 20.80% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [=======>--------------------------------] 20.90% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [=======>--------------------------------] 21.00% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [=======>--------------------------------] 21.10% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [=======>--------------------------------] 21.20% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [=======>--------------------------------] 21.30% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [=======>--------------------------------] 21.40% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [=======>--------------------------------] 21.50% | train_error: 1.88 | train_acc: 0.818 
+
            +
            +
              [=======>--------------------------------] 21.60% | train_error: 1.88 | train_acc: 0.818 
+
            +
            +
              [=======>--------------------------------] 21.70% | train_error: 1.88 | train_acc: 0.818 
+
            +
            +
              [=======>--------------------------------] 21.80% | train_error: 1.88 | train_acc: 0.818 
+
            +
            +
              [=======>--------------------------------] 21.90% | train_error: 1.88 | train_acc: 0.818 
+
            +
            +
              [=======>--------------------------------] 22.00% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [=======>--------------------------------] 22.10% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [=======>--------------------------------] 22.20% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [=======>--------------------------------] 22.30% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [=======>--------------------------------] 22.40% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [========>-------------------------------] 22.50% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [========>-------------------------------] 22.60% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [========>-------------------------------] 22.70% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [========>-------------------------------] 22.80% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [========>-------------------------------] 22.90% | train_error: 1.88 | train_acc: 0.819 
+
            +
            +
              [========>-------------------------------] 23.00% | train_error: 1.87 | train_acc: 0.819 
+
            +
            +
              [========>-------------------------------] 23.10% | train_error: 1.87 | train_acc: 0.819 
+
            +
            +
              [========>-------------------------------] 23.20% | train_error: 1.87 | train_acc: 0.820 
+
            +
            +
              [========>-------------------------------] 23.30% | train_error: 1.87 | train_acc: 0.820 
+
            +
            +
              [========>-------------------------------] 23.40% | train_error: 1.86 | train_acc: 0.820 
+
            +
            +
              [========>-------------------------------] 23.50% | train_error: 1.86 | train_acc: 0.820 
+
            +
            +
              [========>-------------------------------] 23.60% | train_error: 1.86 | train_acc: 0.820 
+
            +
            +
              [========>-------------------------------] 23.70% | train_error: 1.86 | train_acc: 0.821 
+
            +
            +
              [========>-------------------------------] 23.80% | train_error: 1.86 | train_acc: 0.821 
+
            +
            +
              [========>-------------------------------] 23.90% | train_error: 1.86 | train_acc: 0.821 
+
            +
            +
              [========>-------------------------------] 24.00% | train_error: 1.86 | train_acc: 0.821 
+
            +
            +
              [========>-------------------------------] 24.10% | train_error: 1.86 | train_acc: 0.821 
+
            +
            +
              [========>-------------------------------] 24.20% | train_error: 1.86 | train_acc: 0.820 
+
            +
            +
              [========>-------------------------------] 24.30% | train_error: 1.86 | train_acc: 0.821 
+
            +
            +
              [========>-------------------------------] 24.40% | train_error: 1.86 | train_acc: 0.821 
+
            +
            +
              [========>-------------------------------] 24.50% | train_error: 1.85 | train_acc: 0.821 
+
            +
            +
              [========>-------------------------------] 24.60% | train_error: 1.85 | train_acc: 0.821 
+
            +
            +
              [========>-------------------------------] 24.70% | train_error: 1.85 | train_acc: 0.822 
+
            +
            +
              [========>-------------------------------] 24.80% | train_error: 1.85 | train_acc: 0.821 
+
            +
            +
              [========>-------------------------------] 24.90% | train_error: 1.85 | train_acc: 0.822 
+
            +
            +
              [=========>------------------------------] 25.00% | train_error: 1.85 | train_acc: 0.822 
+
            +
            +
              [=========>------------------------------] 25.10% | train_error: 1.84 | train_acc: 0.822 
+
            +
            +
              [=========>------------------------------] 25.20% | train_error: 1.84 | train_acc: 0.822 
+
            +
            +
              [=========>------------------------------] 25.30% | train_error: 1.84 | train_acc: 0.822 
+
            +
            +
              [=========>------------------------------] 25.40% | train_error: 1.84 | train_acc: 0.822 
+
            +
            +
              [=========>------------------------------] 25.50% | train_error: 1.84 | train_acc: 0.822 
+
            +
            +
              [=========>------------------------------] 25.60% | train_error: 1.84 | train_acc: 0.823 
+
            +
            +
              [=========>------------------------------] 25.70% | train_error: 1.84 | train_acc: 0.823 
+
            +
            +
              [=========>------------------------------] 25.80% | train_error: 1.83 | train_acc: 0.823 
+
            +
            +
              [=========>------------------------------] 25.90% | train_error: 1.83 | train_acc: 0.823 
+
            +
            +
              [=========>------------------------------] 26.00% | train_error: 1.83 | train_acc: 0.823 
+
            +
            +
              [=========>------------------------------] 26.10% | train_error: 1.83 | train_acc: 0.823 
+
            +
            +
              [=========>------------------------------] 26.20% | train_error: 1.83 | train_acc: 0.823 
+
            +
            +
              [=========>------------------------------] 26.30% | train_error: 1.83 | train_acc: 0.824 
+
            +
            +
              [=========>------------------------------] 26.40% | train_error: 1.83 | train_acc: 0.824 
+
            +
            +
              [=========>------------------------------] 26.50% | train_error: 1.83 | train_acc: 0.824 
+
            +
            +
              [=========>------------------------------] 26.60% | train_error: 1.83 | train_acc: 0.823 
+
            +
            +
              [=========>------------------------------] 26.70% | train_error: 1.83 | train_acc: 0.823 
+
            +
            +
              [=========>------------------------------] 26.80% | train_error: 1.83 | train_acc: 0.824 
+
            +
            +
              [=========>------------------------------] 26.90% | train_error: 1.82 | train_acc: 0.824 
+
            +
            +
              [=========>------------------------------] 27.00% | train_error: 1.82 | train_acc: 0.824 
+
            +
            +
              [=========>------------------------------] 27.10% | train_error: 1.82 | train_acc: 0.824 
+
            +
            +
              [=========>------------------------------] 27.20% | train_error: 1.82 | train_acc: 0.824 
+
            +
            +
              [=========>------------------------------] 27.30% | train_error: 1.82 | train_acc: 0.825 
+
            +
            +
              [=========>------------------------------] 27.40% | train_error: 1.82 | train_acc: 0.825 
+
            +
            +
              [==========>-----------------------------] 27.50% | train_error: 1.82 | train_acc: 0.825 
+
            +
            +
              [==========>-----------------------------] 27.60% | train_error: 1.82 | train_acc: 0.825 
+
            +
            +
              [==========>-----------------------------] 27.70% | train_error: 1.81 | train_acc: 0.825 
+
            +
            +
              [==========>-----------------------------] 27.80% | train_error: 1.81 | train_acc: 0.825 
+
            +
            +
              [==========>-----------------------------] 27.90% | train_error: 1.81 | train_acc: 0.825 
+
            +
            +
              [==========>-----------------------------] 28.00% | train_error: 1.81 | train_acc: 0.825 
+
            +
            +
              [==========>-----------------------------] 28.10% | train_error: 1.81 | train_acc: 0.825 
+
            +
            +
              [==========>-----------------------------] 28.20% | train_error: 1.81 | train_acc: 0.825 
+
            +
            +
              [==========>-----------------------------] 28.30% | train_error: 1.81 | train_acc: 0.825 
+
            +
            +
              [==========>-----------------------------] 28.40% | train_error: 1.80 | train_acc: 0.826 
+
            +
            +
              [==========>-----------------------------] 28.50% | train_error: 1.80 | train_acc: 0.826 
+
            +
            +
              [==========>-----------------------------] 28.60% | train_error: 1.80 | train_acc: 0.826 
+
            +
            +
              [==========>-----------------------------] 28.70% | train_error: 1.80 | train_acc: 0.826 
+
            +
            +
              [==========>-----------------------------] 28.80% | train_error: 1.80 | train_acc: 0.826 
+
            +
            +
              [==========>-----------------------------] 28.90% | train_error: 1.80 | train_acc: 0.826 
+
            +
            +
              [==========>-----------------------------] 29.00% | train_error: 1.80 | train_acc: 0.826 
+
            +
            +
              [==========>-----------------------------] 29.10% | train_error: 1.80 | train_acc: 0.826 
+
            +
            +
              [==========>-----------------------------] 29.20% | train_error: 1.80 | train_acc: 0.827 
+
            +
            +
              [==========>-----------------------------] 29.30% | train_error: 1.80 | train_acc: 0.827 
+
            +
            +
              [==========>-----------------------------] 29.40% | train_error: 1.80 | train_acc: 0.826 
+
            +
            +
              [==========>-----------------------------] 29.50% | train_error: 1.80 | train_acc: 0.827 
+
            +
            +
              [==========>-----------------------------] 29.60% | train_error: 1.80 | train_acc: 0.827 
+
            +
            +
              [==========>-----------------------------] 29.70% | train_error: 1.80 | train_acc: 0.826 
+
            +
            +
              [==========>-----------------------------] 29.80% | train_error: 1.79 | train_acc: 0.827 
+
            +
            +
              [==========>-----------------------------] 29.90% | train_error: 1.79 | train_acc: 0.827 
+
            +
            +
              [===========>----------------------------] 30.00% | train_error: 1.79 | train_acc: 0.827 
+
            +
            +
              [===========>----------------------------] 30.10% | train_error: 1.79 | train_acc: 0.828 
+
            +
            +
              [===========>----------------------------] 30.20% | train_error: 1.79 | train_acc: 0.827 
+
            +
            +
              [===========>----------------------------] 30.30% | train_error: 1.79 | train_acc: 0.827 
+
            +
            +
              [===========>----------------------------] 30.40% | train_error: 1.79 | train_acc: 0.827 
+
            +
            +
              [===========>----------------------------] 30.50% | train_error: 1.79 | train_acc: 0.828 
+
            +
            +
              [===========>----------------------------] 30.60% | train_error: 1.78 | train_acc: 0.828 
+
            +
            +
              [===========>----------------------------] 30.70% | train_error: 1.78 | train_acc: 0.828 
+
            +
            +
              [===========>----------------------------] 30.80% | train_error: 1.78 | train_acc: 0.828 
+
            +
            +
              [===========>----------------------------] 30.90% | train_error: 1.78 | train_acc: 0.828 
+
            +
            +
              [===========>----------------------------] 31.00% | train_error: 1.78 | train_acc: 0.828 
+
            +
            +
              [===========>----------------------------] 31.10% | train_error: 1.78 | train_acc: 0.828 
+
            +
            +
              [===========>----------------------------] 31.20% | train_error: 1.78 | train_acc: 0.828 
+
            +
            +
              [===========>----------------------------] 31.30% | train_error: 1.78 | train_acc: 0.828 
+
            +
            +
              [===========>----------------------------] 31.40% | train_error: 1.78 | train_acc: 0.829 
+
            +
            +
              [===========>----------------------------] 31.50% | train_error: 1.78 | train_acc: 0.829 
+
            +
            +
              [===========>----------------------------] 31.60% | train_error: 1.77 | train_acc: 0.829 
+
            +
            +
              [===========>----------------------------] 31.70% | train_error: 1.78 | train_acc: 0.829 
+
            +
            +
              [===========>----------------------------] 31.80% | train_error: 1.78 | train_acc: 0.829 
+
            +
            +
              [===========>----------------------------] 31.90% | train_error: 1.77 | train_acc: 0.829 
+
            +
            +
              [===========>----------------------------] 32.00% | train_error: 1.77 | train_acc: 0.829 
+
            +
            +
              [===========>----------------------------] 32.10% | train_error: 1.76 | train_acc: 0.830 
+
            +
            +
              [===========>----------------------------] 32.20% | train_error: 1.77 | train_acc: 0.829 
+
            +
            +
              [===========>----------------------------] 32.30% | train_error: 1.76 | train_acc: 0.830 
+
            +
            +
              [===========>----------------------------] 32.40% | train_error: 1.76 | train_acc: 0.830 
+
            +
            +
              [============>---------------------------] 32.50% | train_error: 1.76 | train_acc: 0.830 
+
            +
            +
              [============>---------------------------] 32.60% | train_error: 1.76 | train_acc: 0.830 
+
            +
            +
              [============>---------------------------] 32.70% | train_error: 1.76 | train_acc: 0.830 
+
            +
            +
              [============>---------------------------] 32.80% | train_error: 1.76 | train_acc: 0.830 
+
            +
            +
              [============>---------------------------] 32.90% | train_error: 1.76 | train_acc: 0.831 
+
            +
            +
              [============>---------------------------] 33.00% | train_error: 1.75 | train_acc: 0.831 
+
            +
            +
              [============>---------------------------] 33.10% | train_error: 1.75 | train_acc: 0.831 
+
            +
            +
              [============>---------------------------] 33.20% | train_error: 1.75 | train_acc: 0.831 
+
            +
            +
              [============>---------------------------] 33.30% | train_error: 1.75 | train_acc: 0.831 
+
            +
            +
              [============>---------------------------] 33.40% | train_error: 1.75 | train_acc: 0.831 
+
            +
            +
              [============>---------------------------] 33.50% | train_error: 1.75 | train_acc: 0.831 
+
            +
            +
              [============>---------------------------] 33.60% | train_error: 1.75 | train_acc: 0.831 
+
            +
            +
              [============>---------------------------] 33.70% | train_error: 1.75 | train_acc: 0.831 
+
            +
            +
              [============>---------------------------] 33.80% | train_error: 1.75 | train_acc: 0.831 
+
            +
            +
              [============>---------------------------] 33.90% | train_error: 1.75 | train_acc: 0.831 
+
            +
            +
              [============>---------------------------] 34.00% | train_error: 1.75 | train_acc: 0.831 
+
            +
            +
              [============>---------------------------] 34.10% | train_error: 1.74 | train_acc: 0.832 
+
            +
            +
              [============>---------------------------] 34.20% | train_error: 1.74 | train_acc: 0.832 
+
            +
            +
              [============>---------------------------] 34.30% | train_error: 1.74 | train_acc: 0.832 
+
            +
            +
              [============>---------------------------] 34.40% | train_error: 1.74 | train_acc: 0.832 
+
            +
            +
              [============>---------------------------] 34.50% | train_error: 1.74 | train_acc: 0.832 
+
            +
            +
              [============>---------------------------] 34.60% | train_error: 1.73 | train_acc: 0.833 
+
            +
            +
              [============>---------------------------] 34.70% | train_error: 1.73 | train_acc: 0.833 
+
            +
            +
              [============>---------------------------] 34.80% | train_error: 1.73 | train_acc: 0.833 
+
            +
            +
              [============>---------------------------] 34.90% | train_error: 1.73 | train_acc: 0.833 
+
            +
            +
              [=============>--------------------------] 35.00% | train_error: 1.73 | train_acc: 0.833 
+
            +
            +
              [=============>--------------------------] 35.10% | train_error: 1.73 | train_acc: 0.834 
+
            +
            +
              [=============>--------------------------] 35.20% | train_error: 1.73 | train_acc: 0.834 
+
            +
            +
              [=============>--------------------------] 35.30% | train_error: 1.72 | train_acc: 0.834 
+
            +
            +
              [=============>--------------------------] 35.40% | train_error: 1.73 | train_acc: 0.833 
+
            +
            +
              [=============>--------------------------] 35.50% | train_error: 1.73 | train_acc: 0.833 
+
            +
            +
              [=============>--------------------------] 35.60% | train_error: 1.74 | train_acc: 0.832 
+
            +
            +
              [=============>--------------------------] 35.70% | train_error: 1.73 | train_acc: 0.833 
+
            +
            +
              [=============>--------------------------] 35.80% | train_error: 1.74 | train_acc: 0.832 
+
            +
            +
              [=============>--------------------------] 35.90% | train_error: 1.74 | train_acc: 0.832 
+
            +
            +
              [=============>--------------------------] 36.00% | train_error: 1.73 | train_acc: 0.833 
+
            +
            +
              [=============>--------------------------] 36.10% | train_error: 1.73 | train_acc: 0.833 
+
            +
            +
              [=============>--------------------------] 36.20% | train_error: 1.74 | train_acc: 0.832 
+
            +
            +
              [=============>--------------------------] 36.30% | train_error: 1.74 | train_acc: 0.832 
+
            +
            +
              [=============>--------------------------] 36.40% | train_error: 1.74 | train_acc: 0.832 
+
            +
            +
              [=============>--------------------------] 36.50% | train_error: 1.74 | train_acc: 0.832 
+
            +
            +
              [=============>--------------------------] 36.60% | train_error: 1.74 | train_acc: 0.832 
+
            +
            +
              [=============>--------------------------] 36.70% | train_error: 1.74 | train_acc: 0.832 
+
            +
            +
              [=============>--------------------------] 36.80% | train_error: 1.74 | train_acc: 0.832 
+
            +
            +
              [=============>--------------------------] 36.90% | train_error: 1.74 | train_acc: 0.832 
+
            +
            +
              [=============>--------------------------] 37.00% | train_error: 1.74 | train_acc: 0.832 
+
            +
            +
              [=============>--------------------------] 37.10% | train_error: 1.74 | train_acc: 0.832 
+
            +
            +
              [=============>--------------------------] 37.20% | train_error: 1.74 | train_acc: 0.832 
+
            +
            +
              [=============>--------------------------] 37.30% | train_error: 1.74 | train_acc: 0.832 
+
            +
            +
              [=============>--------------------------] 37.40% | train_error: 1.74 | train_acc: 0.832 
+
            +
            +
              [==============>-------------------------] 37.50% | train_error: 1.74 | train_acc: 0.832 
+
            +
            +
              [==============>-------------------------] 37.60% | train_error: 1.73 | train_acc: 0.833 
+
            +
            +
              [==============>-------------------------] 37.70% | train_error: 1.72 | train_acc: 0.834 
+
            +
            +
              [==============>-------------------------] 37.80% | train_error: 1.73 | train_acc: 0.834 
+
            +
            +
              [==============>-------------------------] 37.90% | train_error: 1.73 | train_acc: 0.833 
+
            +
            +
              [==============>-------------------------] 38.00% | train_error: 1.73 | train_acc: 0.833 
+
            +
            +
              [==============>-------------------------] 38.10% | train_error: 1.73 | train_acc: 0.833 
+
            +
            +
              [==============>-------------------------] 38.20% | train_error: 1.73 | train_acc: 0.833 
+
            +
            +
              [==============>-------------------------] 38.30% | train_error: 1.72 | train_acc: 0.834 
+
            +
            +
              [==============>-------------------------] 38.40% | train_error: 1.73 | train_acc: 0.834 
+
            +
            +
              [==============>-------------------------] 38.50% | train_error: 1.73 | train_acc: 0.834 
+
            +
            +
              [==============>-------------------------] 38.60% | train_error: 1.73 | train_acc: 0.833 
+
            +
            +
              [==============>-------------------------] 38.70% | train_error: 1.73 | train_acc: 0.834 
+
            +
            +
              [==============>-------------------------] 38.80% | train_error: 1.72 | train_acc: 0.834 
+
            +
            +
              [==============>-------------------------] 38.90% | train_error: 1.72 | train_acc: 0.834 
+
            +
            +
              [==============>-------------------------] 39.00% | train_error: 1.73 | train_acc: 0.833 
+
            +
            +
              [==============>-------------------------] 39.10% | train_error: 1.73 | train_acc: 0.833 
+
            +
            +
              [==============>-------------------------] 39.20% | train_error: 1.73 | train_acc: 0.833 
+
            +
            +
              [==============>-------------------------] 39.30% | train_error: 1.73 | train_acc: 0.833 
+
            +
            +
              [==============>-------------------------] 39.40% | train_error: 1.73 | train_acc: 0.833 
+
            +
            +
              [==============>-------------------------] 39.50% | train_error: 1.73 | train_acc: 0.833 
+
            +
            +
              [==============>-------------------------] 39.60% | train_error: 1.73 | train_acc: 0.833 
+
            +
            +
              [==============>-------------------------] 39.70% | train_error: 1.73 | train_acc: 0.833 
+
            +
            +
              [==============>-------------------------] 39.80% | train_error: 1.73 | train_acc: 0.833 
+
            +
            +
              [==============>-------------------------] 39.90% | train_error: 1.73 | train_acc: 0.833 
+
            +
            +
              [===============>------------------------] 40.00% | train_error: 1.73 | train_acc: 0.833 
+
            +
            +
              [===============>------------------------] 40.10% | train_error: 1.72 | train_acc: 0.834 
+
            +
            +
              [===============>------------------------] 40.20% | train_error: 1.72 | train_acc: 0.834 
+
            +
            +
              [===============>------------------------] 40.30% | train_error: 1.72 | train_acc: 0.834 
+
            +
            +
              [===============>------------------------] 40.40% | train_error: 1.71 | train_acc: 0.835 
+
            +
            +
              [===============>------------------------] 40.50% | train_error: 1.71 | train_acc: 0.835 
+
            +
            +
              [===============>------------------------] 40.60% | train_error: 1.71 | train_acc: 0.835 
+
            +
            +
              [===============>------------------------] 40.70% | train_error: 1.71 | train_acc: 0.835 
+
            +
            +
              [===============>------------------------] 40.80% | train_error: 1.71 | train_acc: 0.835 
+
            +
            +
              [===============>------------------------] 40.90% | train_error: 1.71 | train_acc: 0.835 
+
            +
            +
              [===============>------------------------] 41.00% | train_error: 1.71 | train_acc: 0.835 
+
            +
            +
              [===============>------------------------] 41.10% | train_error: 1.71 | train_acc: 0.835 
+
            +
            +
              [===============>------------------------] 41.20% | train_error: 1.71 | train_acc: 0.835 
+
            +
            +
              [===============>------------------------] 41.30% | train_error: 1.71 | train_acc: 0.835 
+
            +
            +
              [===============>------------------------] 41.40% | train_error: 1.71 | train_acc: 0.835 
+
            +
            +
              [===============>------------------------] 41.50% | train_error: 1.70 | train_acc: 0.836 
+
            +
            +
              [===============>------------------------] 41.60% | train_error: 1.70 | train_acc: 0.836 
+
            +
            +
              [===============>------------------------] 41.70% | train_error: 1.70 | train_acc: 0.836 
+
            +
            +
              [===============>------------------------] 41.80% | train_error: 1.70 | train_acc: 0.836 
+
            +
            +
              [===============>------------------------] 41.90% | train_error: 1.70 | train_acc: 0.836 
+
            +
            +
              [===============>------------------------] 42.00% | train_error: 1.70 | train_acc: 0.836 
+
            +
            +
              [===============>------------------------] 42.10% | train_error: 1.69 | train_acc: 0.837 
+
            +
            +
              [===============>------------------------] 42.20% | train_error: 1.69 | train_acc: 0.837 
+
            +
            +
              [===============>------------------------] 42.30% | train_error: 1.69 | train_acc: 0.837 
+
            +
            +
              [===============>------------------------] 42.40% | train_error: 1.69 | train_acc: 0.837 
+
            +
            +
              [================>-----------------------] 42.50% | train_error: 1.69 | train_acc: 0.837 
+
            +
            +
              [================>-----------------------] 42.60% | train_error: 1.69 | train_acc: 0.837 
+
            +
            +
              [================>-----------------------] 42.70% | train_error: 1.68 | train_acc: 0.838 
+
            +
            +
              [================>-----------------------] 42.80% | train_error: 1.68 | train_acc: 0.838 
+
            +
            +
              [================>-----------------------] 42.90% | train_error: 1.68 | train_acc: 0.838 
+
            +
            +
              [================>-----------------------] 43.00% | train_error: 1.68 | train_acc: 0.838 
+
            +
            +
              [================>-----------------------] 43.10% | train_error: 1.68 | train_acc: 0.838 
+
            +
            +
              [================>-----------------------] 43.20% | train_error: 1.67 | train_acc: 0.839 
+
            +
            +
              [================>-----------------------] 43.30% | train_error: 1.67 | train_acc: 0.839 
+
            +
            +
              [================>-----------------------] 43.40% | train_error: 1.67 | train_acc: 0.839 
+
            +
            +
              [================>-----------------------] 43.50% | train_error: 1.67 | train_acc: 0.839 
+
            +
            +
              [================>-----------------------] 43.60% | train_error: 1.66 | train_acc: 0.840 
+
            +
            +
              [================>-----------------------] 43.70% | train_error: 1.66 | train_acc: 0.840 
+
            +
            +
              [================>-----------------------] 43.80% | train_error: 1.65 | train_acc: 0.840 
+
            +
            +
              [================>-----------------------] 43.90% | train_error: 1.65 | train_acc: 0.841 
+
            +
            +
              [================>-----------------------] 44.00% | train_error: 1.65 | train_acc: 0.841 
+
            +
            +
              [================>-----------------------] 44.10% | train_error: 1.64 | train_acc: 0.841 
+
            +
            +
              [================>-----------------------] 44.20% | train_error: 1.64 | train_acc: 0.842 
+
            +
            +
              [================>-----------------------] 44.30% | train_error: 1.62 | train_acc: 0.843 
+
            +
            +
              [================>-----------------------] 44.40% | train_error: 1.62 | train_acc: 0.843 
+
            +
            +
              [================>-----------------------] 44.50% | train_error: 1.62 | train_acc: 0.843 
+
            +
            +
              [================>-----------------------] 44.60% | train_error: 1.62 | train_acc: 0.844 
+
            +
            +
              [================>-----------------------] 44.70% | train_error: 1.61 | train_acc: 0.844 
+
            +
            +
              [================>-----------------------] 44.80% | train_error: 1.61 | train_acc: 0.845 
+
            +
            +
              [================>-----------------------] 44.90% | train_error: 1.60 | train_acc: 0.846 
+
            +
            +
              [=================>----------------------] 45.00% | train_error: 1.59 | train_acc: 0.846 
+
            +
            +
              [=================>----------------------] 45.10% | train_error: 1.59 | train_acc: 0.846 
+
            +
            +
              [=================>----------------------] 45.20% | train_error: 1.59 | train_acc: 0.847 
+
            +
            +
              [=================>----------------------] 45.30% | train_error: 1.58 | train_acc: 0.847 
+
            +
            +
              [=================>----------------------] 45.40% | train_error: 1.58 | train_acc: 0.847 
+
            +
            +
              [=================>----------------------] 45.50% | train_error: 1.58 | train_acc: 0.848 
+
            +
            +
              [=================>----------------------] 45.60% | train_error: 1.57 | train_acc: 0.848 
+
            +
            +
              [=================>----------------------] 45.70% | train_error: 1.57 | train_acc: 0.849 
+
            +
            +
              [=================>----------------------] 45.80% | train_error: 1.56 | train_acc: 0.849 
+
            +
            +
              [=================>----------------------] 45.90% | train_error: 1.56 | train_acc: 0.850 
+
            +
            +
              [=================>----------------------] 46.00% | train_error: 1.55 | train_acc: 0.850 
+
            +
            +
              [=================>----------------------] 46.10% | train_error: 1.55 | train_acc: 0.851 
+
            +
            +
              [=================>----------------------] 46.20% | train_error: 1.55 | train_acc: 0.851 
+
            +
            +
              [=================>----------------------] 46.30% | train_error: 1.54 | train_acc: 0.852 
+
            +
            +
              [=================>----------------------] 46.40% | train_error: 1.53 | train_acc: 0.852 
+
            +
            +
              [=================>----------------------] 46.50% | train_error: 1.53 | train_acc: 0.852 
+
            +
            +
              [=================>----------------------] 46.60% | train_error: 1.53 | train_acc: 0.853 
+
            +
            +
              [=================>----------------------] 46.70% | train_error: 1.52 | train_acc: 0.853 
+
            +
            +
              [=================>----------------------] 46.80% | train_error: 1.52 | train_acc: 0.854 
+
            +
            +
              [=================>----------------------] 46.90% | train_error: 1.52 | train_acc: 0.854 
+
            +
            +
              [=================>----------------------] 47.00% | train_error: 1.51 | train_acc: 0.854 
+
            +
            +
              [=================>----------------------] 47.10% | train_error: 1.51 | train_acc: 0.854 
+
            +
            +
              [=================>----------------------] 47.20% | train_error: 1.50 | train_acc: 0.855 
+
            +
            +
              [=================>----------------------] 47.30% | train_error: 1.50 | train_acc: 0.855 
+
            +
            +
              [=================>----------------------] 47.40% | train_error: 1.49 | train_acc: 0.856 
+
            +
            +
              [==================>---------------------] 47.50% | train_error: 1.47 | train_acc: 0.858 
+
            +
            +
              [==================>---------------------] 47.60% | train_error: 1.47 | train_acc: 0.858 
+
            +
            +
              [==================>---------------------] 47.70% | train_error: 1.47 | train_acc: 0.858 
+
            +
            +
              [==================>---------------------] 47.80% | train_error: 1.46 | train_acc: 0.859 
+
            +
            +
              [==================>---------------------] 47.90% | train_error: 1.46 | train_acc: 0.859 
+
            +
            +
              [==================>---------------------] 48.00% | train_error: 1.46 | train_acc: 0.859 
+
            +
            +
              [==================>---------------------] 48.10% | train_error: 1.46 | train_acc: 0.859 
+
            +
            +
              [==================>---------------------] 48.20% | train_error: 1.46 | train_acc: 0.859 
+
            +
            +
              [==================>---------------------] 48.30% | train_error: 1.46 | train_acc: 0.859 
+
            +
            +
              [==================>---------------------] 48.40% | train_error: 1.45 | train_acc: 0.860 
+
            +
            +
              [==================>---------------------] 48.50% | train_error: 1.44 | train_acc: 0.861 
+
            +
            +
              [==================>---------------------] 48.60% | train_error: 1.44 | train_acc: 0.861 
+
            +
            +
              [==================>---------------------] 48.70% | train_error: 1.44 | train_acc: 0.861 
+
            +
            +
              [==================>---------------------] 48.80% | train_error: 1.44 | train_acc: 0.861 
+
            +
            +
              [==================>---------------------] 48.90% | train_error: 1.43 | train_acc: 0.862 
+
            +
            +
              [==================>---------------------] 49.00% | train_error: 1.42 | train_acc: 0.863 
+
            +
            +
              [==================>---------------------] 49.10% | train_error: 1.41 | train_acc: 0.864 
+
            +
            +
              [==================>---------------------] 49.20% | train_error: 1.40 | train_acc: 0.865 
+
            +
            +
              [==================>---------------------] 49.30% | train_error: 1.39 | train_acc: 0.866 
+
            +
            +
              [==================>---------------------] 49.40% | train_error: 1.39 | train_acc: 0.866 
+
            +
            +
              [==================>---------------------] 49.50% | train_error: 1.39 | train_acc: 0.866 
+
            +
            +
              [==================>---------------------] 49.60% | train_error: 1.39 | train_acc: 0.866 
+
            +
            +
              [==================>---------------------] 49.70% | train_error: 1.38 | train_acc: 0.867 
+
            +
            +
              [==================>---------------------] 49.80% | train_error: 1.38 | train_acc: 0.867 
+
            +
            +
              [==================>---------------------] 49.90% | train_error: 1.37 | train_acc: 0.868 
+
            +
            +
              [===================>--------------------] 50.00% | train_error: 1.36 | train_acc: 0.869 
+
            +
            +
              [===================>--------------------] 50.10% | train_error: 1.35 | train_acc: 0.869 
+
            +
            +
              [===================>--------------------] 50.20% | train_error: 1.35 | train_acc: 0.870 
+
            +
            +
              [===================>--------------------] 50.30% | train_error: 1.34 | train_acc: 0.870 
+
            +
            +
              [===================>--------------------] 50.40% | train_error: 1.34 | train_acc: 0.870 
+
            +
            +
              [===================>--------------------] 50.50% | train_error: 1.34 | train_acc: 0.870 
+
            +
            +
              [===================>--------------------] 50.60% | train_error: 1.34 | train_acc: 0.871 
+
            +
            +
              [===================>--------------------] 50.70% | train_error: 1.33 | train_acc: 0.872 
+
            +
            +
              [===================>--------------------] 50.80% | train_error: 1.32 | train_acc: 0.873 
+
            +
            +
              [===================>--------------------] 50.90% | train_error: 1.31 | train_acc: 0.873 
+
            +
            +
              [===================>--------------------] 51.00% | train_error: 1.31 | train_acc: 0.874 
+
            +
            +
              [===================>--------------------] 51.10% | train_error: 1.31 | train_acc: 0.874 
+
            +
            +
              [===================>--------------------] 51.20% | train_error: 1.31 | train_acc: 0.874 
+
            +
            +
              [===================>--------------------] 51.30% | train_error: 1.31 | train_acc: 0.874 
+
            +
            +
              [===================>--------------------] 51.40% | train_error: 1.31 | train_acc: 0.874 
+
            +
            +
              [===================>--------------------] 51.50% | train_error: 1.31 | train_acc: 0.874 
+
            +
            +
              [===================>--------------------] 51.60% | train_error: 1.30 | train_acc: 0.874 
+
            +
            +
              [===================>--------------------] 51.70% | train_error: 1.30 | train_acc: 0.875 
+
            +
            +
              [===================>--------------------] 51.80% | train_error: 1.30 | train_acc: 0.875 
+
            +
            +
              [===================>--------------------] 51.90% | train_error: 1.29 | train_acc: 0.875 
+
            +
            +
              [===================>--------------------] 52.00% | train_error: 1.29 | train_acc: 0.875 
+
            +
            +
              [===================>--------------------] 52.10% | train_error: 1.29 | train_acc: 0.875 
+
            +
            +
              [===================>--------------------] 52.20% | train_error: 1.29 | train_acc: 0.875 
+
            +
            +
              [===================>--------------------] 52.30% | train_error: 1.29 | train_acc: 0.876 
+
            +
            +
              [===================>--------------------] 52.40% | train_error: 1.28 | train_acc: 0.876 
+
            +
            +
              [====================>-------------------] 52.50% | train_error: 1.28 | train_acc: 0.876 
+
            +
            +
              [====================>-------------------] 52.60% | train_error: 1.28 | train_acc: 0.877 
+
            +
            +
              [====================>-------------------] 52.70% | train_error: 1.28 | train_acc: 0.877 
+
            +
            +
              [====================>-------------------] 52.80% | train_error: 1.27 | train_acc: 0.877 
+
            +
            +
              [====================>-------------------] 52.90% | train_error: 1.27 | train_acc: 0.877 
+
            +
            +
              [====================>-------------------] 53.00% | train_error: 1.27 | train_acc: 0.877 
+
            +
            +
              [====================>-------------------] 53.10% | train_error: 1.27 | train_acc: 0.878 
+
            +
            +
              [====================>-------------------] 53.20% | train_error: 1.26 | train_acc: 0.878 
+
            +
            +
              [====================>-------------------] 53.30% | train_error: 1.26 | train_acc: 0.878 
+
            +
            +
              [====================>-------------------] 53.40% | train_error: 1.26 | train_acc: 0.879 
+
            +
            +
              [====================>-------------------] 53.50% | train_error: 1.25 | train_acc: 0.879 
+
            +
            +
              [====================>-------------------] 53.60% | train_error: 1.25 | train_acc: 0.880 
+
            +
            +
              [====================>-------------------] 53.70% | train_error: 1.24 | train_acc: 0.880 
+
            +
            +
              [====================>-------------------] 53.80% | train_error: 1.24 | train_acc: 0.880 
+
            +
            +
              [====================>-------------------] 53.90% | train_error: 1.24 | train_acc: 0.880 
+
            +
            +
              [====================>-------------------] 54.00% | train_error: 1.23 | train_acc: 0.881 
+
            +
            +
              [====================>-------------------] 54.10% | train_error: 1.22 | train_acc: 0.882 
+
            +
            +
              [====================>-------------------] 54.20% | train_error: 1.22 | train_acc: 0.882 
+
            +
            +
              [====================>-------------------] 54.30% | train_error: 1.21 | train_acc: 0.883 
+
            +
            +
              [====================>-------------------] 54.40% | train_error: 1.21 | train_acc: 0.883 
+
            +
            +
              [====================>-------------------] 54.50% | train_error: 1.21 | train_acc: 0.883 
+
            +
            +
              [====================>-------------------] 54.60% | train_error: 1.21 | train_acc: 0.883 
+
            +
            +
              [====================>-------------------] 54.70% | train_error: 1.21 | train_acc: 0.884 
+
            +
            +
              [====================>-------------------] 54.80% | train_error: 1.20 | train_acc: 0.884 
+
            +
            +
              [====================>-------------------] 54.90% | train_error: 1.20 | train_acc: 0.884 
+
            +
            +
              [=====================>------------------] 55.00% | train_error: 1.20 | train_acc: 0.884 
+
            +
            +
              [=====================>------------------] 55.10% | train_error: 1.20 | train_acc: 0.884 
+
            +
            +
              [=====================>------------------] 55.20% | train_error: 1.19 | train_acc: 0.885 
+
            +
            +
              [=====================>------------------] 55.30% | train_error: 1.19 | train_acc: 0.885 
+
            +
            +
              [=====================>------------------] 55.40% | train_error: 1.19 | train_acc: 0.885 
+
            +
            +
              [=====================>------------------] 55.50% | train_error: 1.19 | train_acc: 0.885 
+
            +
            +
              [=====================>------------------] 55.60% | train_error: 1.19 | train_acc: 0.885 
+
            +
            +
              [=====================>------------------] 55.70% | train_error: 1.19 | train_acc: 0.885 
+
            +
            +
              [=====================>------------------] 55.80% | train_error: 1.18 | train_acc: 0.886 
+
            +
            +
              [=====================>------------------] 55.90% | train_error: 1.19 | train_acc: 0.885 
+
            +
            +
              [=====================>------------------] 56.00% | train_error: 1.19 | train_acc: 0.885 
+
            +
            +
              [=====================>------------------] 56.10% | train_error: 1.18 | train_acc: 0.886 
+
            +
            +
              [=====================>------------------] 56.20% | train_error: 1.18 | train_acc: 0.886 
+
            +
            +
              [=====================>------------------] 56.30% | train_error: 1.17 | train_acc: 0.887 
+
            +
            +
              [=====================>------------------] 56.40% | train_error: 1.17 | train_acc: 0.887 
+
            +
            +
              [=====================>------------------] 56.50% | train_error: 1.17 | train_acc: 0.887 
+
            +
            +
              [=====================>------------------] 56.60% | train_error: 1.16 | train_acc: 0.888 
+
            +
            +
              [=====================>------------------] 56.70% | train_error: 1.16 | train_acc: 0.888 
+
            +
            +
              [=====================>------------------] 56.80% | train_error: 1.15 | train_acc: 0.889 
+
            +
            +
              [=====================>------------------] 56.90% | train_error: 1.15 | train_acc: 0.889 
+
            +
            +
              [=====================>------------------] 57.00% | train_error: 1.14 | train_acc: 0.890 
+
            +
            +
              [=====================>------------------] 57.10% | train_error: 1.14 | train_acc: 0.890 
+
            +
            +
              [=====================>------------------] 57.20% | train_error: 1.14 | train_acc: 0.890 
+
            +
            +
              [=====================>------------------] 57.30% | train_error: 1.14 | train_acc: 0.890 
+
            +
            +
              [=====================>------------------] 57.40% | train_error: 1.13 | train_acc: 0.891 
+
            +
            +
              [======================>-----------------] 57.50% | train_error: 1.13 | train_acc: 0.891 
+
            +
            +
              [======================>-----------------] 57.60% | train_error: 1.13 | train_acc: 0.891 
+
            +
            +
              [======================>-----------------] 57.70% | train_error: 1.12 | train_acc: 0.891 
+
            +
            +
              [======================>-----------------] 57.80% | train_error: 1.13 | train_acc: 0.891 
+
            +
            +
              [======================>-----------------] 57.90% | train_error: 1.12 | train_acc: 0.892 
+
            +
            +
              [======================>-----------------] 58.00% | train_error: 1.11 | train_acc: 0.893 
+
            +
            +
              [======================>-----------------] 58.10% | train_error: 1.11 | train_acc: 0.893 
+
            +
            +
              [======================>-----------------] 58.20% | train_error: 1.09 | train_acc: 0.894 
+
            +
            +
              [======================>-----------------] 58.30% | train_error: 1.09 | train_acc: 0.895 
+
            +
            +
              [======================>-----------------] 58.40% | train_error: 1.09 | train_acc: 0.895 
+
            +
            +
              [======================>-----------------] 58.50% | train_error: 1.09 | train_acc: 0.895 
+
            +
            +
              [======================>-----------------] 58.60% | train_error: 1.09 | train_acc: 0.895 
+
            +
            +
              [======================>-----------------] 58.70% | train_error: 1.09 | train_acc: 0.895 
+
            +
            +
              [======================>-----------------] 58.80% | train_error: 1.08 | train_acc: 0.895 
+
            +
            +
              [======================>-----------------] 58.90% | train_error: 1.08 | train_acc: 0.895 
+
            +
            +
              [======================>-----------------] 59.00% | train_error: 1.08 | train_acc: 0.896 
+
            +
            +
              [======================>-----------------] 59.10% | train_error: 1.08 | train_acc: 0.896 
+
            +
            +
              [======================>-----------------] 59.20% | train_error: 1.08 | train_acc: 0.896 
+
            +
            +
              [======================>-----------------] 59.30% | train_error: 1.07 | train_acc: 0.896 
+
            +
            +
              [======================>-----------------] 59.40% | train_error: 1.07 | train_acc: 0.897 
+
            +
            +
              [======================>-----------------] 59.50% | train_error: 1.06 | train_acc: 0.898 
+
            +
            +
              [======================>-----------------] 59.60% | train_error: 1.05 | train_acc: 0.898 
+
            +
            +
              [======================>-----------------] 59.70% | train_error: 1.05 | train_acc: 0.899 
+
            +
            +
              [======================>-----------------] 59.80% | train_error: 1.04 | train_acc: 0.899 
+
            +
            +
              [======================>-----------------] 59.90% | train_error: 1.04 | train_acc: 0.899 
+
            +
            +
              [=======================>----------------] 60.00% | train_error: 1.04 | train_acc: 0.899 
+
            +
            +
              [=======================>----------------] 60.10% | train_error: 1.04 | train_acc: 0.900 
+
            +
            +
              [=======================>----------------] 60.20% | train_error: 1.03 | train_acc: 0.900 
+
            +
            +
              [=======================>----------------] 60.30% | train_error: 1.03 | train_acc: 0.901 
+
            +
            +
              [=======================>----------------] 60.40% | train_error: 1.03 | train_acc: 0.901 
+
            +
            +
              [=======================>----------------] 60.50% | train_error: 1.02 | train_acc: 0.901 
+
            +
            +
              [=======================>----------------] 60.60% | train_error: 1.02 | train_acc: 0.902 
+
            +
            +
              [=======================>----------------] 60.70% | train_error: 1.01 | train_acc: 0.902 
+
            +
            +
              [=======================>----------------] 60.80% | train_error: 1.01 | train_acc: 0.903 
+
            +
            +
              [=======================>----------------] 60.90% | train_error: 1.00 | train_acc: 0.903 
+
            +
            +
              [=======================>----------------] 61.00% | train_error: 1.00 | train_acc: 0.903 
+
            +
            +
              [=======================>----------------] 61.10% | train_error: 1.00 | train_acc: 0.903 
+
            +
            +
              [=======================>----------------] 61.20% | train_error: 1.00 | train_acc: 0.903 
+
            +
            +
              [=======================>----------------] 61.30% | train_error: 0.999 | train_acc: 0.904 
+
            +
            +
              [=======================>----------------] 61.40% | train_error: 0.998 | train_acc: 0.904 
+
            +
            +
              [=======================>----------------] 61.50% | train_error: 0.998 | train_acc: 0.904 
+
            +
            +
              [=======================>----------------] 61.60% | train_error: 0.994 | train_acc: 0.904 
+
            +
            +
              [=======================>----------------] 61.70% | train_error: 0.994 | train_acc: 0.904 
+
            +
            +
              [=======================>----------------] 61.80% | train_error: 0.991 | train_acc: 0.904 
+
            +
            +
              [=======================>----------------] 61.90% | train_error: 0.987 | train_acc: 0.905 
+
            +
            +
              [=======================>----------------] 62.00% | train_error: 0.986 | train_acc: 0.905 
+
            +
            +
              [=======================>----------------] 62.10% | train_error: 0.978 | train_acc: 0.906 
+
            +
            +
              [=======================>----------------] 62.20% | train_error: 0.976 | train_acc: 0.906 
+
            +
            +
              [=======================>----------------] 62.30% | train_error: 0.976 | train_acc: 0.906 
+
            +
            +
              [=======================>----------------] 62.40% | train_error: 0.973 | train_acc: 0.906 
+
            +
            +
              [========================>---------------] 62.50% | train_error: 0.972 | train_acc: 0.906 
+
            +
            +
              [========================>---------------] 62.60% | train_error: 0.968 | train_acc: 0.907 
+
            +
            +
              [========================>---------------] 62.70% | train_error: 0.968 | train_acc: 0.907 
+
            +
            +
              [========================>---------------] 62.80% | train_error: 0.965 | train_acc: 0.907 
+
            +
            +
              [========================>---------------] 62.90% | train_error: 0.966 | train_acc: 0.907 
+
            +
            +
              [========================>---------------] 63.00% | train_error: 0.962 | train_acc: 0.907 
+
            +
            +
              [========================>---------------] 63.10% | train_error: 0.954 | train_acc: 0.908 
+
            +
            +
              [========================>---------------] 63.20% | train_error: 0.951 | train_acc: 0.908 
+
            +
            +
              [========================>---------------] 63.30% | train_error: 0.948 | train_acc: 0.909 
+
            +
            +
              [========================>---------------] 63.40% | train_error: 0.946 | train_acc: 0.909 
+
            +
            +
              [========================>---------------] 63.50% | train_error: 0.943 | train_acc: 0.909 
+
            +
            +
              [========================>---------------] 63.60% | train_error: 0.935 | train_acc: 0.910 
+
            +
            +
              [========================>---------------] 63.70% | train_error: 0.926 | train_acc: 0.911 
+
            +
            +
              [========================>---------------] 63.80% | train_error: 0.926 | train_acc: 0.911 
+
            +
            +
              [========================>---------------] 63.90% | train_error: 0.926 | train_acc: 0.911 
+
            +
            +
              [========================>---------------] 64.00% | train_error: 0.914 | train_acc: 0.912 
+
            +
            +
              [========================>---------------] 64.10% | train_error: 0.914 | train_acc: 0.912 
+
            +
            +
              [========================>---------------] 64.20% | train_error: 0.906 | train_acc: 0.913 
+
            +
            +
              [========================>---------------] 64.30% | train_error: 0.903 | train_acc: 0.913 
+
            +
            +
              [========================>---------------] 64.40% | train_error: 0.900 | train_acc: 0.913 
+
            +
            +
              [========================>---------------] 64.50% | train_error: 0.895 | train_acc: 0.914 
+
            +
            +
              [========================>---------------] 64.60% | train_error: 0.894 | train_acc: 0.914 
+
            +
            +
              [========================>---------------] 64.70% | train_error: 0.891 | train_acc: 0.914 
+
            +
            +
              [========================>---------------] 64.80% | train_error: 0.893 | train_acc: 0.914 
+
            +
            +
              [========================>---------------] 64.90% | train_error: 0.893 | train_acc: 0.914 
+
            +
            +
              [=========================>--------------] 65.00% | train_error: 0.890 | train_acc: 0.914 
+
            +
            +
              [=========================>--------------] 65.10% | train_error: 0.889 | train_acc: 0.914 
+
            +
            +
              [=========================>--------------] 65.20% | train_error: 0.883 | train_acc: 0.915 
+
            +
            +
              [=========================>--------------] 65.30% | train_error: 0.880 | train_acc: 0.915 
+
            +
            +
              [=========================>--------------] 65.40% | train_error: 0.878 | train_acc: 0.915 
+
            +
            +
              [=========================>--------------] 65.50% | train_error: 0.876 | train_acc: 0.915 
+
            +
            +
              [=========================>--------------] 65.60% | train_error: 0.875 | train_acc: 0.916 
+
            +
            +
              [=========================>--------------] 65.70% | train_error: 0.874 | train_acc: 0.916 
+
            +
            +
              [=========================>--------------] 65.80% | train_error: 0.875 | train_acc: 0.916 
+
            +
            +
              [=========================>--------------] 65.90% | train_error: 0.873 | train_acc: 0.916 
+
            +
            +
              [=========================>--------------] 66.00% | train_error: 0.871 | train_acc: 0.916 
+
            +
            +
              [=========================>--------------] 66.10% | train_error: 0.868 | train_acc: 0.916 
+
            +
            +
              [=========================>--------------] 66.20% | train_error: 0.866 | train_acc: 0.916 
+
            +
            +
              [=========================>--------------] 66.30% | train_error: 0.864 | train_acc: 0.917 
+
            +
            +
              [=========================>--------------] 66.40% | train_error: 0.855 | train_acc: 0.918 
+
            +
            +
              [=========================>--------------] 66.50% | train_error: 0.851 | train_acc: 0.918 
+
            +
            +
              [=========================>--------------] 66.60% | train_error: 0.850 | train_acc: 0.918 
+
            +
            +
              [=========================>--------------] 66.70% | train_error: 0.850 | train_acc: 0.918 
+
            +
            +
              [=========================>--------------] 66.80% | train_error: 0.848 | train_acc: 0.918 
+
            +
            +
              [=========================>--------------] 66.90% | train_error: 0.844 | train_acc: 0.919 
+
            +
            +
              [=========================>--------------] 67.00% | train_error: 0.845 | train_acc: 0.918 
+
            +
            +
              [=========================>--------------] 67.10% | train_error: 0.842 | train_acc: 0.919 
+
            +
            +
              [=========================>--------------] 67.20% | train_error: 0.841 | train_acc: 0.919 
+
            +
            +
              [=========================>--------------] 67.30% | train_error: 0.841 | train_acc: 0.919 
+
            +
            +
              [=========================>--------------] 67.40% | train_error: 0.836 | train_acc: 0.919 
+
            +
            +
              [==========================>-------------] 67.50% | train_error: 0.821 | train_acc: 0.921 
+
            +
            +
              [==========================>-------------] 67.60% | train_error: 0.808 | train_acc: 0.922 
+
            +
            +
              [==========================>-------------] 67.70% | train_error: 0.805 | train_acc: 0.922 
+
            +
            +
              [==========================>-------------] 67.80% | train_error: 0.800 | train_acc: 0.923 
+
            +
            +
              [==========================>-------------] 67.90% | train_error: 0.799 | train_acc: 0.923 
+
            +
            +
              [==========================>-------------] 68.00% | train_error: 0.792 | train_acc: 0.924 
+
            +
            +
              [==========================>-------------] 68.10% | train_error: 0.791 | train_acc: 0.924 
+
            +
            +
              [==========================>-------------] 68.20% | train_error: 0.782 | train_acc: 0.925 
+
            +
            +
              [==========================>-------------] 68.30% | train_error: 0.774 | train_acc: 0.925 
+
            +
            +
              [==========================>-------------] 68.40% | train_error: 0.766 | train_acc: 0.926 
+
            +
            +
              [==========================>-------------] 68.50% | train_error: 0.763 | train_acc: 0.926 
+
            +
            +
              [==========================>-------------] 68.60% | train_error: 0.757 | train_acc: 0.927 
+
            +
            +
              [==========================>-------------] 68.70% | train_error: 0.753 | train_acc: 0.927 
+
            +
            +
              [==========================>-------------] 68.80% | train_error: 0.754 | train_acc: 0.927 
+
            +
            +
              [==========================>-------------] 68.90% | train_error: 0.747 | train_acc: 0.928 
+
            +
            +
              [==========================>-------------] 69.00% | train_error: 0.740 | train_acc: 0.929 
+
            +
            +
              [==========================>-------------] 69.10% | train_error: 0.746 | train_acc: 0.928 
+
            +
            +
              [==========================>-------------] 69.20% | train_error: 0.737 | train_acc: 0.929 
+
            +
            +
              [==========================>-------------] 69.30% | train_error: 0.744 | train_acc: 0.928 
+
            +
            +
              [==========================>-------------] 69.40% | train_error: 0.736 | train_acc: 0.929 
+
            +
            +
              [==========================>-------------] 69.50% | train_error: 0.745 | train_acc: 0.928 
+
            +
            +
              [==========================>-------------] 69.60% | train_error: 0.737 | train_acc: 0.929 
+
            +
            +
              [==========================>-------------] 69.70% | train_error: 0.736 | train_acc: 0.929 
+
            +
            +
              [==========================>-------------] 69.80% | train_error: 0.724 | train_acc: 0.930 
+
            +
            +
              [==========================>-------------] 69.90% | train_error: 0.722 | train_acc: 0.930 
+
            +
            +
              [===========================>------------] 70.00% | train_error: 0.718 | train_acc: 0.931 
+
            +
            +
              [===========================>------------] 70.10% | train_error: 0.718 | train_acc: 0.931 
+
            +
            +
              [===========================>------------] 70.20% | train_error: 0.717 | train_acc: 0.931 
+
            +
            +
              [===========================>------------] 70.30% | train_error: 0.712 | train_acc: 0.931 
+
            +
            +
              [===========================>------------] 70.40% | train_error: 0.713 | train_acc: 0.931 
+
            +
            +
              [===========================>------------] 70.50% | train_error: 0.710 | train_acc: 0.931 
+
            +
            +
              [===========================>------------] 70.60% | train_error: 0.708 | train_acc: 0.932 
+
            +
            +
              [===========================>------------] 70.70% | train_error: 0.705 | train_acc: 0.932 
+
            +
            +
              [===========================>------------] 70.80% | train_error: 0.702 | train_acc: 0.932 
+
            +
            +
              [===========================>------------] 70.90% | train_error: 0.701 | train_acc: 0.932 
+
            +
            +
              [===========================>------------] 71.00% | train_error: 0.695 | train_acc: 0.933 
+
            +
            +
              [===========================>------------] 71.10% | train_error: 0.694 | train_acc: 0.933 
+
            +
            +
              [===========================>------------] 71.20% | train_error: 0.691 | train_acc: 0.933 
+
            +
            +
              [===========================>------------] 71.30% | train_error: 0.687 | train_acc: 0.934 
+
            +
            +
              [===========================>------------] 71.40% | train_error: 0.683 | train_acc: 0.934 
+
            +
            +
              [===========================>------------] 71.50% | train_error: 0.682 | train_acc: 0.934 
+
            +
            +
              [===========================>------------] 71.60% | train_error: 0.677 | train_acc: 0.935 
+
            +
            +
              [===========================>------------] 71.70% | train_error: 0.672 | train_acc: 0.935 
+
            +
            +
              [===========================>------------] 71.80% | train_error: 0.669 | train_acc: 0.935 
+
            +
            +
              [===========================>------------] 71.90% | train_error: 0.669 | train_acc: 0.935 
+
            +
            +
              [===========================>------------] 72.00% | train_error: 0.668 | train_acc: 0.936 
+
            +
            +
              [===========================>------------] 72.10% | train_error: 0.665 | train_acc: 0.936 
+
            +
            +
              [===========================>------------] 72.20% | train_error: 0.658 | train_acc: 0.936 
+
            +
            +
              [===========================>------------] 72.30% | train_error: 0.655 | train_acc: 0.937 
+
            +
            +
              [===========================>------------] 72.40% | train_error: 0.654 | train_acc: 0.937 
+
            +
            +
              [============================>-----------] 72.50% | train_error: 0.657 | train_acc: 0.937 
+
            +
            +
              [============================>-----------] 72.60% | train_error: 0.652 | train_acc: 0.937 
+
            +
            +
              [============================>-----------] 72.70% | train_error: 0.647 | train_acc: 0.938 
+
            +
            +
              [============================>-----------] 72.80% | train_error: 0.645 | train_acc: 0.938 
+
            +
            +
              [============================>-----------] 72.90% | train_error: 0.645 | train_acc: 0.938 
+
            +
            +
              [============================>-----------] 73.00% | train_error: 0.630 | train_acc: 0.939 
+
            +
            +
              [============================>-----------] 73.10% | train_error: 0.637 | train_acc: 0.939 
+
            +
            +
              [============================>-----------] 73.20% | train_error: 0.624 | train_acc: 0.940 
+
            +
            +
              [============================>-----------] 73.30% | train_error: 0.630 | train_acc: 0.939 
+
            +
            +
              [============================>-----------] 73.40% | train_error: 0.618 | train_acc: 0.940 
+
            +
            +
              [============================>-----------] 73.50% | train_error: 0.623 | train_acc: 0.940 
+
            +
            +
              [============================>-----------] 73.60% | train_error: 0.611 | train_acc: 0.941 
+
            +
            +
              [============================>-----------] 73.70% | train_error: 0.612 | train_acc: 0.941 
+
            +
            +
              [============================>-----------] 73.80% | train_error: 0.614 | train_acc: 0.941 
+
            +
            +
              [============================>-----------] 73.90% | train_error: 0.608 | train_acc: 0.941 
+
            +
            +
              [============================>-----------] 74.00% | train_error: 0.605 | train_acc: 0.942 
+
            +
            +
              [============================>-----------] 74.10% | train_error: 0.609 | train_acc: 0.941 
+
            +
            +
              [============================>-----------] 74.20% | train_error: 0.604 | train_acc: 0.942 
+
            +
            +
              [============================>-----------] 74.30% | train_error: 0.601 | train_acc: 0.942 
+
            +
            +
              [============================>-----------] 74.40% | train_error: 0.600 | train_acc: 0.942 
+
            +
            +
              [============================>-----------] 74.50% | train_error: 0.601 | train_acc: 0.942 
+
            +
            +
              [============================>-----------] 74.60% | train_error: 0.599 | train_acc: 0.942 
+
            +
            +
              [============================>-----------] 74.70% | train_error: 0.593 | train_acc: 0.943 
+
            +
            +
              [============================>-----------] 74.80% | train_error: 0.592 | train_acc: 0.943 
+
            +
            +
              [============================>-----------] 74.90% | train_error: 0.593 | train_acc: 0.943 
+
            +
            +
              [=============================>----------] 75.00% | train_error: 0.589 | train_acc: 0.943 
+
            +
            +
              [=============================>----------] 75.10% | train_error: 0.590 | train_acc: 0.943 
+
            +
            +
              [=============================>----------] 75.20% | train_error: 0.585 | train_acc: 0.944 
+
            +
            +
              [=============================>----------] 75.30% | train_error: 0.590 | train_acc: 0.943 
+
            +
            +
              [=============================>----------] 75.40% | train_error: 0.580 | train_acc: 0.944 
+
            +
            +
              [=============================>----------] 75.50% | train_error: 0.579 | train_acc: 0.944 
+
            +
            +
              [=============================>----------] 75.60% | train_error: 0.574 | train_acc: 0.945 
+
            +
            +
              [=============================>----------] 75.70% | train_error: 0.578 | train_acc: 0.944 
+
            +
            +
              [=============================>----------] 75.80% | train_error: 0.560 | train_acc: 0.946 
+
            +
            +
              [=============================>----------] 75.90% | train_error: 0.566 | train_acc: 0.945 
+
            +
            +
              [=============================>----------] 76.00% | train_error: 0.564 | train_acc: 0.946 
+
            +
            +
              [=============================>----------] 76.10% | train_error: 0.563 | train_acc: 0.946 
+
            +
            +
              [=============================>----------] 76.20% | train_error: 0.559 | train_acc: 0.946 
+
            +
            +
              [=============================>----------] 76.30% | train_error: 0.560 | train_acc: 0.946 
+
            +
            +
              [=============================>----------] 76.40% | train_error: 0.549 | train_acc: 0.947 
+
            +
            +
              [=============================>----------] 76.50% | train_error: 0.563 | train_acc: 0.946 
+
            +
            +
              [=============================>----------] 76.60% | train_error: 0.542 | train_acc: 0.948 
+
            +
            +
              [=============================>----------] 76.70% | train_error: 0.558 | train_acc: 0.946 
+
            +
            +
              [=============================>----------] 76.80% | train_error: 0.542 | train_acc: 0.948 
+
            +
            +
              [=============================>----------] 76.90% | train_error: 0.556 | train_acc: 0.946 
+
            +
            +
              [=============================>----------] 77.00% | train_error: 0.536 | train_acc: 0.948 
+
            +
            +
              [=============================>----------] 77.10% | train_error: 0.549 | train_acc: 0.947 
+
            +
            +
              [=============================>----------] 77.20% | train_error: 0.529 | train_acc: 0.949 
+
            +
            +
              [=============================>----------] 77.30% | train_error: 0.536 | train_acc: 0.948 
+
            +
            +
              [=============================>----------] 77.40% | train_error: 0.534 | train_acc: 0.949 
+
            +
            +
              [==============================>---------] 77.50% | train_error: 0.532 | train_acc: 0.949 
+
            +
            +
              [==============================>---------] 77.60% | train_error: 0.528 | train_acc: 0.949 
+
            +
            +
              [==============================>---------] 77.70% | train_error: 0.526 | train_acc: 0.949 
+
            +
            +
              [==============================>---------] 77.80% | train_error: 0.515 | train_acc: 0.950 
+
            +
            +
              [==============================>---------] 77.90% | train_error: 0.517 | train_acc: 0.950 
+
            +
            +
              [==============================>---------] 78.00% | train_error: 0.510 | train_acc: 0.951 
+
            +
            +
              [==============================>---------] 78.10% | train_error: 0.511 | train_acc: 0.951 
+
            +
            +
              [==============================>---------] 78.20% | train_error: 0.510 | train_acc: 0.951 
+
            +
            +
              [==============================>---------] 78.30% | train_error: 0.504 | train_acc: 0.951 
+
            +
            +
              [==============================>---------] 78.40% | train_error: 0.510 | train_acc: 0.951 
+
            +
            +
              [==============================>---------] 78.50% | train_error: 0.507 | train_acc: 0.951 
+
            +
            +
              [==============================>---------] 78.60% | train_error: 0.509 | train_acc: 0.951 
+
            +
            +
              [==============================>---------] 78.70% | train_error: 0.499 | train_acc: 0.952 
+
            +
            +
              [==============================>---------] 78.80% | train_error: 0.506 | train_acc: 0.951 
+
            +
            +
              [==============================>---------] 78.90% | train_error: 0.499 | train_acc: 0.952 
+
            +
            +
              [==============================>---------] 79.00% | train_error: 0.504 | train_acc: 0.951 
+
            +
            +
              [==============================>---------] 79.10% | train_error: 0.503 | train_acc: 0.952 
+
            +
            +
              [==============================>---------] 79.20% | train_error: 0.495 | train_acc: 0.952 
+
            +
            +
              [==============================>---------] 79.30% | train_error: 0.495 | train_acc: 0.952 
+
            +
            +
              [==============================>---------] 79.40% | train_error: 0.488 | train_acc: 0.953 
+
            +
            +
              [==============================>---------] 79.50% | train_error: 0.482 | train_acc: 0.953 
+
            +
            +
              [==============================>---------] 79.60% | train_error: 0.473 | train_acc: 0.954 
+
            +
            +
              [==============================>---------] 79.70% | train_error: 0.476 | train_acc: 0.954 
+
            +
            +
              [==============================>---------] 79.80% | train_error: 0.477 | train_acc: 0.954 
+
            +
            +
              [==============================>---------] 79.90% | train_error: 0.468 | train_acc: 0.955 
+
            +
            +
              [===============================>--------] 80.00% | train_error: 0.472 | train_acc: 0.954 
+
            +
            +
              [===============================>--------] 80.10% | train_error: 0.466 | train_acc: 0.955 
+
            +
            +
              [===============================>--------] 80.20% | train_error: 0.473 | train_acc: 0.954 
+
            +
            +
              [===============================>--------] 80.30% | train_error: 0.464 | train_acc: 0.955 
+
            +
            +
              [===============================>--------] 80.40% | train_error: 0.467 | train_acc: 0.955 
+
            +
            +
              [===============================>--------] 80.50% | train_error: 0.458 | train_acc: 0.956 
+
            +
            +
              [===============================>--------] 80.60% | train_error: 0.461 | train_acc: 0.955 
+
            +
            +
              [===============================>--------] 80.70% | train_error: 0.449 | train_acc: 0.957 
+
            +
            +
              [===============================>--------] 80.80% | train_error: 0.464 | train_acc: 0.955 
+
            +
            +
              [===============================>--------] 80.90% | train_error: 0.446 | train_acc: 0.957 
+
            +
            +
              [===============================>--------] 81.00% | train_error: 0.456 | train_acc: 0.956 
+
            +
            +
              [===============================>--------] 81.10% | train_error: 0.449 | train_acc: 0.957 
+
            +
            +
              [===============================>--------] 81.20% | train_error: 0.454 | train_acc: 0.956 
+
            +
            +
              [===============================>--------] 81.30% | train_error: 0.446 | train_acc: 0.957 
+
            +
            +
              [===============================>--------] 81.40% | train_error: 0.443 | train_acc: 0.957 
+
            +
            +
              [===============================>--------] 81.50% | train_error: 0.443 | train_acc: 0.957 
+
            +
            +
              [===============================>--------] 81.60% | train_error: 0.429 | train_acc: 0.959 
+
            +
            +
              [===============================>--------] 81.70% | train_error: 0.434 | train_acc: 0.958 
+
            +
            +
              [===============================>--------] 81.80% | train_error: 0.427 | train_acc: 0.959 
+
            +
            +
              [===============================>--------] 81.90% | train_error: 0.422 | train_acc: 0.959 
+
            +
            +
              [===============================>--------] 82.00% | train_error: 0.419 | train_acc: 0.960 
+
            +
            +
              [===============================>--------] 82.10% | train_error: 0.424 | train_acc: 0.959 
+
            +
            +
              [===============================>--------] 82.20% | train_error: 0.424 | train_acc: 0.959 
+
            +
            +
              [===============================>--------] 82.30% | train_error: 0.422 | train_acc: 0.959 
+
            +
            +
              [===============================>--------] 82.40% | train_error: 0.417 | train_acc: 0.960 
+
            +
            +
              [================================>-------] 82.50% | train_error: 0.413 | train_acc: 0.960 
+
            +
            +
              [================================>-------] 82.60% | train_error: 0.408 | train_acc: 0.961 
+
            +
            +
              [================================>-------] 82.70% | train_error: 0.401 | train_acc: 0.961 
+
            +
            +
              [================================>-------] 82.80% | train_error: 0.402 | train_acc: 0.961 
+
            +
            +
              [================================>-------] 82.90% | train_error: 0.396 | train_acc: 0.962 
+
            +
            +
              [================================>-------] 83.00% | train_error: 0.402 | train_acc: 0.961 
+
            +
            +
              [================================>-------] 83.10% | train_error: 0.399 | train_acc: 0.962 
+
            +
            +
              [================================>-------] 83.20% | train_error: 0.401 | train_acc: 0.961 
+
            +
            +
              [================================>-------] 83.30% | train_error: 0.389 | train_acc: 0.962 
+
            +
            +
              [================================>-------] 83.40% | train_error: 0.397 | train_acc: 0.962 
+
            +
            +
              [================================>-------] 83.50% | train_error: 0.386 | train_acc: 0.963 
+
            +
            +
              [================================>-------] 83.60% | train_error: 0.389 | train_acc: 0.963 
+
            +
            +
              [================================>-------] 83.70% | train_error: 0.386 | train_acc: 0.963 
+
            +
            +
              [================================>-------] 83.80% | train_error: 0.385 | train_acc: 0.963 
+
            +
            +
              [================================>-------] 83.90% | train_error: 0.385 | train_acc: 0.963 
+
            +
            +
              [================================>-------] 84.00% | train_error: 0.382 | train_acc: 0.963 
+
            +
            +
              [================================>-------] 84.10% | train_error: 0.378 | train_acc: 0.963 
+
            +
            +
              [================================>-------] 84.20% | train_error: 0.374 | train_acc: 0.964 
+
            +
            +
              [================================>-------] 84.30% | train_error: 0.372 | train_acc: 0.964 
+
            +
            +
              [================================>-------] 84.40% | train_error: 0.371 | train_acc: 0.964 
+
            +
            +
              [================================>-------] 84.50% | train_error: 0.369 | train_acc: 0.964 
+
            +
            +
              [================================>-------] 84.60% | train_error: 0.364 | train_acc: 0.965 
+
            +
            +
              [================================>-------] 84.70% | train_error: 0.372 | train_acc: 0.964 
+
            +
            +
              [================================>-------] 84.80% | train_error: 0.368 | train_acc: 0.964 
+
            +
            +
              [================================>-------] 84.90% | train_error: 0.362 | train_acc: 0.965 
+
            +
            +
              [=================================>------] 85.00% | train_error: 0.364 | train_acc: 0.965 
+
            +
            +
              [=================================>------] 85.10% | train_error: 0.355 | train_acc: 0.966 
+
            +
            +
              [=================================>------] 85.20% | train_error: 0.356 | train_acc: 0.966 
+
            +
            +
              [=================================>------] 85.30% | train_error: 0.349 | train_acc: 0.966 
+
            +
            +
              [=================================>------] 85.40% | train_error: 0.341 | train_acc: 0.967 
+
            +
            +
              [=================================>------] 85.50% | train_error: 0.347 | train_acc: 0.966 
+
            +
            +
              [=================================>------] 85.60% | train_error: 0.349 | train_acc: 0.966 
+
            +
            +
              [=================================>------] 85.70% | train_error: 0.345 | train_acc: 0.967 
+
            +
            +
              [=================================>------] 85.80% | train_error: 0.346 | train_acc: 0.967 
+
            +
            +
              [=================================>------] 85.90% | train_error: 0.338 | train_acc: 0.968 
+
            +
            +
              [=================================>------] 86.00% | train_error: 0.348 | train_acc: 0.966 
+
            +
            +
              [=================================>------] 86.10% | train_error: 0.344 | train_acc: 0.967 
+
            +
            +
              [=================================>------] 86.20% | train_error: 0.346 | train_acc: 0.967 
+
            +
            +
              [=================================>------] 86.30% | train_error: 0.340 | train_acc: 0.967 
+
            +
            +
              [=================================>------] 86.40% | train_error: 0.339 | train_acc: 0.967 
+
            +
            +
              [=================================>------] 86.50% | train_error: 0.336 | train_acc: 0.968 
+
            +
            +
              [=================================>------] 86.60% | train_error: 0.343 | train_acc: 0.967 
+
            +
            +
              [=================================>------] 86.70% | train_error: 0.336 | train_acc: 0.968 
+
            +
            +
              [=================================>------] 86.80% | train_error: 0.339 | train_acc: 0.967 
+
            +
            +
              [=================================>------] 86.90% | train_error: 0.330 | train_acc: 0.968 
+
            +
            +
              [=================================>------] 87.00% | train_error: 0.338 | train_acc: 0.967 
+
            +
            +
              [=================================>------] 87.10% | train_error: 0.328 | train_acc: 0.969 
+
            +
            +
              [=================================>------] 87.20% | train_error: 0.326 | train_acc: 0.969 
+
            +
            +
              [=================================>------] 87.30% | train_error: 0.317 | train_acc: 0.969 
+
            +
            +
              [=================================>------] 87.40% | train_error: 0.329 | train_acc: 0.968 
+
            +
            +
              [==================================>-----] 87.50% | train_error: 0.317 | train_acc: 0.969 
+
            +
            +
              [==================================>-----] 87.60% | train_error: 0.316 | train_acc: 0.970 
+
            +
            +
              [==================================>-----] 87.70% | train_error: 0.318 | train_acc: 0.969 
+
            +
            +
              [==================================>-----] 87.80% | train_error: 0.315 | train_acc: 0.970 
+
            +
            +
              [==================================>-----] 87.90% | train_error: 0.309 | train_acc: 0.970 
+
            +
            +
              [==================================>-----] 88.00% | train_error: 0.308 | train_acc: 0.970 
+
            +
            +
              [==================================>-----] 88.10% | train_error: 0.296 | train_acc: 0.972 
+
            +
            +
              [==================================>-----] 88.20% | train_error: 0.302 | train_acc: 0.971 
+
            +
            +
              [==================================>-----] 88.30% | train_error: 0.298 | train_acc: 0.971 
+
            +
            +
              [==================================>-----] 88.40% | train_error: 0.300 | train_acc: 0.971 
+
            +
            +
              [==================================>-----] 88.50% | train_error: 0.296 | train_acc: 0.971 
+
            +
            +
              [==================================>-----] 88.60% | train_error: 0.291 | train_acc: 0.972 
+
            +
            +
              [==================================>-----] 88.70% | train_error: 0.287 | train_acc: 0.972 
+
            +
            +
              [==================================>-----] 88.80% | train_error: 0.283 | train_acc: 0.973 
+
            +
            +
              [==================================>-----] 88.90% | train_error: 0.280 | train_acc: 0.973 
+
            +
            +
              [==================================>-----] 89.00% | train_error: 0.285 | train_acc: 0.973 
+
            +
            +
              [==================================>-----] 89.10% | train_error: 0.277 | train_acc: 0.973 
+
            +
            +
              [==================================>-----] 89.20% | train_error: 0.292 | train_acc: 0.972 
+
            +
            +
              [==================================>-----] 89.30% | train_error: 0.289 | train_acc: 0.972 
+
            +
            +
              [==================================>-----] 89.40% | train_error: 0.292 | train_acc: 0.972 
+
            +
            +
              [==================================>-----] 89.50% | train_error: 0.287 | train_acc: 0.972 
+
            +
            +
              [==================================>-----] 89.60% | train_error: 0.285 | train_acc: 0.973 
+
            +
            +
              [==================================>-----] 89.70% | train_error: 0.280 | train_acc: 0.973 
+
            +
            +
              [==================================>-----] 89.80% | train_error: 0.283 | train_acc: 0.973 
+
            +
            +
              [==================================>-----] 89.90% | train_error: 0.277 | train_acc: 0.973 
+
            +
            +
              [===================================>----] 90.00% | train_error: 0.285 | train_acc: 0.973 
+
            +
            +
              [===================================>----] 90.10% | train_error: 0.278 | train_acc: 0.973 
+
            +
            +
              [===================================>----] 90.20% | train_error: 0.266 | train_acc: 0.974 
+
            +
            +
              [===================================>----] 90.30% | train_error: 0.264 | train_acc: 0.975 
+
            +
            +
              [===================================>----] 90.40% | train_error: 0.271 | train_acc: 0.974 
+
            +
            +
              [===================================>----] 90.50% | train_error: 0.265 | train_acc: 0.974 
+
            +
            +
              [===================================>----] 90.60% | train_error: 0.265 | train_acc: 0.974 
+
            +
            +
              [===================================>----] 90.70% | train_error: 0.258 | train_acc: 0.975 
+
            +
            +
              [===================================>----] 90.80% | train_error: 0.263 | train_acc: 0.975 
+
            +
            +
              [===================================>----] 90.90% | train_error: 0.251 | train_acc: 0.976 
+
            +
            +
              [===================================>----] 91.00% | train_error: 0.248 | train_acc: 0.976 
+
            +
            +
              [===================================>----] 91.10% | train_error: 0.248 | train_acc: 0.976 
+
            +
            +
              [===================================>----] 91.20% | train_error: 0.250 | train_acc: 0.976 
+
            +
            +
              [===================================>----] 91.30% | train_error: 0.243 | train_acc: 0.977 
+
            +
            +
              [===================================>----] 91.40% | train_error: 0.241 | train_acc: 0.977 
+
            +
            +
              [===================================>----] 91.50% | train_error: 0.239 | train_acc: 0.977 
+
            +
            +
              [===================================>----] 91.60% | train_error: 0.240 | train_acc: 0.977 
+
            +
            +
              [===================================>----] 91.70% | train_error: 0.239 | train_acc: 0.977 
+
            +
            +
              [===================================>----] 91.80% | train_error: 0.239 | train_acc: 0.977 
+
            +
            +
              [===================================>----] 91.90% | train_error: 0.235 | train_acc: 0.977 
+
            +
            +
              [===================================>----] 92.00% | train_error: 0.233 | train_acc: 0.978 
+
            +
            +
              [===================================>----] 92.10% | train_error: 0.234 | train_acc: 0.977 
+
            +
            +
              [===================================>----] 92.20% | train_error: 0.240 | train_acc: 0.977 
+
            +
            +
              [===================================>----] 92.30% | train_error: 0.238 | train_acc: 0.977 
+
            +
            +
              [===================================>----] 92.40% | train_error: 0.226 | train_acc: 0.978 
+
            +
            +
              [====================================>---] 92.50% | train_error: 0.226 | train_acc: 0.978 
+
            +
            +
              [====================================>---] 92.60% | train_error: 0.229 | train_acc: 0.978 
+
            +
            +
              [====================================>---] 92.70% | train_error: 0.226 | train_acc: 0.978 
+
            +
            +
              [====================================>---] 92.80% | train_error: 0.218 | train_acc: 0.979 
+
            +
            +
              [====================================>---] 92.90% | train_error: 0.219 | train_acc: 0.979 
+
            +
            +
              [====================================>---] 93.00% | train_error: 0.220 | train_acc: 0.979 
+
            +
            +
              [====================================>---] 93.10% | train_error: 0.216 | train_acc: 0.979 
+
            +
            +
              [====================================>---] 93.20% | train_error: 0.217 | train_acc: 0.979 
+
            +
            +
              [====================================>---] 93.30% | train_error: 0.216 | train_acc: 0.979 
+
            +
            +
              [====================================>---] 93.40% | train_error: 0.216 | train_acc: 0.979 
+
            +
            +
              [====================================>---] 93.50% | train_error: 0.213 | train_acc: 0.979 
+
            +
            +
              [====================================>---] 93.60% | train_error: 0.213 | train_acc: 0.979 
+
            +
            +
              [====================================>---] 93.70% | train_error: 0.213 | train_acc: 0.979 
+
            +
            +
              [====================================>---] 93.80% | train_error: 0.205 | train_acc: 0.980 
+
            +
            +
              [====================================>---] 93.90% | train_error: 0.210 | train_acc: 0.980 
+
            +
            +
              [====================================>---] 94.00% | train_error: 0.211 | train_acc: 0.980 
+
            +
            +
              [====================================>---] 94.10% | train_error: 0.209 | train_acc: 0.980 
+
            +
            +
              [====================================>---] 94.20% | train_error: 0.206 | train_acc: 0.980 
+
            +
            +
              [====================================>---] 94.30% | train_error: 0.204 | train_acc: 0.980 
+
            +
            +
              [====================================>---] 94.40% | train_error: 0.210 | train_acc: 0.980 
+
            +
            +
              [====================================>---] 94.50% | train_error: 0.198 | train_acc: 0.981 
+
            +
            +
              [====================================>---] 94.60% | train_error: 0.198 | train_acc: 0.981 
+
            +
            +
              [====================================>---] 94.70% | train_error: 0.202 | train_acc: 0.981 
+
            +
            +
              [====================================>---] 94.80% | train_error: 0.201 | train_acc: 0.981 
+
            +
            +
              [====================================>---] 94.90% | train_error: 0.202 | train_acc: 0.981 
+
            +
            +
              [=====================================>--] 95.00% | train_error: 0.195 | train_acc: 0.981 
+
            +
            +
              [=====================================>--] 95.10% | train_error: 0.203 | train_acc: 0.980 
+
            +
            +
              [=====================================>--] 95.20% | train_error: 0.197 | train_acc: 0.981 
+
            +
            +
              [=====================================>--] 95.30% | train_error: 0.201 | train_acc: 0.981 
+
            +
            +
              [=====================================>--] 95.40% | train_error: 0.187 | train_acc: 0.982 
+
            +
            +
              [=====================================>--] 95.50% | train_error: 0.197 | train_acc: 0.981 
+
            +
            +
              [=====================================>--] 95.60% | train_error: 0.187 | train_acc: 0.982 
+
            +
            +
              [=====================================>--] 95.70% | train_error: 0.195 | train_acc: 0.981 
+
            +
            +
              [=====================================>--] 95.80% | train_error: 0.195 | train_acc: 0.981 
+
            +
            +
              [=====================================>--] 95.90% | train_error: 0.190 | train_acc: 0.982 
+
            +
            +
              [=====================================>--] 96.00% | train_error: 0.190 | train_acc: 0.982 
+
            +
            +
              [=====================================>--] 96.10% | train_error: 0.194 | train_acc: 0.981 
+
            +
            +
              [=====================================>--] 96.20% | train_error: 0.187 | train_acc: 0.982 
+
            +
            +
              [=====================================>--] 96.30% | train_error: 0.190 | train_acc: 0.982 
+
            +
            +
              [=====================================>--] 96.40% | train_error: 0.190 | train_acc: 0.982 
+
            +
            +
              [=====================================>--] 96.50% | train_error: 0.195 | train_acc: 0.981 
+
            +
            +
              [=====================================>--] 96.60% | train_error: 0.188 | train_acc: 0.982 
+
            +
            +
              [=====================================>--] 96.70% | train_error: 0.188 | train_acc: 0.982 
+
            +
            +
              [=====================================>--] 96.80% | train_error: 0.190 | train_acc: 0.982 
+
            +
            +
              [=====================================>--] 96.90% | train_error: 0.191 | train_acc: 0.982 
+
            +
            +
              [=====================================>--] 97.00% | train_error: 0.188 | train_acc: 0.982 
+
            +
            +
              [=====================================>--] 97.10% | train_error: 0.189 | train_acc: 0.982 
+
            +
            +
              [=====================================>--] 97.20% | train_error: 0.175 | train_acc: 0.983 
+
            +
            +
              [=====================================>--] 97.30% | train_error: 0.189 | train_acc: 0.982 
+
            +
            +
              [=====================================>--] 97.40% | train_error: 0.183 | train_acc: 0.982 
+
            +
            +
              [======================================>-] 97.50% | train_error: 0.189 | train_acc: 0.982 
+
            +
            +
              [======================================>-] 97.60% | train_error: 0.181 | train_acc: 0.983 
+
            +
            +
              [======================================>-] 97.70% | train_error: 0.188 | train_acc: 0.982 
+
            +
            +
              [======================================>-] 97.80% | train_error: 0.179 | train_acc: 0.983 
+
            +
            +
              [======================================>-] 97.90% | train_error: 0.190 | train_acc: 0.982 
+
            +
            +
              [======================================>-] 98.00% | train_error: 0.186 | train_acc: 0.982 
+
            +
            +
              [======================================>-] 98.10% | train_error: 0.185 | train_acc: 0.982 
+
            +
            +
              [======================================>-] 98.20% | train_error: 0.182 | train_acc: 0.982 
+
            +
            +
              [======================================>-] 98.30% | train_error: 0.182 | train_acc: 0.982 
+
            +
            +
              [======================================>-] 98.40% | train_error: 0.171 | train_acc: 0.984 
+
            +
            +
              [======================================>-] 98.50% | train_error: 0.178 | train_acc: 0.983 
+
            +
            +
              [======================================>-] 98.60% | train_error: 0.170 | train_acc: 0.984 
+
            +
            +
              [======================================>-] 98.70% | train_error: 0.181 | train_acc: 0.983 
+
            +
            +
              [======================================>-] 98.80% | train_error: 0.166 | train_acc: 0.984 
+
            +
            +
              [======================================>-] 98.90% | train_error: 0.175 | train_acc: 0.983 
+
            +
            +
              [======================================>-] 99.00% | train_error: 0.173 | train_acc: 0.983 
+
            +
            +
              [======================================>-] 99.10% | train_error: 0.173 | train_acc: 0.983 
+
            +
            +
              [======================================>-] 99.20% | train_error: 0.171 | train_acc: 0.984 
+
            +
            +
              [======================================>-] 99.30% | train_error: 0.168 | train_acc: 0.984 
+
            +
            +
              [======================================>-] 99.40% | train_error: 0.167 | train_acc: 0.984 
+
            +
            +
              [======================================>-] 99.50% | train_error: 0.174 | train_acc: 0.983 
+
            +
            +
              [======================================>-] 99.60% | train_error: 0.158 | train_acc: 0.985 
+
            +
            +
              [======================================>-] 99.70% | train_error: 0.173 | train_acc: 0.983 
+
            +
            +
              [======================================>-] 99.80% | train_error: 0.164 | train_acc: 0.984 
+
            +
            +
              [======================================>-] 99.90% | train_error: 0.175 | train_acc: 0.983 
+
            +
            +
                                                                                                        
+
            +
            +
              [=======================================>] 100.0% | train_error: 0.175 | train_acc: 0.983 
+
            +
            +
            @@ -10720,6 +15481,3016 @@ digits between the range of 0 to 9.

            +
            +
            Adam: Eta=0.1, Lambda=0
+
+  [----------------------------------------] 0.000% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [----------------------------------------] 0.1000% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [----------------------------------------] 0.2000% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [----------------------------------------] 0.3000% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [----------------------------------------] 0.4000% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [----------------------------------------] 0.5000% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [----------------------------------------] 0.6000% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [----------------------------------------] 0.7000% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [----------------------------------------] 0.8000% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [----------------------------------------] 0.9000% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [----------------------------------------] 1.000% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [----------------------------------------] 1.100% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [----------------------------------------] 1.200% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [----------------------------------------] 1.300% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [----------------------------------------] 1.400% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [----------------------------------------] 1.500% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [----------------------------------------] 1.600% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [----------------------------------------] 1.700% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [----------------------------------------] 1.800% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [----------------------------------------] 1.900% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [----------------------------------------] 2.000% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [----------------------------------------] 2.100% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [----------------------------------------] 2.200% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [----------------------------------------] 2.300% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [----------------------------------------] 2.400% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [>---------------------------------------] 2.500% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [>---------------------------------------] 2.600% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [>---------------------------------------] 2.700% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [>---------------------------------------] 2.800% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [>---------------------------------------] 2.900% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [>---------------------------------------] 3.000% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [>---------------------------------------] 3.100% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [>---------------------------------------] 3.200% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [>---------------------------------------] 3.300% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [>---------------------------------------] 3.400% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [>---------------------------------------] 3.500% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [>---------------------------------------] 3.600% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [>---------------------------------------] 3.700% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [>---------------------------------------] 3.800% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [>---------------------------------------] 3.900% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [>---------------------------------------] 4.000% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [>---------------------------------------] 4.100% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [>---------------------------------------] 4.200% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [>---------------------------------------] 4.300% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [>---------------------------------------] 4.400% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [>---------------------------------------] 4.500% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [>---------------------------------------] 4.600% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [>---------------------------------------] 4.700% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [>---------------------------------------] 4.800% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [>---------------------------------------] 4.900% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=>--------------------------------------] 5.000% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=>--------------------------------------] 5.100% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=>--------------------------------------] 5.200% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=>--------------------------------------] 5.300% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=>--------------------------------------] 5.400% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=>--------------------------------------] 5.500% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=>--------------------------------------] 5.600% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=>--------------------------------------] 5.700% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=>--------------------------------------] 5.800% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=>--------------------------------------] 5.900% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=>--------------------------------------] 6.000% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=>--------------------------------------] 6.100% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=>--------------------------------------] 6.200% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=>--------------------------------------] 6.300% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=>--------------------------------------] 6.400% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=>--------------------------------------] 6.500% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=>--------------------------------------] 6.600% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=>--------------------------------------] 6.700% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=>--------------------------------------] 6.800% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=>--------------------------------------] 6.900% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=>--------------------------------------] 7.000% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=>--------------------------------------] 7.100% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=>--------------------------------------] 7.200% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=>--------------------------------------] 7.300% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=>--------------------------------------] 7.400% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==>-------------------------------------] 7.500% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==>-------------------------------------] 7.600% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==>-------------------------------------] 7.700% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==>-------------------------------------] 7.800% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==>-------------------------------------] 7.900% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==>-------------------------------------] 8.000% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==>-------------------------------------] 8.100% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==>-------------------------------------] 8.200% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==>-------------------------------------] 8.300% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==>-------------------------------------] 8.400% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==>-------------------------------------] 8.500% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==>-------------------------------------] 8.600% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==>-------------------------------------] 8.700% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==>-------------------------------------] 8.800% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==>-------------------------------------] 8.900% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==>-------------------------------------] 9.000% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==>-------------------------------------] 9.100% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==>-------------------------------------] 9.200% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==>-------------------------------------] 9.300% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==>-------------------------------------] 9.400% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==>-------------------------------------] 9.500% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==>-------------------------------------] 9.600% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==>-------------------------------------] 9.700% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==>-------------------------------------] 9.800% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==>-------------------------------------] 9.900% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===>------------------------------------] 10.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===>------------------------------------] 10.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===>------------------------------------] 10.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===>------------------------------------] 10.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===>------------------------------------] 10.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===>------------------------------------] 10.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===>------------------------------------] 10.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===>------------------------------------] 10.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===>------------------------------------] 10.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===>------------------------------------] 10.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===>------------------------------------] 11.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===>------------------------------------] 11.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===>------------------------------------] 11.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===>------------------------------------] 11.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===>------------------------------------] 11.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===>------------------------------------] 11.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===>------------------------------------] 11.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===>------------------------------------] 11.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===>------------------------------------] 11.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===>------------------------------------] 11.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===>------------------------------------] 12.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===>------------------------------------] 12.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===>------------------------------------] 12.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===>------------------------------------] 12.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===>------------------------------------] 12.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====>-----------------------------------] 12.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====>-----------------------------------] 12.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====>-----------------------------------] 12.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====>-----------------------------------] 12.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====>-----------------------------------] 12.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====>-----------------------------------] 13.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====>-----------------------------------] 13.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====>-----------------------------------] 13.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====>-----------------------------------] 13.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====>-----------------------------------] 13.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====>-----------------------------------] 13.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====>-----------------------------------] 13.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====>-----------------------------------] 13.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====>-----------------------------------] 13.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====>-----------------------------------] 13.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====>-----------------------------------] 14.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====>-----------------------------------] 14.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====>-----------------------------------] 14.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====>-----------------------------------] 14.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====>-----------------------------------] 14.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====>-----------------------------------] 14.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====>-----------------------------------] 14.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====>-----------------------------------] 14.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====>-----------------------------------] 14.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====>-----------------------------------] 14.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=====>----------------------------------] 15.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=====>----------------------------------] 15.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=====>----------------------------------] 15.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=====>----------------------------------] 15.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=====>----------------------------------] 15.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=====>----------------------------------] 15.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=====>----------------------------------] 15.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=====>----------------------------------] 15.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=====>----------------------------------] 15.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=====>----------------------------------] 15.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=====>----------------------------------] 16.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=====>----------------------------------] 16.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=====>----------------------------------] 16.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=====>----------------------------------] 16.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=====>----------------------------------] 16.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=====>----------------------------------] 16.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=====>----------------------------------] 16.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=====>----------------------------------] 16.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=====>----------------------------------] 16.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=====>----------------------------------] 16.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=====>----------------------------------] 17.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=====>----------------------------------] 17.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=====>----------------------------------] 17.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=====>----------------------------------] 17.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=====>----------------------------------] 17.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======>---------------------------------] 17.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======>---------------------------------] 17.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======>---------------------------------] 17.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======>---------------------------------] 17.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======>---------------------------------] 17.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======>---------------------------------] 18.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======>---------------------------------] 18.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======>---------------------------------] 18.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======>---------------------------------] 18.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======>---------------------------------] 18.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======>---------------------------------] 18.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======>---------------------------------] 18.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======>---------------------------------] 18.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======>---------------------------------] 18.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======>---------------------------------] 18.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======>---------------------------------] 19.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======>---------------------------------] 19.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======>---------------------------------] 19.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======>---------------------------------] 19.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======>---------------------------------] 19.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======>---------------------------------] 19.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======>---------------------------------] 19.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======>---------------------------------] 19.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======>---------------------------------] 19.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======>---------------------------------] 19.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=======>--------------------------------] 20.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=======>--------------------------------] 20.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=======>--------------------------------] 20.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=======>--------------------------------] 20.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=======>--------------------------------] 20.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=======>--------------------------------] 20.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=======>--------------------------------] 20.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=======>--------------------------------] 20.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=======>--------------------------------] 20.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=======>--------------------------------] 20.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=======>--------------------------------] 21.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=======>--------------------------------] 21.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=======>--------------------------------] 21.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=======>--------------------------------] 21.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=======>--------------------------------] 21.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=======>--------------------------------] 21.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=======>--------------------------------] 21.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=======>--------------------------------] 21.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=======>--------------------------------] 21.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=======>--------------------------------] 21.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=======>--------------------------------] 22.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=======>--------------------------------] 22.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=======>--------------------------------] 22.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=======>--------------------------------] 22.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=======>--------------------------------] 22.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========>-------------------------------] 22.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========>-------------------------------] 22.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========>-------------------------------] 22.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========>-------------------------------] 22.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========>-------------------------------] 22.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========>-------------------------------] 23.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========>-------------------------------] 23.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========>-------------------------------] 23.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========>-------------------------------] 23.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========>-------------------------------] 23.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========>-------------------------------] 23.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========>-------------------------------] 23.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========>-------------------------------] 23.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========>-------------------------------] 23.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========>-------------------------------] 23.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========>-------------------------------] 24.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========>-------------------------------] 24.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========>-------------------------------] 24.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========>-------------------------------] 24.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========>-------------------------------] 24.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========>-------------------------------] 24.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========>-------------------------------] 24.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========>-------------------------------] 24.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========>-------------------------------] 24.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========>-------------------------------] 24.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=========>------------------------------] 25.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=========>------------------------------] 25.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=========>------------------------------] 25.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=========>------------------------------] 25.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=========>------------------------------] 25.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=========>------------------------------] 25.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=========>------------------------------] 25.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=========>------------------------------] 25.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=========>------------------------------] 25.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=========>------------------------------] 25.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=========>------------------------------] 26.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=========>------------------------------] 26.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=========>------------------------------] 26.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=========>------------------------------] 26.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=========>------------------------------] 26.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=========>------------------------------] 26.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=========>------------------------------] 26.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=========>------------------------------] 26.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=========>------------------------------] 26.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=========>------------------------------] 26.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=========>------------------------------] 27.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=========>------------------------------] 27.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=========>------------------------------] 27.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=========>------------------------------] 27.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=========>------------------------------] 27.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========>-----------------------------] 27.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========>-----------------------------] 27.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========>-----------------------------] 27.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========>-----------------------------] 27.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========>-----------------------------] 27.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========>-----------------------------] 28.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========>-----------------------------] 28.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========>-----------------------------] 28.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========>-----------------------------] 28.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========>-----------------------------] 28.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========>-----------------------------] 28.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========>-----------------------------] 28.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========>-----------------------------] 28.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========>-----------------------------] 28.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========>-----------------------------] 28.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========>-----------------------------] 29.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========>-----------------------------] 29.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========>-----------------------------] 29.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========>-----------------------------] 29.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========>-----------------------------] 29.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========>-----------------------------] 29.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========>-----------------------------] 29.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========>-----------------------------] 29.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========>-----------------------------] 29.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========>-----------------------------] 29.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===========>----------------------------] 30.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===========>----------------------------] 30.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===========>----------------------------] 30.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===========>----------------------------] 30.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===========>----------------------------] 30.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===========>----------------------------] 30.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===========>----------------------------] 30.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===========>----------------------------] 30.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===========>----------------------------] 30.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===========>----------------------------] 30.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===========>----------------------------] 31.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===========>----------------------------] 31.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===========>----------------------------] 31.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===========>----------------------------] 31.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===========>----------------------------] 31.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===========>----------------------------] 31.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===========>----------------------------] 31.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===========>----------------------------] 31.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===========>----------------------------] 31.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===========>----------------------------] 31.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===========>----------------------------] 32.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===========>----------------------------] 32.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===========>----------------------------] 32.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===========>----------------------------] 32.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===========>----------------------------] 32.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============>---------------------------] 32.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============>---------------------------] 32.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============>---------------------------] 32.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============>---------------------------] 32.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============>---------------------------] 32.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============>---------------------------] 33.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============>---------------------------] 33.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============>---------------------------] 33.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============>---------------------------] 33.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============>---------------------------] 33.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============>---------------------------] 33.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============>---------------------------] 33.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============>---------------------------] 33.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============>---------------------------] 33.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============>---------------------------] 33.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============>---------------------------] 34.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============>---------------------------] 34.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============>---------------------------] 34.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============>---------------------------] 34.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============>---------------------------] 34.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============>---------------------------] 34.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============>---------------------------] 34.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============>---------------------------] 34.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============>---------------------------] 34.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============>---------------------------] 34.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=============>--------------------------] 35.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=============>--------------------------] 35.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=============>--------------------------] 35.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=============>--------------------------] 35.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=============>--------------------------] 35.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=============>--------------------------] 35.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=============>--------------------------] 35.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=============>--------------------------] 35.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=============>--------------------------] 35.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=============>--------------------------] 35.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=============>--------------------------] 36.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=============>--------------------------] 36.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=============>--------------------------] 36.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=============>--------------------------] 36.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=============>--------------------------] 36.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=============>--------------------------] 36.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=============>--------------------------] 36.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=============>--------------------------] 36.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=============>--------------------------] 36.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=============>--------------------------] 36.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=============>--------------------------] 37.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=============>--------------------------] 37.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=============>--------------------------] 37.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=============>--------------------------] 37.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=============>--------------------------] 37.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==============>-------------------------] 37.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==============>-------------------------] 37.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==============>-------------------------] 37.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==============>-------------------------] 37.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==============>-------------------------] 37.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==============>-------------------------] 38.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==============>-------------------------] 38.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==============>-------------------------] 38.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==============>-------------------------] 38.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==============>-------------------------] 38.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==============>-------------------------] 38.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==============>-------------------------] 38.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==============>-------------------------] 38.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==============>-------------------------] 38.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==============>-------------------------] 38.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==============>-------------------------] 39.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==============>-------------------------] 39.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==============>-------------------------] 39.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==============>-------------------------] 39.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==============>-------------------------] 39.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==============>-------------------------] 39.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==============>-------------------------] 39.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==============>-------------------------] 39.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==============>-------------------------] 39.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==============>-------------------------] 39.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===============>------------------------] 40.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===============>------------------------] 40.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===============>------------------------] 40.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===============>------------------------] 40.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===============>------------------------] 40.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===============>------------------------] 40.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===============>------------------------] 40.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===============>------------------------] 40.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===============>------------------------] 40.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===============>------------------------] 40.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===============>------------------------] 41.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===============>------------------------] 41.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===============>------------------------] 41.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===============>------------------------] 41.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===============>------------------------] 41.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===============>------------------------] 41.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===============>------------------------] 41.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===============>------------------------] 41.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===============>------------------------] 41.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===============>------------------------] 41.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===============>------------------------] 42.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===============>------------------------] 42.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===============>------------------------] 42.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===============>------------------------] 42.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===============>------------------------] 42.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [================>-----------------------] 42.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [================>-----------------------] 42.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [================>-----------------------] 42.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [================>-----------------------] 42.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [================>-----------------------] 42.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [================>-----------------------] 43.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [================>-----------------------] 43.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [================>-----------------------] 43.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [================>-----------------------] 43.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [================>-----------------------] 43.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [================>-----------------------] 43.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [================>-----------------------] 43.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [================>-----------------------] 43.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [================>-----------------------] 43.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [================>-----------------------] 43.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [================>-----------------------] 44.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [================>-----------------------] 44.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [================>-----------------------] 44.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [================>-----------------------] 44.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [================>-----------------------] 44.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [================>-----------------------] 44.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [================>-----------------------] 44.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [================>-----------------------] 44.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [================>-----------------------] 44.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [================>-----------------------] 44.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=================>----------------------] 45.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=================>----------------------] 45.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=================>----------------------] 45.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=================>----------------------] 45.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=================>----------------------] 45.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=================>----------------------] 45.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=================>----------------------] 45.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=================>----------------------] 45.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=================>----------------------] 45.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=================>----------------------] 45.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=================>----------------------] 46.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=================>----------------------] 46.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=================>----------------------] 46.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=================>----------------------] 46.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=================>----------------------] 46.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=================>----------------------] 46.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=================>----------------------] 46.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=================>----------------------] 46.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=================>----------------------] 46.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=================>----------------------] 46.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=================>----------------------] 47.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=================>----------------------] 47.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=================>----------------------] 47.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=================>----------------------] 47.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=================>----------------------] 47.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==================>---------------------] 47.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==================>---------------------] 47.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==================>---------------------] 47.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==================>---------------------] 47.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==================>---------------------] 47.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==================>---------------------] 48.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==================>---------------------] 48.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==================>---------------------] 48.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==================>---------------------] 48.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==================>---------------------] 48.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==================>---------------------] 48.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==================>---------------------] 48.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==================>---------------------] 48.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==================>---------------------] 48.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==================>---------------------] 48.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==================>---------------------] 49.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==================>---------------------] 49.10% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==================>---------------------] 49.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==================>---------------------] 49.30% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==================>---------------------] 49.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==================>---------------------] 49.50% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==================>---------------------] 49.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==================>---------------------] 49.70% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==================>---------------------] 49.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==================>---------------------] 49.90% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===================>--------------------] 50.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===================>--------------------] 50.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===================>--------------------] 50.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===================>--------------------] 50.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===================>--------------------] 50.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===================>--------------------] 50.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===================>--------------------] 50.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===================>--------------------] 50.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===================>--------------------] 50.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===================>--------------------] 50.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===================>--------------------] 51.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===================>--------------------] 51.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===================>--------------------] 51.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===================>--------------------] 51.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===================>--------------------] 51.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===================>--------------------] 51.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===================>--------------------] 51.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===================>--------------------] 51.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===================>--------------------] 51.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===================>--------------------] 51.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===================>--------------------] 52.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===================>--------------------] 52.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===================>--------------------] 52.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===================>--------------------] 52.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===================>--------------------] 52.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====================>-------------------] 52.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [====================>-------------------] 52.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====================>-------------------] 52.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [====================>-------------------] 52.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====================>-------------------] 52.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [====================>-------------------] 53.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====================>-------------------] 53.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [====================>-------------------] 53.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====================>-------------------] 53.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [====================>-------------------] 53.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====================>-------------------] 53.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [====================>-------------------] 53.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====================>-------------------] 53.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [====================>-------------------] 53.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====================>-------------------] 53.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [====================>-------------------] 54.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====================>-------------------] 54.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [====================>-------------------] 54.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====================>-------------------] 54.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [====================>-------------------] 54.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====================>-------------------] 54.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [====================>-------------------] 54.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====================>-------------------] 54.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [====================>-------------------] 54.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [====================>-------------------] 54.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================>------------------] 55.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=====================>------------------] 55.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================>------------------] 55.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=====================>------------------] 55.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================>------------------] 55.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=====================>------------------] 55.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================>------------------] 55.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=====================>------------------] 55.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================>------------------] 55.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=====================>------------------] 55.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================>------------------] 56.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=====================>------------------] 56.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================>------------------] 56.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=====================>------------------] 56.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================>------------------] 56.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=====================>------------------] 56.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================>------------------] 56.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=====================>------------------] 56.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================>------------------] 56.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=====================>------------------] 56.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================>------------------] 57.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=====================>------------------] 57.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================>------------------] 57.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=====================>------------------] 57.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================>------------------] 57.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======================>-----------------] 57.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [======================>-----------------] 57.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======================>-----------------] 57.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [======================>-----------------] 57.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======================>-----------------] 57.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [======================>-----------------] 58.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======================>-----------------] 58.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [======================>-----------------] 58.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======================>-----------------] 58.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [======================>-----------------] 58.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======================>-----------------] 58.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [======================>-----------------] 58.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======================>-----------------] 58.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [======================>-----------------] 58.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======================>-----------------] 58.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [======================>-----------------] 59.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======================>-----------------] 59.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [======================>-----------------] 59.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======================>-----------------] 59.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [======================>-----------------] 59.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======================>-----------------] 59.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [======================>-----------------] 59.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======================>-----------------] 59.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [======================>-----------------] 59.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [======================>-----------------] 59.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=======================>----------------] 60.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=======================>----------------] 60.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=======================>----------------] 60.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=======================>----------------] 60.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=======================>----------------] 60.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=======================>----------------] 60.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=======================>----------------] 60.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=======================>----------------] 60.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=======================>----------------] 60.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=======================>----------------] 60.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=======================>----------------] 61.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=======================>----------------] 61.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=======================>----------------] 61.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=======================>----------------] 61.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=======================>----------------] 61.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=======================>----------------] 61.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=======================>----------------] 61.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=======================>----------------] 61.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=======================>----------------] 61.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=======================>----------------] 61.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=======================>----------------] 62.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=======================>----------------] 62.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=======================>----------------] 62.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=======================>----------------] 62.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=======================>----------------] 62.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========================>---------------] 62.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [========================>---------------] 62.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========================>---------------] 62.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [========================>---------------] 62.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========================>---------------] 62.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [========================>---------------] 63.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========================>---------------] 63.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [========================>---------------] 63.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========================>---------------] 63.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [========================>---------------] 63.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========================>---------------] 63.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [========================>---------------] 63.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========================>---------------] 63.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [========================>---------------] 63.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========================>---------------] 63.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [========================>---------------] 64.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========================>---------------] 64.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [========================>---------------] 64.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========================>---------------] 64.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [========================>---------------] 64.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========================>---------------] 64.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [========================>---------------] 64.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========================>---------------] 64.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [========================>---------------] 64.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [========================>---------------] 64.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=========================>--------------] 65.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=========================>--------------] 65.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=========================>--------------] 65.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=========================>--------------] 65.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=========================>--------------] 65.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=========================>--------------] 65.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=========================>--------------] 65.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=========================>--------------] 65.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=========================>--------------] 65.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=========================>--------------] 65.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=========================>--------------] 66.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=========================>--------------] 66.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=========================>--------------] 66.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=========================>--------------] 66.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=========================>--------------] 66.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=========================>--------------] 66.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=========================>--------------] 66.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=========================>--------------] 66.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=========================>--------------] 66.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=========================>--------------] 66.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=========================>--------------] 67.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=========================>--------------] 67.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=========================>--------------] 67.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=========================>--------------] 67.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=========================>--------------] 67.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========================>-------------] 67.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==========================>-------------] 67.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========================>-------------] 67.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==========================>-------------] 67.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========================>-------------] 67.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==========================>-------------] 68.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========================>-------------] 68.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==========================>-------------] 68.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========================>-------------] 68.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==========================>-------------] 68.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========================>-------------] 68.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==========================>-------------] 68.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========================>-------------] 68.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==========================>-------------] 68.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========================>-------------] 68.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==========================>-------------] 69.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========================>-------------] 69.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==========================>-------------] 69.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========================>-------------] 69.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==========================>-------------] 69.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========================>-------------] 69.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==========================>-------------] 69.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========================>-------------] 69.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==========================>-------------] 69.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [==========================>-------------] 69.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===========================>------------] 70.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===========================>------------] 70.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===========================>------------] 70.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===========================>------------] 70.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===========================>------------] 70.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===========================>------------] 70.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===========================>------------] 70.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===========================>------------] 70.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===========================>------------] 70.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===========================>------------] 70.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===========================>------------] 71.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===========================>------------] 71.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===========================>------------] 71.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===========================>------------] 71.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===========================>------------] 71.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===========================>------------] 71.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===========================>------------] 71.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===========================>------------] 71.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===========================>------------] 71.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===========================>------------] 71.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===========================>------------] 72.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===========================>------------] 72.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===========================>------------] 72.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [===========================>------------] 72.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===========================>------------] 72.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============================>-----------] 72.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [============================>-----------] 72.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============================>-----------] 72.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [============================>-----------] 72.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============================>-----------] 72.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [============================>-----------] 73.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============================>-----------] 73.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [============================>-----------] 73.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============================>-----------] 73.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [============================>-----------] 73.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============================>-----------] 73.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [============================>-----------] 73.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============================>-----------] 73.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [============================>-----------] 73.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============================>-----------] 73.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [============================>-----------] 74.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============================>-----------] 74.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [============================>-----------] 74.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============================>-----------] 74.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [============================>-----------] 74.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============================>-----------] 74.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [============================>-----------] 74.60% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============================>-----------] 74.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [============================>-----------] 74.80% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [============================>-----------] 74.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=============================>----------] 75.00% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=============================>----------] 75.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=============================>----------] 75.20% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=============================>----------] 75.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=============================>----------] 75.40% | train_error: 10.4 | train_acc: 0.500 
+
            +
            +
              [=============================>----------] 75.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=============================>----------] 75.60% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=============================>----------] 75.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=============================>----------] 75.80% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=============================>----------] 75.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=============================>----------] 76.00% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=============================>----------] 76.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=============================>----------] 76.20% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=============================>----------] 76.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=============================>----------] 76.40% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=============================>----------] 76.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=============================>----------] 76.60% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=============================>----------] 76.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=============================>----------] 76.80% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=============================>----------] 76.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=============================>----------] 77.00% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=============================>----------] 77.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=============================>----------] 77.20% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=============================>----------] 77.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=============================>----------] 77.40% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==============================>---------] 77.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==============================>---------] 77.60% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==============================>---------] 77.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==============================>---------] 77.80% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==============================>---------] 77.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==============================>---------] 78.00% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==============================>---------] 78.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==============================>---------] 78.20% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==============================>---------] 78.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==============================>---------] 78.40% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==============================>---------] 78.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==============================>---------] 78.60% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==============================>---------] 78.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==============================>---------] 78.80% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==============================>---------] 78.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==============================>---------] 79.00% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==============================>---------] 79.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==============================>---------] 79.20% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==============================>---------] 79.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==============================>---------] 79.40% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==============================>---------] 79.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==============================>---------] 79.60% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==============================>---------] 79.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==============================>---------] 79.80% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==============================>---------] 79.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===============================>--------] 80.00% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===============================>--------] 80.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===============================>--------] 80.20% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===============================>--------] 80.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===============================>--------] 80.40% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===============================>--------] 80.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===============================>--------] 80.60% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===============================>--------] 80.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===============================>--------] 80.80% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===============================>--------] 80.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===============================>--------] 81.00% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===============================>--------] 81.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===============================>--------] 81.20% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===============================>--------] 81.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===============================>--------] 81.40% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===============================>--------] 81.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===============================>--------] 81.60% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===============================>--------] 81.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===============================>--------] 81.80% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===============================>--------] 81.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===============================>--------] 82.00% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===============================>--------] 82.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===============================>--------] 82.20% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===============================>--------] 82.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===============================>--------] 82.40% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [================================>-------] 82.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [================================>-------] 82.60% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [================================>-------] 82.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [================================>-------] 82.80% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [================================>-------] 82.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [================================>-------] 83.00% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [================================>-------] 83.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [================================>-------] 83.20% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [================================>-------] 83.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [================================>-------] 83.40% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [================================>-------] 83.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [================================>-------] 83.60% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [================================>-------] 83.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [================================>-------] 83.80% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [================================>-------] 83.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [================================>-------] 84.00% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [================================>-------] 84.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [================================>-------] 84.20% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [================================>-------] 84.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [================================>-------] 84.40% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [================================>-------] 84.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [================================>-------] 84.60% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [================================>-------] 84.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [================================>-------] 84.80% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [================================>-------] 84.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=================================>------] 85.00% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=================================>------] 85.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=================================>------] 85.20% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=================================>------] 85.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=================================>------] 85.40% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=================================>------] 85.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=================================>------] 85.60% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=================================>------] 85.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=================================>------] 85.80% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=================================>------] 85.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=================================>------] 86.00% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=================================>------] 86.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=================================>------] 86.20% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=================================>------] 86.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=================================>------] 86.40% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=================================>------] 86.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=================================>------] 86.60% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=================================>------] 86.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=================================>------] 86.80% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=================================>------] 86.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=================================>------] 87.00% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=================================>------] 87.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=================================>------] 87.20% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=================================>------] 87.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=================================>------] 87.40% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==================================>-----] 87.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==================================>-----] 87.60% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==================================>-----] 87.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==================================>-----] 87.80% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==================================>-----] 87.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==================================>-----] 88.00% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==================================>-----] 88.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==================================>-----] 88.20% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==================================>-----] 88.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==================================>-----] 88.40% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==================================>-----] 88.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==================================>-----] 88.60% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==================================>-----] 88.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==================================>-----] 88.80% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==================================>-----] 88.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==================================>-----] 89.00% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==================================>-----] 89.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==================================>-----] 89.20% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==================================>-----] 89.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==================================>-----] 89.40% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==================================>-----] 89.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==================================>-----] 89.60% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==================================>-----] 89.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==================================>-----] 89.80% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [==================================>-----] 89.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===================================>----] 90.00% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===================================>----] 90.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===================================>----] 90.20% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===================================>----] 90.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===================================>----] 90.40% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===================================>----] 90.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===================================>----] 90.60% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===================================>----] 90.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===================================>----] 90.80% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===================================>----] 90.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===================================>----] 91.00% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===================================>----] 91.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===================================>----] 91.20% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===================================>----] 91.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===================================>----] 91.40% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===================================>----] 91.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===================================>----] 91.60% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===================================>----] 91.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===================================>----] 91.80% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===================================>----] 91.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===================================>----] 92.00% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===================================>----] 92.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===================================>----] 92.20% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===================================>----] 92.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [===================================>----] 92.40% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [====================================>---] 92.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [====================================>---] 92.60% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [====================================>---] 92.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [====================================>---] 92.80% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [====================================>---] 92.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [====================================>---] 93.00% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [====================================>---] 93.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [====================================>---] 93.20% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [====================================>---] 93.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [====================================>---] 93.40% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [====================================>---] 93.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [====================================>---] 93.60% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [====================================>---] 93.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [====================================>---] 93.80% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [====================================>---] 93.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [====================================>---] 94.00% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [====================================>---] 94.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [====================================>---] 94.20% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [====================================>---] 94.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [====================================>---] 94.40% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [====================================>---] 94.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [====================================>---] 94.60% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [====================================>---] 94.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [====================================>---] 94.80% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [====================================>---] 94.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================================>--] 95.00% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================================>--] 95.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================================>--] 95.20% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================================>--] 95.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================================>--] 95.40% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================================>--] 95.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================================>--] 95.60% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================================>--] 95.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================================>--] 95.80% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================================>--] 95.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================================>--] 96.00% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================================>--] 96.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================================>--] 96.20% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================================>--] 96.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================================>--] 96.40% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================================>--] 96.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================================>--] 96.60% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================================>--] 96.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================================>--] 96.80% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================================>--] 96.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================================>--] 97.00% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================================>--] 97.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================================>--] 97.20% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================================>--] 97.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [=====================================>--] 97.40% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [======================================>-] 97.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [======================================>-] 97.60% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [======================================>-] 97.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [======================================>-] 97.80% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [======================================>-] 97.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [======================================>-] 98.00% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [======================================>-] 98.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [======================================>-] 98.20% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [======================================>-] 98.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [======================================>-] 98.40% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [======================================>-] 98.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [======================================>-] 98.60% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [======================================>-] 98.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [======================================>-] 98.80% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [======================================>-] 98.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [======================================>-] 99.00% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [======================================>-] 99.10% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [======================================>-] 99.20% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [======================================>-] 99.30% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [======================================>-] 99.40% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [======================================>-] 99.50% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [======================================>-] 99.60% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [======================================>-] 99.70% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [======================================>-] 99.80% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
              [======================================>-] 99.90% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +
                                                                                                               
+
            +
            +
              [=======================================>] 100.0% | train_error: -0.0000000010 | train_acc: 1.00 
+
            +
            +

            Not bad, but the results depend strongly on the learning reate. Try different learning rates.

            @@ -10825,6 +18596,14 @@ images.

            +
            +
            inputs = (n_inputs, pixel_width, pixel_height) = (1797, 8, 8)
+labels = (n_inputs) = (1797,)
+X = (n_inputs, n_features) = (1797, 64)
+
            +
            +_images/week43_72_1.png +
            @@ -10868,6 +18647,12 @@ collected from 12.00 to 24.00.

            +
            +
            Number of training images: 1437
+Number of test images: 360
+
            +
            +
            @@ -11040,6 +18825,25 @@ This is then passed through the activation:

            +
            +
            probabilities = (n_inputs, n_categories) = (1437, 10)
+
            +
            +
            probability that image 0 is in category 0,1,2,...,9 = 
+[5.41511965e-04 2.17174962e-03 8.84355903e-03 1.44970586e-03
+ 1.10378326e-04 5.08318298e-09 2.03256632e-04 1.92507116e-03
+ 9.84443254e-01 3.11507992e-04]
+probabilities sum up to: 1.0
+
            +
            +
            predictions = (n_inputs) = (1437,)
+
            +
            +
            prediction for image 0: 8
+correct label for image 0: 6
+
            +
            +
            @@ -11208,6 +19012,41 @@ the Hadamard product, meaning element-wise multiplication.

            +
            +
            Old accuracy on training data: 0.1440501043841336
+
            +
            +
            ---------------------------------------------------------------------------
+RuntimeWarning                            Traceback (most recent call last)
+Input In [26], in <cell line: 54>()
+     53 lmbd = 0.01
+     54 for i in range(1000):
+     55     # calculate gradients
+---> 56     dWo, dBo, dWh, dBh = backpropagation(X_train, Y_train_onehot)
+     58     # regularization term gradients
+     59     dWo += lmbd * output_weights
+
+Input In [26], in backpropagation(X, Y)
+     32 def backpropagation(X, Y):
+---> 33     a_h, probabilities = feed_forward_train(X)
+     35     # error in the output layer
+     36     error_output = probabilities - Y
+
+Input In [26], in feed_forward_train(X)
+     18 z_h = np.matmul(X, hidden_weights) + hidden_bias
+     19 # activation in the hidden layer
+---> 20 a_h = sigmoid(z_h)
+     22 # weighted sum of inputs to the output layer
+     23 z_o = np.matmul(a_h, output_weights) + output_bias
+
+Input In [25], in sigmoid(x)
+      3 def sigmoid(x):
+----> 4     return 1/(1 + np.exp(-x))
+
+RuntimeWarning: overflow encountered in exp
+
            +
            +
            diff --git a/doc/LectureNotes/_build/jupyter_execute/week41.ipynb b/doc/LectureNotes/_build/jupyter_execute/week41.ipynb index 0eb2283e7..b8d566e00 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week41.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/week41.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "2ab4355a", + "id": "9a4eccc2", "metadata": { "editable": true }, @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "bb11e4db", + "id": "243c5d47", "metadata": { "editable": true }, @@ -27,7 +27,7 @@ }, { "cell_type": "markdown", - "id": "1d2fb3fd", + "id": "0cae636e", "metadata": { "editable": true }, @@ -52,6 +52,10 @@ "\n", " * Building our own Feed-forward Neural Network\n", "\n", + " * [Video of lecture notes](https://youtu.be/5-RRTO9uDvI)\n", + "\n", + " * [Whiteboard notes](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesOct12.pdf)\n", + "\n", " * Readings and Videos:\n", "\n", " * These lecture notes\n", @@ -71,7 +75,7 @@ }, { "cell_type": "markdown", - "id": "9dcb51d0", + "id": "51ff64f7", "metadata": { "editable": true }, @@ -81,7 +85,7 @@ }, { "cell_type": "markdown", - "id": "b2dde1a5", + "id": "07ff4601", "metadata": { "editable": true }, @@ -99,7 +103,7 @@ }, { "cell_type": "markdown", - "id": "9dbb7580", + "id": "4806cddf", "metadata": { "editable": true }, @@ -123,7 +127,7 @@ }, { "cell_type": "markdown", - "id": "9b37b818", + "id": "ccfdcf2b", "metadata": { "editable": true }, @@ -141,7 +145,7 @@ }, { "cell_type": "markdown", - "id": "f0c18c43", + "id": "900fd38b", "metadata": { "editable": true }, @@ -181,7 +185,7 @@ }, { "cell_type": "markdown", - "id": "4d476078", + "id": "15e0098f", "metadata": { "editable": true }, @@ -210,7 +214,7 @@ }, { "cell_type": "markdown", - "id": "6577603f", + "id": "70d3fe5d", "metadata": { "editable": true }, @@ -231,7 +235,7 @@ }, { "cell_type": "markdown", - "id": "3f765006", + "id": "838210ba", "metadata": { "editable": true }, @@ -260,7 +264,7 @@ }, { "cell_type": "markdown", - "id": "20de12ca", + "id": "8a78d22d", "metadata": { "editable": true }, @@ -281,7 +285,7 @@ }, { "cell_type": "markdown", - "id": "48751fb1", + "id": "4ca0b479", "metadata": { "editable": true }, @@ -302,7 +306,7 @@ }, { "cell_type": "markdown", - "id": "4bc57cc0", + "id": "b80ebf1a", "metadata": { "editable": true }, @@ -319,7 +323,7 @@ }, { "cell_type": "markdown", - "id": "f194e3be", + "id": "749545b4", "metadata": { "editable": true }, @@ -340,7 +344,7 @@ }, { "cell_type": "markdown", - "id": "11dceaa5", + "id": "f524768e", "metadata": { "editable": true }, @@ -356,7 +360,7 @@ }, { "cell_type": "markdown", - "id": "9564ced4", + "id": "6c2f8b44", "metadata": { "editable": true }, @@ -373,7 +377,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "605a35fc", + "id": "8dfb9c06", "metadata": { "collapsed": false, "editable": true @@ -433,7 +437,7 @@ }, { "cell_type": "markdown", - "id": "7682eeab", + "id": "b0033599", "metadata": { "editable": true }, @@ -443,7 +447,7 @@ }, { "cell_type": "markdown", - "id": "8bbee5fe", + "id": "4663d22e", "metadata": { "editable": true }, @@ -454,7 +458,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "9ede7953", + "id": "846ae942", "metadata": { "collapsed": false, "editable": true @@ -538,7 +542,7 @@ }, { "cell_type": "markdown", - "id": "110b8ae5", + "id": "22786e56", "metadata": { "editable": true }, @@ -548,7 +552,7 @@ }, { "cell_type": "markdown", - "id": "7e5f94af", + "id": "b6bdebe1", "metadata": { "editable": true }, @@ -559,7 +563,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "f5aee400", + "id": "fdfb8244", "metadata": { "collapsed": false, "editable": true @@ -587,7 +591,7 @@ }, { "cell_type": "markdown", - "id": "812e072e", + "id": "13c5155d", "metadata": { "editable": true }, @@ -599,7 +603,7 @@ }, { "cell_type": "markdown", - "id": "64b04fac", + "id": "47b4c719", "metadata": { "editable": true }, @@ -611,7 +615,7 @@ }, { "cell_type": "markdown", - "id": "7f8be757", + "id": "d65d78c7", "metadata": { "editable": true }, @@ -626,7 +630,7 @@ }, { "cell_type": "markdown", - "id": "a3a920b2", + "id": "b8605d0d", "metadata": { "editable": true }, @@ -638,7 +642,7 @@ }, { "cell_type": "markdown", - "id": "dacd264b", + "id": "be0a2d56", "metadata": { "editable": true }, @@ -655,7 +659,7 @@ }, { "cell_type": "markdown", - "id": "5cb2ca57", + "id": "8a8ec932", "metadata": { "editable": true }, @@ -670,7 +674,7 @@ }, { "cell_type": "markdown", - "id": "c33f37be", + "id": "30b5df75", "metadata": { "editable": true }, @@ -688,7 +692,7 @@ }, { "cell_type": "markdown", - "id": "8747dd79", + "id": "9906f339", "metadata": { "editable": true }, @@ -700,7 +704,7 @@ }, { "cell_type": "markdown", - "id": "238b66fc", + "id": "705c18d2", "metadata": { "editable": true }, @@ -718,7 +722,7 @@ }, { "cell_type": "markdown", - "id": "ec349a1b", + "id": "c0779fc6", "metadata": { "editable": true }, @@ -731,7 +735,7 @@ }, { "cell_type": "markdown", - "id": "ab5abd1e", + "id": "128a41e3", "metadata": { "editable": true }, @@ -743,7 +747,7 @@ }, { "cell_type": "markdown", - "id": "f4cebe1e", + "id": "e8efb8a6", "metadata": { "editable": true }, @@ -761,7 +765,7 @@ }, { "cell_type": "markdown", - "id": "dd700242", + "id": "f975dcc6", "metadata": { "editable": true }, @@ -779,7 +783,7 @@ }, { "cell_type": "markdown", - "id": "6e309498", + "id": "7c3843f7", "metadata": { "editable": true }, @@ -789,7 +793,7 @@ }, { "cell_type": "markdown", - "id": "aad2882a", + "id": "0008d41a", "metadata": { "editable": true }, @@ -807,7 +811,7 @@ }, { "cell_type": "markdown", - "id": "dc802cd2", + "id": "e8dc6d51", "metadata": { "editable": true }, @@ -826,7 +830,7 @@ }, { "cell_type": "markdown", - "id": "57e22c56", + "id": "1455a093", "metadata": { "editable": true }, @@ -839,7 +843,7 @@ }, { "cell_type": "markdown", - "id": "6e91389e", + "id": "c2affab6", "metadata": { "editable": true }, @@ -857,7 +861,7 @@ }, { "cell_type": "markdown", - "id": "8755afdf", + "id": "c7910d23", "metadata": { "editable": true }, @@ -868,7 +872,7 @@ }, { "cell_type": "markdown", - "id": "f1dc0077", + "id": "ec9e660d", "metadata": { "editable": true }, @@ -887,7 +891,7 @@ }, { "cell_type": "markdown", - "id": "71fb8777", + "id": "29c77377", "metadata": { "editable": true }, @@ -905,7 +909,7 @@ }, { "cell_type": "markdown", - "id": "96280b34", + "id": "f52146ef", "metadata": { "editable": true }, @@ -918,7 +922,7 @@ }, { "cell_type": "markdown", - "id": "6be3363c", + "id": "7f9f65ce", "metadata": { "editable": true }, @@ -938,7 +942,7 @@ }, { "cell_type": "markdown", - "id": "d1eb96a8", + "id": "7aec05b7", "metadata": { "editable": true }, @@ -971,7 +975,7 @@ }, { "cell_type": "markdown", - "id": "044d5e68", + "id": "524f3145", "metadata": { "editable": true }, @@ -983,7 +987,7 @@ }, { "cell_type": "markdown", - "id": "23a2c440", + "id": "67ea322c", "metadata": { "editable": true }, @@ -1002,7 +1006,7 @@ }, { "cell_type": "markdown", - "id": "5f6921f1", + "id": "fb2a1836", "metadata": { "editable": true }, @@ -1016,7 +1020,7 @@ }, { "cell_type": "markdown", - "id": "bc3e047f", + "id": "c6e37074", "metadata": { "editable": true }, @@ -1039,7 +1043,7 @@ }, { "cell_type": "markdown", - "id": "f2ff2ee2", + "id": "516427eb", "metadata": { "editable": true }, @@ -1058,7 +1062,7 @@ }, { "cell_type": "markdown", - "id": "a336177e", + "id": "607e0e1f", "metadata": { "editable": true }, @@ -1070,7 +1074,7 @@ }, { "cell_type": "markdown", - "id": "c9a263c9", + "id": "4147fa7c", "metadata": { "editable": true }, @@ -1080,7 +1084,7 @@ }, { "cell_type": "markdown", - "id": "f102c33c", + "id": "4c880e2a", "metadata": { "editable": true }, @@ -1092,7 +1096,7 @@ }, { "cell_type": "markdown", - "id": "92eeded3", + "id": "56c9cd1b", "metadata": { "editable": true }, @@ -1109,7 +1113,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "17339021", + "id": "187aa1c5", "metadata": { "collapsed": false, "editable": true @@ -1248,7 +1252,7 @@ }, { "cell_type": "markdown", - "id": "8c915f6d", + "id": "e3195fd0", "metadata": { "editable": true }, @@ -1286,7 +1290,7 @@ }, { "cell_type": "markdown", - "id": "cdcb2b32", + "id": "f15de9c4", "metadata": { "editable": true }, @@ -1316,7 +1320,7 @@ }, { "cell_type": "markdown", - "id": "566b0dcb", + "id": "9d79d680", "metadata": { "editable": true }, @@ -1337,7 +1341,7 @@ }, { "cell_type": "markdown", - "id": "66c76764", + "id": "31e1a9b1", "metadata": { "editable": true }, @@ -1349,7 +1353,7 @@ }, { "cell_type": "markdown", - "id": "e3d0bfa4", + "id": "890921b9", "metadata": { "editable": true }, @@ -1364,7 +1368,7 @@ }, { "cell_type": "markdown", - "id": "01cf3c8d", + "id": "fe55dffb", "metadata": { "editable": true }, @@ -1381,7 +1385,7 @@ }, { "cell_type": "markdown", - "id": "1417eea3", + "id": "61548fd5", "metadata": { "editable": true }, @@ -1393,7 +1397,7 @@ }, { "cell_type": "markdown", - "id": "3c4e10cf", + "id": "7b437827", "metadata": { "editable": true }, @@ -1406,7 +1410,7 @@ }, { "cell_type": "markdown", - "id": "d9dc403d", + "id": "88aeaf0f", "metadata": { "editable": true }, @@ -1418,7 +1422,7 @@ }, { "cell_type": "markdown", - "id": "83b73b28", + "id": "d81878b1", "metadata": { "editable": true }, @@ -1432,7 +1436,7 @@ }, { "cell_type": "markdown", - "id": "0516dfcf", + "id": "aa3e6b55", "metadata": { "editable": true }, @@ -1444,7 +1448,7 @@ }, { "cell_type": "markdown", - "id": "7cebeb6f", + "id": "282fc0f1", "metadata": { "editable": true }, @@ -1456,7 +1460,7 @@ }, { "cell_type": "markdown", - "id": "e94a91e9", + "id": "5007c640", "metadata": { "editable": true }, @@ -1468,7 +1472,7 @@ }, { "cell_type": "markdown", - "id": "343a68be", + "id": "ed8b0010", "metadata": { "editable": true }, @@ -1478,7 +1482,7 @@ }, { "cell_type": "markdown", - "id": "1eb93ade", + "id": "3174619f", "metadata": { "editable": true }, @@ -1490,7 +1494,7 @@ }, { "cell_type": "markdown", - "id": "b4294825", + "id": "448a714d", "metadata": { "editable": true }, @@ -1500,7 +1504,7 @@ }, { "cell_type": "markdown", - "id": "3c84d05d", + "id": "862da704", "metadata": { "editable": true }, @@ -1512,7 +1516,7 @@ }, { "cell_type": "markdown", - "id": "acd76b39", + "id": "9958b437", "metadata": { "editable": true }, @@ -1526,7 +1530,7 @@ }, { "cell_type": "markdown", - "id": "f0383586", + "id": "be3a329b", "metadata": { "editable": true }, @@ -1538,7 +1542,7 @@ }, { "cell_type": "markdown", - "id": "81c5b644", + "id": "2237bf2b", "metadata": { "editable": true }, @@ -1548,7 +1552,7 @@ }, { "cell_type": "markdown", - "id": "31fde909", + "id": "74e6185e", "metadata": { "editable": true }, @@ -1560,7 +1564,7 @@ }, { "cell_type": "markdown", - "id": "4e4e2612", + "id": "462a7694", "metadata": { "editable": true }, @@ -1570,7 +1574,7 @@ }, { "cell_type": "markdown", - "id": "9c9e5698", + "id": "cde99728", "metadata": { "editable": true }, @@ -1582,7 +1586,7 @@ }, { "cell_type": "markdown", - "id": "421c9de0", + "id": "11c5d932", "metadata": { "editable": true }, @@ -1594,7 +1598,7 @@ }, { "cell_type": "markdown", - "id": "10597fa3", + "id": "d46a1cc5", "metadata": { "editable": true }, @@ -1606,7 +1610,7 @@ }, { "cell_type": "markdown", - "id": "0cf3157b", + "id": "7f634831", "metadata": { "editable": true }, @@ -1616,7 +1620,7 @@ }, { "cell_type": "markdown", - "id": "01128c99", + "id": "c2245dab", "metadata": { "editable": true }, @@ -1628,7 +1632,7 @@ }, { "cell_type": "markdown", - "id": "097ee928", + "id": "fbda1939", "metadata": { "editable": true }, @@ -1638,7 +1642,7 @@ }, { "cell_type": "markdown", - "id": "cb2ff4ec", + "id": "54e8fb49", "metadata": { "editable": true }, @@ -1650,7 +1654,7 @@ }, { "cell_type": "markdown", - "id": "ee3423ea", + "id": "0fa8b43d", "metadata": { "editable": true }, @@ -1674,7 +1678,7 @@ }, { "cell_type": "markdown", - "id": "49a7e0e3", + "id": "00455d1e", "metadata": { "editable": true }, @@ -1686,7 +1690,7 @@ }, { "cell_type": "markdown", - "id": "8efd3f58", + "id": "8a503b44", "metadata": { "editable": true }, @@ -1696,7 +1700,7 @@ }, { "cell_type": "markdown", - "id": "dd61c9f6", + "id": "28aaa847", "metadata": { "editable": true }, @@ -1708,7 +1712,7 @@ }, { "cell_type": "markdown", - "id": "7574ecb2", + "id": "ca7f309e", "metadata": { "editable": true }, @@ -1720,7 +1724,7 @@ }, { "cell_type": "markdown", - "id": "103823da", + "id": "79b5d957", "metadata": { "editable": true }, @@ -1732,7 +1736,7 @@ }, { "cell_type": "markdown", - "id": "8fffaff6", + "id": "ba24b0ba", "metadata": { "editable": true }, @@ -1742,7 +1746,7 @@ }, { "cell_type": "markdown", - "id": "ae94d046", + "id": "2f98d5ae", "metadata": { "editable": true }, @@ -1754,7 +1758,7 @@ }, { "cell_type": "markdown", - "id": "3874588e", + "id": "596bd7eb", "metadata": { "editable": true }, @@ -1764,7 +1768,7 @@ }, { "cell_type": "markdown", - "id": "296c98ed", + "id": "9144dcb7", "metadata": { "editable": true }, @@ -1778,7 +1782,7 @@ }, { "cell_type": "markdown", - "id": "19e9a098", + "id": "4f2e52cb", "metadata": { "editable": true }, @@ -1796,7 +1800,7 @@ }, { "cell_type": "markdown", - "id": "edb4be27", + "id": "3b94d154", "metadata": { "editable": true }, @@ -1806,7 +1810,7 @@ }, { "cell_type": "markdown", - "id": "f85640ae", + "id": "18f353fd", "metadata": { "editable": true }, @@ -1824,7 +1828,7 @@ }, { "cell_type": "markdown", - "id": "ef718563", + "id": "631f4feb", "metadata": { "editable": true }, @@ -1834,7 +1838,7 @@ }, { "cell_type": "markdown", - "id": "69a17671", + "id": "1793bcd7", "metadata": { "editable": true }, @@ -1852,7 +1856,7 @@ }, { "cell_type": "markdown", - "id": "84ef0653", + "id": "0cc3885e", "metadata": { "editable": true }, @@ -1879,7 +1883,7 @@ }, { "cell_type": "markdown", - "id": "fb567e6f", + "id": "7f697fec", "metadata": { "editable": true }, @@ -1891,7 +1895,7 @@ }, { "cell_type": "markdown", - "id": "42773e4f", + "id": "42a9acc8", "metadata": { "editable": true }, @@ -1903,7 +1907,7 @@ }, { "cell_type": "markdown", - "id": "c01f5515", + "id": "ac01943b", "metadata": { "editable": true }, @@ -1913,7 +1917,7 @@ }, { "cell_type": "markdown", - "id": "b23962dc", + "id": "a6ec5df0", "metadata": { "editable": true }, @@ -1925,7 +1929,7 @@ }, { "cell_type": "markdown", - "id": "c84de332", + "id": "8b0f324a", "metadata": { "editable": true }, @@ -1935,7 +1939,7 @@ }, { "cell_type": "markdown", - "id": "ed77e307", + "id": "4ae4c890", "metadata": { "editable": true }, @@ -1947,7 +1951,7 @@ }, { "cell_type": "markdown", - "id": "ab04fbdc", + "id": "b8af6a3b", "metadata": { "editable": true }, @@ -1957,7 +1961,7 @@ }, { "cell_type": "markdown", - "id": "d0ce3d50", + "id": "c5a36652", "metadata": { "editable": true }, @@ -1969,7 +1973,7 @@ }, { "cell_type": "markdown", - "id": "a217439f", + "id": "eb9f0a42", "metadata": { "editable": true }, @@ -1981,7 +1985,7 @@ }, { "cell_type": "markdown", - "id": "71731044", + "id": "cd78653c", "metadata": { "editable": true }, @@ -2004,7 +2008,7 @@ }, { "cell_type": "markdown", - "id": "a2afecfd", + "id": "adab4f13", "metadata": { "editable": true }, @@ -2016,7 +2020,7 @@ }, { "cell_type": "markdown", - "id": "0ffa0ea9", + "id": "9091074a", "metadata": { "editable": true }, @@ -2026,7 +2030,7 @@ }, { "cell_type": "markdown", - "id": "025daba8", + "id": "da04b8a8", "metadata": { "editable": true }, @@ -2038,7 +2042,7 @@ }, { "cell_type": "markdown", - "id": "935b27ef", + "id": "d707b6e0", "metadata": { "editable": true }, @@ -2048,7 +2052,7 @@ }, { "cell_type": "markdown", - "id": "50179be2", + "id": "f26ac396", "metadata": { "editable": true }, @@ -2060,7 +2064,7 @@ }, { "cell_type": "markdown", - "id": "90daf72c", + "id": "1d5d1ed0", "metadata": { "editable": true }, @@ -2072,7 +2076,7 @@ }, { "cell_type": "markdown", - "id": "63e9545c", + "id": "d686cbb0", "metadata": { "editable": true }, @@ -2083,7 +2087,7 @@ }, { "cell_type": "markdown", - "id": "e8fbaffb", + "id": "31554688", "metadata": { "editable": true }, @@ -2106,7 +2110,7 @@ }, { "cell_type": "markdown", - "id": "59b2fcba", + "id": "a34466dc", "metadata": { "editable": true }, @@ -2118,7 +2122,7 @@ }, { "cell_type": "markdown", - "id": "db7fd0df", + "id": "742b0899", "metadata": { "editable": true }, @@ -2128,7 +2132,7 @@ }, { "cell_type": "markdown", - "id": "fef9229b", + "id": "813c7f07", "metadata": { "editable": true }, @@ -2140,7 +2144,7 @@ }, { "cell_type": "markdown", - "id": "720ee2fe", + "id": "88fccb02", "metadata": { "editable": true }, @@ -2150,7 +2154,7 @@ }, { "cell_type": "markdown", - "id": "df965f5a", + "id": "a5d5593c", "metadata": { "editable": true }, @@ -2162,7 +2166,7 @@ }, { "cell_type": "markdown", - "id": "29bef69b", + "id": "1b5aeaa8", "metadata": { "editable": true }, @@ -2174,7 +2178,7 @@ }, { "cell_type": "markdown", - "id": "8f8670bc", + "id": "29156297", "metadata": { "editable": true }, @@ -2185,7 +2189,7 @@ }, { "cell_type": "markdown", - "id": "b37be623", + "id": "752b2b7a", "metadata": { "editable": true }, @@ -2208,7 +2212,7 @@ }, { "cell_type": "markdown", - "id": "6ebda602", + "id": "29bb348b", "metadata": { "editable": true }, @@ -2220,7 +2224,7 @@ }, { "cell_type": "markdown", - "id": "6f2371ba", + "id": "2cbb4812", "metadata": { "editable": true }, @@ -2230,7 +2234,7 @@ }, { "cell_type": "markdown", - "id": "b3458652", + "id": "73fa8d95", "metadata": { "editable": true }, @@ -2242,7 +2246,7 @@ }, { "cell_type": "markdown", - "id": "4a46987f", + "id": "d815b24a", "metadata": { "editable": true }, @@ -2252,7 +2256,7 @@ }, { "cell_type": "markdown", - "id": "2b898b0d", + "id": "43d2f617", "metadata": { "editable": true }, @@ -2264,7 +2268,7 @@ }, { "cell_type": "markdown", - "id": "a1af5bcf", + "id": "bf7af7f5", "metadata": { "editable": true }, @@ -2276,7 +2280,7 @@ }, { "cell_type": "markdown", - "id": "8d7a0d4d", + "id": "9a5e5eaf", "metadata": { "editable": true }, @@ -2287,7 +2291,7 @@ }, { "cell_type": "markdown", - "id": "37f6a409", + "id": "e62f23f4", "metadata": { "editable": true }, @@ -2315,7 +2319,7 @@ }, { "cell_type": "markdown", - "id": "e7939992", + "id": "42d7a94e", "metadata": { "editable": true }, @@ -2327,7 +2331,7 @@ }, { "cell_type": "markdown", - "id": "d754aefd", + "id": "5bfea505", "metadata": { "editable": true }, @@ -2337,7 +2341,7 @@ }, { "cell_type": "markdown", - "id": "7711b16e", + "id": "9f6b8143", "metadata": { "editable": true }, @@ -2349,7 +2353,7 @@ }, { "cell_type": "markdown", - "id": "e831f3b5", + "id": "194f2cbe", "metadata": { "editable": true }, @@ -2360,7 +2364,7 @@ }, { "cell_type": "markdown", - "id": "428cb018", + "id": "20cd18e6", "metadata": { "editable": true }, @@ -2372,7 +2376,7 @@ }, { "cell_type": "markdown", - "id": "303e8c9e", + "id": "b3e52796", "metadata": { "editable": true }, @@ -2385,7 +2389,7 @@ }, { "cell_type": "markdown", - "id": "42c3514b", + "id": "7cb68d89", "metadata": { "editable": true }, @@ -2410,7 +2414,7 @@ }, { "cell_type": "markdown", - "id": "6675c087", + "id": "0a98f80a", "metadata": { "editable": true }, @@ -2423,7 +2427,7 @@ }, { "cell_type": "markdown", - "id": "73cfb814", + "id": "b86447a6", "metadata": { "editable": true }, @@ -2435,7 +2439,7 @@ }, { "cell_type": "markdown", - "id": "c32b2b0e", + "id": "942e0060", "metadata": { "editable": true }, @@ -2447,7 +2451,7 @@ }, { "cell_type": "markdown", - "id": "15c86254", + "id": "99eae6f4", "metadata": { "editable": true }, @@ -2457,7 +2461,7 @@ }, { "cell_type": "markdown", - "id": "9da11ce8", + "id": "872017e1", "metadata": { "editable": true }, @@ -2469,7 +2473,7 @@ }, { "cell_type": "markdown", - "id": "47275fce", + "id": "f55259c0", "metadata": { "editable": true }, @@ -2481,7 +2485,7 @@ }, { "cell_type": "markdown", - "id": "22860f6c", + "id": "db983bc4", "metadata": { "editable": true }, @@ -2493,7 +2497,7 @@ }, { "cell_type": "markdown", - "id": "2c90d0da", + "id": "165881cf", "metadata": { "editable": true }, @@ -2505,7 +2509,7 @@ }, { "cell_type": "markdown", - "id": "12ae44b0", + "id": "50ecaa83", "metadata": { "editable": true }, @@ -2515,7 +2519,7 @@ }, { "cell_type": "markdown", - "id": "7d921bf4", + "id": "b16d30f7", "metadata": { "editable": true }, @@ -2527,7 +2531,7 @@ }, { "cell_type": "markdown", - "id": "5e48ee91", + "id": "2d4c1829", "metadata": { "editable": true }, @@ -2537,7 +2541,7 @@ }, { "cell_type": "markdown", - "id": "6add3f84", + "id": "4d7a8c79", "metadata": { "editable": true }, @@ -2549,7 +2553,7 @@ }, { "cell_type": "markdown", - "id": "22759cfd", + "id": "7c4a0d89", "metadata": { "editable": true }, @@ -2562,7 +2566,7 @@ }, { "cell_type": "markdown", - "id": "d18311a9", + "id": "96bccb9e", "metadata": { "editable": true }, @@ -2574,7 +2578,7 @@ }, { "cell_type": "markdown", - "id": "9761e98e", + "id": "01f9ae68", "metadata": { "editable": true }, @@ -2584,7 +2588,7 @@ }, { "cell_type": "markdown", - "id": "01a4059b", + "id": "590fc8bd", "metadata": { "editable": true }, @@ -2596,7 +2600,7 @@ }, { "cell_type": "markdown", - "id": "56bf706a", + "id": "f3dc3a43", "metadata": { "editable": true }, @@ -2607,7 +2611,7 @@ }, { "cell_type": "markdown", - "id": "60613b46", + "id": "7dd1bbaa", "metadata": { "editable": true }, @@ -2619,7 +2623,7 @@ }, { "cell_type": "markdown", - "id": "e97cc842", + "id": "dc472c94", "metadata": { "editable": true }, @@ -2629,7 +2633,7 @@ }, { "cell_type": "markdown", - "id": "9c431661", + "id": "372c5019", "metadata": { "editable": true }, @@ -2641,7 +2645,7 @@ }, { "cell_type": "markdown", - "id": "1903bf14", + "id": "783cc2c1", "metadata": { "editable": true }, @@ -2651,7 +2655,7 @@ }, { "cell_type": "markdown", - "id": "dab43f84", + "id": "d37a48b8", "metadata": { "editable": true }, @@ -2662,7 +2666,7 @@ }, { "cell_type": "markdown", - "id": "03d7a2cf", + "id": "e6790133", "metadata": { "editable": true }, @@ -2675,7 +2679,7 @@ }, { "cell_type": "markdown", - "id": "1813356c", + "id": "97578009", "metadata": { "editable": true }, @@ -2685,7 +2689,7 @@ }, { "cell_type": "markdown", - "id": "debe1289", + "id": "13822f62", "metadata": { "editable": true }, @@ -2697,7 +2701,7 @@ }, { "cell_type": "markdown", - "id": "6fc65621", + "id": "10bf9fb7", "metadata": { "editable": true }, @@ -2707,7 +2711,7 @@ }, { "cell_type": "markdown", - "id": "8c86ad26", + "id": "cf59a594", "metadata": { "editable": true }, @@ -2719,7 +2723,7 @@ }, { "cell_type": "markdown", - "id": "86ecd366", + "id": "80a4d3d7", "metadata": { "editable": true }, @@ -2729,7 +2733,7 @@ }, { "cell_type": "markdown", - "id": "d68a68c2", + "id": "91baac68", "metadata": { "editable": true }, @@ -2753,7 +2757,7 @@ }, { "cell_type": "markdown", - "id": "c9ec79c6", + "id": "b4d8a71e", "metadata": { "editable": true }, @@ -2803,7 +2807,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "b07f9bb8", + "id": "ad941e6f", "metadata": { "collapsed": false, "editable": true @@ -2880,7 +2884,7 @@ }, { "cell_type": "markdown", - "id": "6b776a41", + "id": "c2f821dc", "metadata": { "editable": true }, @@ -2901,7 +2905,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "3e399ed6", + "id": "d368bcd4", "metadata": { "collapsed": false, "editable": true @@ -2948,7 +2952,7 @@ }, { "cell_type": "markdown", - "id": "2c165e0e", + "id": "c0e21c2d", "metadata": { "editable": true }, @@ -2992,7 +2996,7 @@ }, { "cell_type": "markdown", - "id": "94075506", + "id": "ba8404a4", "metadata": { "editable": true }, @@ -3032,7 +3036,7 @@ }, { "cell_type": "markdown", - "id": "0bb70540", + "id": "5ea9e948", "metadata": { "editable": true }, @@ -3053,7 +3057,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "f3894282", + "id": "890bc8b7", "metadata": { "collapsed": false, "editable": true @@ -3079,7 +3083,7 @@ }, { "cell_type": "markdown", - "id": "25c08547", + "id": "1acc9cfe", "metadata": { "editable": true }, @@ -3107,7 +3111,7 @@ }, { "cell_type": "markdown", - "id": "96a5c9b5", + "id": "e13f966a", "metadata": { "editable": true }, @@ -3144,7 +3148,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "948f1d5c", + "id": "8df5db3a", "metadata": { "collapsed": false, "editable": true @@ -3207,7 +3211,7 @@ }, { "cell_type": "markdown", - "id": "6a48b541", + "id": "aa73fc1e", "metadata": { "editable": true }, @@ -3238,7 +3242,7 @@ }, { "cell_type": "markdown", - "id": "92ae85de", + "id": "74081e44", "metadata": { "editable": true }, @@ -3276,7 +3280,7 @@ }, { "cell_type": "markdown", - "id": "50af9f08", + "id": "1e1c4fe3", "metadata": { "editable": true }, @@ -3310,7 +3314,7 @@ }, { "cell_type": "markdown", - "id": "947d899e", + "id": "6f66a9ad", "metadata": { "editable": true }, @@ -3351,7 +3355,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "9ac16dfb", + "id": "b4ba72fe", "metadata": { "collapsed": false, "editable": true @@ -3368,7 +3372,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -3453,7 +3457,7 @@ }, { "cell_type": "markdown", - "id": "15fa07c3", + "id": "1bf824b2", "metadata": { "editable": true }, @@ -3474,7 +3478,7 @@ }, { "cell_type": "markdown", - "id": "e8e7304b", + "id": "c98cec8d", "metadata": { "editable": true }, @@ -3488,7 +3492,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "da41ccbe", + "id": "26d4691e", "metadata": { "collapsed": false, "editable": true @@ -3598,7 +3602,7 @@ }, { "cell_type": "markdown", - "id": "6ed32b94", + "id": "24f75613", "metadata": { "editable": true }, @@ -3617,7 +3621,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "a2566347", + "id": "52a987ae", "metadata": { "collapsed": false, "editable": true @@ -3652,7 +3656,7 @@ }, { "cell_type": "markdown", - "id": "779ebe17", + "id": "1605eb14", "metadata": { "editable": true }, @@ -3666,7 +3670,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "f55e622e", + "id": "952131d2", "metadata": { "collapsed": false, "editable": true @@ -3956,7 +3960,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -3974,7 +3978,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -3992,7 +3996,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -4010,7 +4014,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -4028,7 +4032,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -4046,7 +4050,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -4064,7 +4068,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -4082,11 +4086,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -4104,11 +4108,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -4126,11 +4130,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -4148,11 +4152,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -4170,11 +4174,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -4192,7 +4196,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -4210,11 +4214,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -4232,11 +4236,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -4254,11 +4258,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -4276,11 +4280,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -4298,11 +4302,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -4320,11 +4324,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -4342,11 +4346,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -4364,11 +4368,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -4408,7 +4412,7 @@ }, { "cell_type": "markdown", - "id": "4a2ea09c", + "id": "182a8a49", "metadata": { "editable": true }, @@ -4419,7 +4423,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "d1dc32a5", + "id": "d3d1b7d5", "metadata": { "collapsed": false, "editable": true @@ -4429,15 +4433,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19431/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -4508,7 +4512,7 @@ }, { "cell_type": "markdown", - "id": "fb02adc3", + "id": "aa705900", "metadata": { "editable": true }, @@ -4531,7 +4535,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "94531c6e", + "id": "09c32314", "metadata": { "collapsed": false, "editable": true @@ -4980,13 +4984,7 @@ "Learning rate = 0.01\n", "Lambda = 1.0\n", "Accuracy score on test set: 0.9722222222222222\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "\n", "Learning rate = 0.01\n", "Lambda = 10.0\n", "Accuracy score on test set: 0.9527777777777777\n", @@ -5064,10 +5062,6 @@ "Learning rate = 1.0\n", "Lambda = 0.01\n", "Accuracy score on test set: 0.17777777777777778\n", - "\n", - "Learning rate = 1.0\n", - "Lambda = 0.1\n", - "Accuracy score on test set: 0.08333333333333333\n", "\n" ] }, @@ -5075,6 +5069,10 @@ "name": "stdout", "output_type": "stream", "text": [ + "Learning rate = 1.0\n", + "Lambda = 0.1\n", + "Accuracy score on test set: 0.08333333333333333\n", + "\n", "Learning rate = 1.0\n", "Lambda = 1.0\n", "Accuracy score on test set: 0.08888888888888889\n", @@ -5159,7 +5157,7 @@ }, { "cell_type": "markdown", - "id": "1373fc77", + "id": "fde4721e", "metadata": { "editable": true }, @@ -5170,7 +5168,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "2693a59e", + "id": "e855729e", "metadata": { "collapsed": false, "editable": true @@ -5244,7 +5242,7 @@ }, { "cell_type": "markdown", - "id": "a7e93079", + "id": "765ac227", "metadata": { "editable": true }, @@ -5271,7 +5269,7 @@ }, { "cell_type": "markdown", - "id": "5ed2f79a", + "id": "02d395cd", "metadata": { "editable": true }, @@ -5309,7 +5307,7 @@ }, { "cell_type": "markdown", - "id": "cbf09e4f", + "id": "0fba1e7d", "metadata": { "editable": true }, @@ -5321,7 +5319,7 @@ }, { "cell_type": "markdown", - "id": "fbb06edc", + "id": "9a3c4e68", "metadata": { "editable": true }, @@ -5336,7 +5334,7 @@ }, { "cell_type": "markdown", - "id": "9859c6a0", + "id": "545fccde", "metadata": { "editable": true }, @@ -5346,7 +5344,7 @@ }, { "cell_type": "markdown", - "id": "d71c8649", + "id": "678395d0", "metadata": { "editable": true }, @@ -5359,7 +5357,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "e8dc02bd", + "id": "7c64daef", "metadata": { "collapsed": false, "editable": true @@ -5447,7 +5445,7 @@ }, { "cell_type": "markdown", - "id": "87cfac77", + "id": "c35ff8f2", "metadata": { "editable": true }, @@ -5457,7 +5455,7 @@ }, { "cell_type": "markdown", - "id": "fa50c112", + "id": "3b5396c0", "metadata": { "editable": true }, @@ -5468,7 +5466,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "6673546e", + "id": "3d4e508b", "metadata": { "collapsed": false, "editable": true @@ -5645,13 +5643,7 @@ "Learning rate = 1.0\n", "Lambda = 10.0\n", "Accuracy score on data set: 0.5\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "\n", "Learning rate = 10.0\n", "Lambda = 1e-05\n", "Accuracy score on data set: 0.5\n", @@ -5717,7 +5709,7 @@ }, "metadata": { "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week41_211_3.png" + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week41_211_2.png" } }, "output_type": "display_data" diff --git a/doc/LectureNotes/_build/jupyter_execute/week41.py b/doc/LectureNotes/_build/jupyter_execute/week41.py index ac72c936e..f18f2382c 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week41.py +++ b/doc/LectureNotes/_build/jupyter_execute/week41.py @@ -30,6 +30,10 @@ # # * Building our own Feed-forward Neural Network # +# * [Video of lecture notes](https://youtu.be/5-RRTO9uDvI) +# +# * [Whiteboard notes](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesOct12.pdf) +# # * Readings and Videos: # # * These lecture notes diff --git a/doc/LectureNotes/_build/jupyter_execute/week42.ipynb b/doc/LectureNotes/_build/jupyter_execute/week42.ipynb index 0bc7cbf2b..6bc3aeb2d 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week42.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/week42.ipynb @@ -2,8 +2,10 @@ "cells": [ { "cell_type": "markdown", - "id": "50ce4eae", - "metadata": {}, + "id": "bbaa1aec", + "metadata": { + "editable": true + }, "source": [ "\n", @@ -12,8 +14,10 @@ }, { "cell_type": "markdown", - "id": "f46bd6b4", - "metadata": {}, + "id": "d981139a", + "metadata": { + "editable": true + }, "source": [ "# Week 42 Constructing a Neural Network code with introduction to Tensor flow\n", "**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and Facility for Rare Isotope Beams, Michigan State University\n", @@ -23,8 +27,10 @@ }, { "cell_type": "markdown", - "id": "8c0fa4d7", - "metadata": {}, + "id": "d3d2fdcb", + "metadata": { + "editable": true + }, "source": [ "## Plan for week 42\n", "\n", @@ -46,6 +52,10 @@ "\n", " * These lecture notes\n", "\n", + " * [Video of lecture](https://youtu.be/0q5-PhovchQ)\n", + "\n", + " * [Whiteboard notes](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesOct19.pdf)\n", + "\n", " * [Aurelien Geron's chapters 10-11](https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf)\n", "\n", " * For a more in depth discussion on neural networks we recommend Goodfellow et al chapters 6 and 7. \n", @@ -63,16 +73,20 @@ }, { "cell_type": "markdown", - "id": "89b6b637", - "metadata": {}, + "id": "8e0c0ad3", + "metadata": { + "editable": true + }, "source": [ "## Lecture Thursday October 19" ] }, { "cell_type": "markdown", - "id": "3a32ad82", - "metadata": {}, + "id": "6d072b79", + "metadata": { + "editable": true + }, "source": [ "## Review of the back propagation algorithm\n", "\n", @@ -84,8 +98,10 @@ }, { "cell_type": "markdown", - "id": "4f9291ee", - "metadata": {}, + "id": "6a5894d2", + "metadata": { + "editable": true + }, "source": [ "## Setting up the Back propagation algorithm\n", "\n", @@ -105,8 +121,10 @@ }, { "cell_type": "markdown", - "id": "7753981f", - "metadata": {}, + "id": "47296efd", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\delta_j^L = f'(z_j^L)\\frac{\\partial {\\cal C}}{\\partial (a_j^L)}.\n", @@ -115,16 +133,20 @@ }, { "cell_type": "markdown", - "id": "8b093c71", - "metadata": {}, + "id": "598d3a19", + "metadata": { + "editable": true + }, "source": [ "Then we compute the back propagate error for each $l=L-1,L-2,\\dots,2$ as" ] }, { "cell_type": "markdown", - "id": "96ca25bd", - "metadata": {}, + "id": "7077d9c2", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\delta_j^l = \\sum_k \\delta_k^{l+1}w_{kj}^{l+1}f'(z_j^l).\n", @@ -133,16 +155,20 @@ }, { "cell_type": "markdown", - "id": "a156d8bd", - "metadata": {}, + "id": "c62043b3", + "metadata": { + "editable": true + }, "source": [ "Finally, we update the weights and the biases using gradient descent for each $l=L-1,L-2,\\dots,2$ and update the weights and biases according to the rules" ] }, { "cell_type": "markdown", - "id": "f35c8afe", - "metadata": {}, + "id": "3307a4bc", + "metadata": { + "editable": true + }, "source": [ "$$\n", "w_{jk}^l\\leftarrow = w_{jk}^l- \\eta \\delta_j^la_k^{l-1},\n", @@ -151,8 +177,10 @@ }, { "cell_type": "markdown", - "id": "ffa6d322", - "metadata": {}, + "id": "50db23f1", + "metadata": { + "editable": true + }, "source": [ "$$\n", "b_j^l \\leftarrow b_j^l-\\eta \\frac{\\partial {\\cal C}}{\\partial b_j^l}=b_j^l-\\eta \\delta_j^l,\n", @@ -161,8 +189,10 @@ }, { "cell_type": "markdown", - "id": "7b6e59f6", - "metadata": {}, + "id": "0cf89ca4", + "metadata": { + "editable": true + }, "source": [ "The parameter $\\eta$ is the learning parameter discussed in connection with the gradient descent methods.\n", "Here it is convenient to use stochastic gradient descent (see the examples below) with mini-batches with an outer loop that steps through multiple epochs of training." @@ -170,8 +200,10 @@ }, { "cell_type": "markdown", - "id": "e93ff00c", - "metadata": {}, + "id": "d5374d6f", + "metadata": { + "editable": true + }, "source": [ "## Setting up a Multi-layer perceptron model for classification\n", "\n", @@ -196,8 +228,10 @@ }, { "cell_type": "markdown", - "id": "3c437395", - "metadata": {}, + "id": "fc8ce130", + "metadata": { + "editable": true + }, "source": [ "$$\n", "P(y = 0 \\mid \\boldsymbol{x}, \\boldsymbol{\\theta}) = \\frac{1}{1 + \\exp{(- \\boldsymbol{x}})} ,\n", @@ -206,16 +240,20 @@ }, { "cell_type": "markdown", - "id": "3d7b1140", - "metadata": {}, + "id": "8eaf0c3c", + "metadata": { + "editable": true + }, "source": [ "and" ] }, { "cell_type": "markdown", - "id": "e3df5aec", - "metadata": {}, + "id": "3caeb6b3", + "metadata": { + "editable": true + }, "source": [ "$$\n", "P(y = 1 \\mid \\boldsymbol{x}, \\boldsymbol{\\theta}) = 1 - P(y = 0 \\mid \\boldsymbol{x}, \\boldsymbol{\\theta}) ,\n", @@ -224,8 +262,10 @@ }, { "cell_type": "markdown", - "id": "63345646", - "metadata": {}, + "id": "cb5b4f3d", + "metadata": { + "editable": true + }, "source": [ "where $y \\in \\{0, 1\\}$ and $\\boldsymbol{\\theta}$ represents the weights and biases\n", "of our network." @@ -233,8 +273,10 @@ }, { "cell_type": "markdown", - "id": "6ac465b3", - "metadata": {}, + "id": "6edbd945", + "metadata": { + "editable": true + }, "source": [ "## Defining the cost function\n", "\n", @@ -243,8 +285,10 @@ }, { "cell_type": "markdown", - "id": "cf06b4a0", - "metadata": {}, + "id": "3e039295", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\mathcal{C}(\\boldsymbol{\\theta}) = - \\ln P(\\mathcal{D} \\mid \\boldsymbol{\\theta}) = - \\sum_{i=1}^n\n", @@ -254,8 +298,10 @@ }, { "cell_type": "markdown", - "id": "719f761f", - "metadata": {}, + "id": "60d3b57c", + "metadata": { + "editable": true + }, "source": [ "This last equality means that we can interpret our *cost* function as a sum over the *loss* function\n", "for each point in the dataset $\\mathcal{L}_i(\\boldsymbol{\\theta})$. \n", @@ -277,8 +323,10 @@ }, { "cell_type": "markdown", - "id": "342de1d9", - "metadata": {}, + "id": "9045875f", + "metadata": { + "editable": true + }, "source": [ "$$\n", "P(y_{ic} = 1 \\mid \\boldsymbol{x}_i, \\boldsymbol{\\theta}) = \\frac{\\exp{((\\boldsymbol{a}_i^{hidden})^T \\boldsymbol{w}_c)}}\n", @@ -288,8 +336,10 @@ }, { "cell_type": "markdown", - "id": "211a69ba", - "metadata": {}, + "id": "1d37a3a2", + "metadata": { + "editable": true + }, "source": [ "which reduces to the logistic function in the binary case. \n", "The likelihood of this $C$-class classifier\n", @@ -298,8 +348,10 @@ }, { "cell_type": "markdown", - "id": "5f0cd5a2", - "metadata": {}, + "id": "429c3549", + "metadata": { + "editable": true + }, "source": [ "$$\n", "P(\\mathcal{D} \\mid \\boldsymbol{\\theta}) = \\prod_{i=1}^n \\prod_{c=0}^{C-1} [P(y_{ic} = 1)]^{y_{ic}} .\n", @@ -308,16 +360,20 @@ }, { "cell_type": "markdown", - "id": "fe018e32", - "metadata": {}, + "id": "cde118d9", + "metadata": { + "editable": true + }, "source": [ "Again we take the negative log-likelihood to define our cost function:" ] }, { "cell_type": "markdown", - "id": "9d48faca", - "metadata": {}, + "id": "16740280", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\mathcal{C}(\\boldsymbol{\\theta}) = - \\log{P(\\mathcal{D} \\mid \\boldsymbol{\\theta})}.\n", @@ -326,8 +382,10 @@ }, { "cell_type": "markdown", - "id": "897c8b0c", - "metadata": {}, + "id": "a4b60c6f", + "metadata": { + "editable": true + }, "source": [ "See the logistic regression lectures for a full definition of the cost function.\n", "\n", @@ -336,8 +394,10 @@ }, { "cell_type": "markdown", - "id": "68347a7f", - "metadata": {}, + "id": "36cce044", + "metadata": { + "editable": true + }, "source": [ "## Example: binary classification problem\n", "\n", @@ -346,8 +406,10 @@ }, { "cell_type": "markdown", - "id": "8425d868", - "metadata": {}, + "id": "d2cc5185", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\mathcal{C}(\\boldsymbol{\\beta}) = - \\sum_{i=1}^n \\left(y_i\\log{p(y_i \\vert x_i,\\boldsymbol{\\beta})}+(1-y_i)\\log{1-p(y_i \\vert x_i,\\boldsymbol{\\beta})}\\right),\n", @@ -356,16 +418,20 @@ }, { "cell_type": "markdown", - "id": "9108d4ac", - "metadata": {}, + "id": "6f62ac34", + "metadata": { + "editable": true + }, "source": [ "where we had defined the logistic (sigmoid) function" ] }, { "cell_type": "markdown", - "id": "77e0ec3b", - "metadata": {}, + "id": "980d2595", + "metadata": { + "editable": true + }, "source": [ "$$\n", "p(y_i =1\\vert x_i,\\boldsymbol{\\beta})=\\frac{\\exp{(\\beta_0+\\beta_1 x_i)}}{1+\\exp{(\\beta_0+\\beta_1 x_i)}},\n", @@ -374,16 +440,20 @@ }, { "cell_type": "markdown", - "id": "64ed867c", - "metadata": {}, + "id": "07f96bba", + "metadata": { + "editable": true + }, "source": [ "and" ] }, { "cell_type": "markdown", - "id": "51819578", - "metadata": {}, + "id": "ab7ef463", + "metadata": { + "editable": true + }, "source": [ "$$\n", "p(y_i =0\\vert x_i,\\boldsymbol{\\beta})=1-p(y_i =1\\vert x_i,\\boldsymbol{\\beta}).\n", @@ -392,8 +462,10 @@ }, { "cell_type": "markdown", - "id": "db6532a5", - "metadata": {}, + "id": "712f14c5", + "metadata": { + "editable": true + }, "source": [ "The parameters $\\boldsymbol{\\beta}$ were defined using a minimization method like gradient descent or Newton-Raphson's method. \n", "\n", @@ -403,8 +475,10 @@ }, { "cell_type": "markdown", - "id": "24e5e213", - "metadata": {}, + "id": "efb3f21c", + "metadata": { + "editable": true + }, "source": [ "$$\n", "a_i^l = y_i = \\frac{\\exp{(z_i^l)}}{1+\\exp{(z_i^l)}},\n", @@ -413,16 +487,20 @@ }, { "cell_type": "markdown", - "id": "d398c961", - "metadata": {}, + "id": "661dd5e4", + "metadata": { + "editable": true + }, "source": [ "with" ] }, { "cell_type": "markdown", - "id": "236d161c", - "metadata": {}, + "id": "545879f3", + "metadata": { + "editable": true + }, "source": [ "$$\n", "z_i^l = \\sum_{j}w_{ij}^l a_j^{l-1}+b_i^l,\n", @@ -431,8 +509,10 @@ }, { "cell_type": "markdown", - "id": "25e3004d", - "metadata": {}, + "id": "20187a39", + "metadata": { + "editable": true + }, "source": [ "where the superscript $l-1$ indicates that these are the outputs from layer $l-1$.\n", "Our cost function at the final layer $l=L$ is now" @@ -440,8 +520,10 @@ }, { "cell_type": "markdown", - "id": "9440c725", - "metadata": {}, + "id": "ecd3c551", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\mathcal{C}(\\boldsymbol{W}) = - \\sum_{i=1}^n \\left(t_i\\log{a_i^L}+(1-t_i)\\log{(1-a_i^L)}\\right),\n", @@ -450,16 +532,20 @@ }, { "cell_type": "markdown", - "id": "782f5282", - "metadata": {}, + "id": "03d1bd2b", + "metadata": { + "editable": true + }, "source": [ "where we have defined the targets $t_i$. The derivatives of the cost function with respect to the output $a_i^L$ are then easily calculated and we get" ] }, { "cell_type": "markdown", - "id": "0e8498a5", - "metadata": {}, + "id": "1baaf3b0", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\frac{\\partial \\mathcal{C}(\\boldsymbol{W})}{\\partial a_i^L} = \\frac{a_i^L-t_i}{a_i^L(1-a_i^L)}.\n", @@ -468,16 +554,20 @@ }, { "cell_type": "markdown", - "id": "68398b35", - "metadata": {}, + "id": "9114b454", + "metadata": { + "editable": true + }, "source": [ "In case we use another activation function than the logistic one, we need to evaluate other derivatives." ] }, { "cell_type": "markdown", - "id": "19887152", - "metadata": {}, + "id": "19b41dd4", + "metadata": { + "editable": true + }, "source": [ "## The Softmax function\n", "In case we employ the more general case given by the Softmax equation, we need to evaluate the derivative of the activation function with respect to the activation $z_i^l$, that is we need" @@ -485,8 +575,10 @@ }, { "cell_type": "markdown", - "id": "80e8dc5d", - "metadata": {}, + "id": "bc1b97c5", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\frac{\\partial f(z_i^l)}{\\partial w_{jk}^l} =\n", @@ -496,16 +588,20 @@ }, { "cell_type": "markdown", - "id": "68d33776", - "metadata": {}, + "id": "52f2e768", + "metadata": { + "editable": true + }, "source": [ "For the Softmax function we have" ] }, { "cell_type": "markdown", - "id": "3c86943c", - "metadata": {}, + "id": "1a60c363", + "metadata": { + "editable": true + }, "source": [ "$$\n", "f(z_i^l) = \\frac{\\exp{(z_i^l)}}{\\sum_{m=1}^K\\exp{(z_m^l)}}.\n", @@ -514,16 +610,20 @@ }, { "cell_type": "markdown", - "id": "efe53876", - "metadata": {}, + "id": "93eb34b6", + "metadata": { + "editable": true + }, "source": [ "Its derivative with respect to $z_j^l$ gives" ] }, { "cell_type": "markdown", - "id": "fce5b9b2", - "metadata": {}, + "id": "aa26229f", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\frac{\\partial f(z_i^l)}{\\partial z_j^l}= f(z_i^l)\\left(\\delta_{ij}-f(z_j^l)\\right),\n", @@ -532,16 +632,20 @@ }, { "cell_type": "markdown", - "id": "97210471", - "metadata": {}, + "id": "1f075e8c", + "metadata": { + "editable": true + }, "source": [ "which in case of the simply binary model reduces to having $i=j$." ] }, { "cell_type": "markdown", - "id": "4f515591", - "metadata": {}, + "id": "b0cb8b0e", + "metadata": { + "editable": true + }, "source": [ "## Developing a code for doing neural networks with back propagation\n", "\n", @@ -562,8 +666,10 @@ }, { "cell_type": "markdown", - "id": "ec34f212", - "metadata": {}, + "id": "c4cd71b6", + "metadata": { + "editable": true + }, "source": [ "## Collect and pre-process data\n", "\n", @@ -610,8 +716,11 @@ { "cell_type": "code", "execution_count": 1, - "id": "e389e60e", - "metadata": {}, + "id": "ca43227b", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [ { "name": "stdout", @@ -686,8 +795,10 @@ }, { "cell_type": "markdown", - "id": "9a264b82", - "metadata": {}, + "id": "79f4798f", + "metadata": { + "editable": true + }, "source": [ "## Train and test datasets\n", "\n", @@ -705,8 +816,11 @@ { "cell_type": "code", "execution_count": 2, - "id": "8750ea41", - "metadata": {}, + "id": "38e01634", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [ { "name": "stdout", @@ -749,8 +863,10 @@ }, { "cell_type": "markdown", - "id": "d3897eca", - "metadata": {}, + "id": "faca5ec2", + "metadata": { + "editable": true + }, "source": [ "## Define model and architecture\n", "\n", @@ -791,8 +907,10 @@ }, { "cell_type": "markdown", - "id": "ad593a03", - "metadata": {}, + "id": "e720f042", + "metadata": { + "editable": true + }, "source": [ "## Layers\n", "\n", @@ -829,8 +947,10 @@ }, { "cell_type": "markdown", - "id": "e37b3844", - "metadata": {}, + "id": "b4a3815d", + "metadata": { + "editable": true + }, "source": [ "## Weights and biases\n", "\n", @@ -848,8 +968,11 @@ { "cell_type": "code", "execution_count": 3, - "id": "3d909fc7", - "metadata": {}, + "id": "5c7ae6ce", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "# building our neural network\n", @@ -871,8 +994,10 @@ }, { "cell_type": "markdown", - "id": "b89c2d9f", - "metadata": {}, + "id": "bc289dbd", + "metadata": { + "editable": true + }, "source": [ "## Feed-forward pass\n", "\n", @@ -897,8 +1022,10 @@ }, { "cell_type": "markdown", - "id": "435c0ced", - "metadata": {}, + "id": "3e93f012", + "metadata": { + "editable": true + }, "source": [ "## Matrix multiplications\n", "\n", @@ -932,8 +1059,11 @@ { "cell_type": "code", "execution_count": 4, - "id": "3037d7ab", - "metadata": {}, + "id": "31084597", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [ { "name": "stdout", @@ -992,8 +1122,10 @@ }, { "cell_type": "markdown", - "id": "61c33a3f", - "metadata": {}, + "id": "93ca9a82", + "metadata": { + "editable": true + }, "source": [ "## Choose cost function and optimizer\n", "\n", @@ -1021,8 +1153,10 @@ }, { "cell_type": "markdown", - "id": "665f44ff", - "metadata": {}, + "id": "59ad4e01", + "metadata": { + "editable": true + }, "source": [ "## Optimizing the cost function\n", "\n", @@ -1057,8 +1191,10 @@ }, { "cell_type": "markdown", - "id": "2d0168d0", - "metadata": {}, + "id": "d017d149", + "metadata": { + "editable": true + }, "source": [ "## Regularization\n", "\n", @@ -1089,8 +1225,10 @@ }, { "cell_type": "markdown", - "id": "9c0a8db3", - "metadata": {}, + "id": "3b624b6e", + "metadata": { + "editable": true + }, "source": [ "## Matrix multiplication\n", "\n", @@ -1128,8 +1266,11 @@ { "cell_type": "code", "execution_count": 5, - "id": "0bf3739e", - "metadata": {}, + "id": "39eabb7a", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [ { "name": "stdout", @@ -1142,7 +1283,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -1227,8 +1368,10 @@ }, { "cell_type": "markdown", - "id": "33e198f3", - "metadata": {}, + "id": "22c14a38", + "metadata": { + "editable": true + }, "source": [ "## Improving performance\n", "\n", @@ -1246,8 +1389,10 @@ }, { "cell_type": "markdown", - "id": "932f6c5e", - "metadata": {}, + "id": "33d33cf6", + "metadata": { + "editable": true + }, "source": [ "## Full object-oriented implementation\n", "\n", @@ -1258,8 +1403,11 @@ { "cell_type": "code", "execution_count": 6, - "id": "91e351de", - "metadata": {}, + "id": "a5009498", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "class NeuralNetwork:\n", @@ -1365,8 +1513,10 @@ }, { "cell_type": "markdown", - "id": "e8c2feb6", - "metadata": {}, + "id": "68639daa", + "metadata": { + "editable": true + }, "source": [ "## Evaluate model performance on test data\n", "\n", @@ -1382,8 +1532,11 @@ { "cell_type": "code", "execution_count": 7, - "id": "1534af1b", - "metadata": {}, + "id": "487c6612", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [ { "name": "stdout", @@ -1414,8 +1567,10 @@ }, { "cell_type": "markdown", - "id": "85627e28", - "metadata": {}, + "id": "c3b10024", + "metadata": { + "editable": true + }, "source": [ "## Adjust hyperparameters\n", "\n", @@ -1426,8 +1581,11 @@ { "cell_type": "code", "execution_count": 8, - "id": "19382903", - "metadata": {}, + "id": "7ab55f7a", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [ { "name": "stdout", @@ -1713,7 +1871,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -1731,7 +1889,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -1749,7 +1907,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -1767,7 +1925,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -1785,7 +1943,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -1803,7 +1961,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -1821,7 +1979,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -1839,11 +1997,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -1861,11 +2019,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -1883,11 +2041,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -1905,11 +2063,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -1927,11 +2085,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -1949,7 +2107,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -1967,11 +2125,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -1989,11 +2147,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -2011,11 +2169,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -2033,11 +2191,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -2055,11 +2213,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -2077,11 +2235,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -2099,11 +2257,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -2121,11 +2279,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -2165,8 +2323,10 @@ }, { "cell_type": "markdown", - "id": "12bc42df", - "metadata": {}, + "id": "aa9d91e2", + "metadata": { + "editable": true + }, "source": [ "## Visualization" ] @@ -2174,22 +2334,25 @@ { "cell_type": "code", "execution_count": 9, - "id": "ec0dc239", - "metadata": {}, + "id": "ce6b84ae", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11123/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19440/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -2260,8 +2423,10 @@ }, { "cell_type": "markdown", - "id": "4dd39506", - "metadata": {}, + "id": "1d50ccf4", + "metadata": { + "editable": true + }, "source": [ "## scikit-learn implementation\n", "\n", @@ -2281,8 +2446,11 @@ { "cell_type": "code", "execution_count": 10, - "id": "d9dbb807", - "metadata": {}, + "id": "05cc9271", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [ { "name": "stderr", @@ -2787,20 +2955,10 @@ "Learning rate = 1.0\n", "Lambda = 1e-05\n", "Accuracy score on test set: 0.08611111111111111\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "\n", "Learning rate = 1.0\n", "Lambda = 0.0001\n", "Accuracy score on test set: 0.10555555555555556\n", - "\n", - "Learning rate = 1.0\n", - "Lambda = 0.001\n", - "Accuracy score on test set: 0.10555555555555556\n", "\n" ] }, @@ -2808,6 +2966,10 @@ "name": "stdout", "output_type": "stream", "text": [ + "Learning rate = 1.0\n", + "Lambda = 0.001\n", + "Accuracy score on test set: 0.10555555555555556\n", + "\n", "Learning rate = 1.0\n", "Lambda = 0.01\n", "Accuracy score on test set: 0.17777777777777778\n", @@ -2815,24 +2977,20 @@ "Learning rate = 1.0\n", "Lambda = 0.1\n", "Accuracy score on test set: 0.08333333333333333\n", - "\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Learning rate = 1.0\n", "Lambda = 1.0\n", "Accuracy score on test set: 0.08888888888888889\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "\n", "Learning rate = 1.0\n", "Lambda = 10.0\n", "Accuracy score on test set: 0.09444444444444444\n", - "\n", - "Learning rate = 10.0\n", - "Lambda = 1e-05\n", - "Accuracy score on test set: 0.17222222222222222\n", "\n" ] }, @@ -2840,6 +2998,10 @@ "name": "stdout", "output_type": "stream", "text": [ + "Learning rate = 10.0\n", + "Lambda = 1e-05\n", + "Accuracy score on test set: 0.17222222222222222\n", + "\n", "Learning rate = 10.0\n", "Lambda = 0.0001\n", "Accuracy score on test set: 0.11666666666666667\n", @@ -2900,8 +3062,10 @@ }, { "cell_type": "markdown", - "id": "214af3ab", - "metadata": {}, + "id": "9f51a74d", + "metadata": { + "editable": true + }, "source": [ "## Visualization" ] @@ -2909,8 +3073,11 @@ { "cell_type": "code", "execution_count": 11, - "id": "d57415ac", - "metadata": {}, + "id": "38a896b8", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [ { "data": { @@ -2980,8 +3147,10 @@ }, { "cell_type": "markdown", - "id": "fe7af77c", - "metadata": {}, + "id": "f8ee3eb3", + "metadata": { + "editable": true + }, "source": [ "## Testing our code for the XOR, OR and AND gates\n", "\n", @@ -3005,8 +3174,10 @@ }, { "cell_type": "markdown", - "id": "7e12b1cf", - "metadata": {}, + "id": "5c0e406c", + "metadata": { + "editable": true + }, "source": [ "## The AND and XOR Gates\n", "\n", @@ -3041,8 +3212,10 @@ }, { "cell_type": "markdown", - "id": "4b5002b4", - "metadata": {}, + "id": "f52ee7dd", + "metadata": { + "editable": true + }, "source": [ "## Representing the Data Sets\n", "\n", @@ -3051,8 +3224,10 @@ }, { "cell_type": "markdown", - "id": "a44df1a3", - "metadata": {}, + "id": "f2634e6f", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\boldsymbol{X}=\\begin{bmatrix} 0 & 0 \\\\\n", @@ -3064,16 +3239,20 @@ }, { "cell_type": "markdown", - "id": "acdb4e08", - "metadata": {}, + "id": "a39715fe", + "metadata": { + "editable": true + }, "source": [ "while the vector of outputs is $\\boldsymbol{y}^T=[0,1,1,0]$ for the XOR gate, $\\boldsymbol{y}^T=[0,0,0,1]$ for the AND gate and $\\boldsymbol{y}^T=[0,1,1,1]$ for the OR gate." ] }, { "cell_type": "markdown", - "id": "0567fd0f", - "metadata": {}, + "id": "8bff01b2", + "metadata": { + "editable": true + }, "source": [ "## Setting up the Neural Network\n", "\n", @@ -3083,8 +3262,11 @@ { "cell_type": "code", "execution_count": 12, - "id": "412401df", - "metadata": {}, + "id": "9d94da1e", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [ { "name": "stdout", @@ -3168,16 +3350,20 @@ }, { "cell_type": "markdown", - "id": "53c52dab", - "metadata": {}, + "id": "fdbce6ba", + "metadata": { + "editable": true + }, "source": [ "Not an impressive result, but this was our first forward pass with randomly assigned weights. Let us now add the full network with the back-propagation algorithm discussed above." ] }, { "cell_type": "markdown", - "id": "7d192a02", - "metadata": {}, + "id": "f5cbb06d", + "metadata": { + "editable": true + }, "source": [ "## The Code using Scikit-Learn" ] @@ -3185,8 +3371,11 @@ { "cell_type": "code", "execution_count": 13, - "id": "766d5af6", - "metadata": {}, + "id": "edf69ad3", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [ { "name": "stdout", @@ -3355,8 +3544,47 @@ "Learning rate = 1.0\n", "Lambda = 1.0\n", "Accuracy score on data set: 0.5\n", - "\n", - "Learning rate = 1.0\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n", + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n", + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n", + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n", + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n", + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n", + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n", + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n", + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n", + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1.0\n", "Lambda = 10.0\n", "Accuracy score on data set: 0.5\n", "\n", @@ -3390,32 +3618,6 @@ "\n" ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, { "data": { "image/png": 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\n", @@ -3425,7 +3627,7 @@ }, "metadata": { "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week42_88_2.png" + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week42_88_4.png" } }, "output_type": "display_data" @@ -3493,8 +3695,10 @@ }, { "cell_type": "markdown", - "id": "9b182ae1", - "metadata": {}, + "id": "d5e5d8a0", + "metadata": { + "editable": true + }, "source": [ "## Building neural networks in Tensorflow and Keras\n", "\n", @@ -3509,8 +3713,10 @@ }, { "cell_type": "markdown", - "id": "60683ec5", - "metadata": {}, + "id": "8326a878", + "metadata": { + "editable": true + }, "source": [ "## Tensorflow\n", "\n", @@ -3542,8 +3748,11 @@ { "cell_type": "code", "execution_count": 14, - "id": "8a0c6901", - "metadata": {}, + "id": "dd988cfc", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [ { "ename": "SyntaxError", @@ -3560,8 +3769,10 @@ }, { "cell_type": "markdown", - "id": "b66e0227", - "metadata": {}, + "id": "d2f8d6f3", + "metadata": { + "editable": true + }, "source": [ "and/or if you use **anaconda**, just write (or install from the graphical user interface)\n", "(current release of CPU-only TensorFlow)" @@ -3570,8 +3781,11 @@ { "cell_type": "code", "execution_count": 15, - "id": "df994f58", - "metadata": {}, + "id": "fdaffccc", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "conda create -n tf tensorflow\n", @@ -3580,8 +3794,10 @@ }, { "cell_type": "markdown", - "id": "b9005559", - "metadata": {}, + "id": "80db034a", + "metadata": { + "editable": true + }, "source": [ "To install the current release of GPU TensorFlow" ] @@ -3589,8 +3805,11 @@ { "cell_type": "code", "execution_count": 16, - "id": "7287b5eb", - "metadata": {}, + "id": "e632c541", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "conda create -n tf-gpu tensorflow-gpu\n", @@ -3599,8 +3818,10 @@ }, { "cell_type": "markdown", - "id": "c066b083", - "metadata": {}, + "id": "605ac1fd", + "metadata": { + "editable": true + }, "source": [ "## Using Keras\n", "\n", @@ -3612,8 +3833,11 @@ { "cell_type": "code", "execution_count": 17, - "id": "6582adea", - "metadata": {}, + "id": "11e69ce0", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "conda install keras" @@ -3621,8 +3845,10 @@ }, { "cell_type": "markdown", - "id": "7d305596", - "metadata": {}, + "id": "176bf3ac", + "metadata": { + "editable": true + }, "source": [ "You can look up the [instructions here](https://keras.io/) for more information.\n", "\n", @@ -3631,8 +3857,10 @@ }, { "cell_type": "markdown", - "id": "a4508850", - "metadata": {}, + "id": "7a085449", + "metadata": { + "editable": true + }, "source": [ "## Collect and pre-process data\n", "\n", @@ -3642,8 +3870,11 @@ { "cell_type": "code", "execution_count": 18, - "id": "5f2256f6", - "metadata": {}, + "id": "d8bae540", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "# import necessary packages\n", @@ -3694,8 +3925,11 @@ { "cell_type": "code", "execution_count": 19, - "id": "a5dfa0e9", - "metadata": {}, + "id": "5608d691", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "from tensorflow.keras.layers import Input\n", @@ -3720,8 +3954,11 @@ { "cell_type": "code", "execution_count": 20, - "id": "dd935ce0", - "metadata": {}, + "id": "7bce7422", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "\n", @@ -3747,8 +3984,11 @@ { "cell_type": "code", "execution_count": 21, - "id": "67158cb2", - "metadata": {}, + "id": "65a68468", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "DNN_keras = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)\n", @@ -3771,8 +4011,11 @@ { "cell_type": "code", "execution_count": 22, - "id": "86d74ee3", - "metadata": {}, + "id": "45ae200f", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "# optional\n", @@ -3810,8 +4053,10 @@ }, { "cell_type": "markdown", - "id": "563d3f68", - "metadata": {}, + "id": "b955ad39", + "metadata": { + "editable": true + }, "source": [ "## The Breast Cancer Data, now with Keras" ] @@ -3819,8 +4064,11 @@ { "cell_type": "code", "execution_count": 23, - "id": "34e6467a", - "metadata": {}, + "id": "8ed2e257", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "\n", @@ -3993,8 +4241,10 @@ }, { "cell_type": "markdown", - "id": "09879108", - "metadata": {}, + "id": "107bab25", + "metadata": { + "editable": true + }, "source": [ "## Fine-tuning neural network hyperparameters\n", "\n", @@ -4019,8 +4269,10 @@ }, { "cell_type": "markdown", - "id": "f8ec1769", - "metadata": {}, + "id": "53ad43ce", + "metadata": { + "editable": true + }, "source": [ "## Hidden layers\n", "\n", @@ -4040,8 +4292,10 @@ }, { "cell_type": "markdown", - "id": "43cc1fe5", - "metadata": {}, + "id": "6f613497", + "metadata": { + "editable": true + }, "source": [ "## Which activation function should I use?\n", "\n", @@ -4069,8 +4323,10 @@ }, { "cell_type": "markdown", - "id": "a9cbce9f", - "metadata": {}, + "id": "91843fee", + "metadata": { + "editable": true + }, "source": [ "## Is the Logistic activation function (Sigmoid) our choice?\n", "\n", @@ -4099,8 +4355,10 @@ }, { "cell_type": "markdown", - "id": "2dfb3f9a", - "metadata": {}, + "id": "83535426", + "metadata": { + "editable": true + }, "source": [ "## The derivative of the Logistic funtion\n", "\n", @@ -4135,8 +4393,10 @@ }, { "cell_type": "markdown", - "id": "f806c047", - "metadata": {}, + "id": "37bdca60", + "metadata": { + "editable": true + }, "source": [ "## The RELU function family\n", "\n", @@ -4158,8 +4418,10 @@ }, { "cell_type": "markdown", - "id": "ef9e2a08", - "metadata": {}, + "id": "11ae19d2", + "metadata": { + "editable": true + }, "source": [ "$$\n", "ELU(z) = \\left\\{\\begin{array}{cc} \\alpha\\left( \\exp{(z)}-1\\right) & z < 0,\\\\ z & z \\ge 0.\\end{array}\\right.\n", @@ -4168,8 +4430,10 @@ }, { "cell_type": "markdown", - "id": "2e2750d8", - "metadata": {}, + "id": "f3a54f08", + "metadata": { + "editable": true + }, "source": [ "## Which activation function should we use?\n", "\n", @@ -4189,8 +4453,10 @@ }, { "cell_type": "markdown", - "id": "4e566f13", - "metadata": {}, + "id": "4dd226db", + "metadata": { + "editable": true + }, "source": [ "## More on activation functions, output layers\n", "\n", @@ -4207,8 +4473,10 @@ }, { "cell_type": "markdown", - "id": "03205666", - "metadata": {}, + "id": "f9521d8f", + "metadata": { + "editable": true + }, "source": [ "## Batch Normalization\n", "\n", @@ -4227,8 +4495,10 @@ }, { "cell_type": "markdown", - "id": "ad7c3e53", - "metadata": {}, + "id": "080c7f12", + "metadata": { + "editable": true + }, "source": [ "## Dropout\n", "\n", @@ -4243,8 +4513,10 @@ }, { "cell_type": "markdown", - "id": "c3b98a7c", - "metadata": {}, + "id": "963e7d21", + "metadata": { + "editable": true + }, "source": [ "## Gradient Clipping\n", "\n", @@ -4260,8 +4532,10 @@ }, { "cell_type": "markdown", - "id": "e7f21477", - "metadata": {}, + "id": "0b69f45e", + "metadata": { + "editable": true + }, "source": [ "## A very nice website on Neural Networks\n", "\n", @@ -4270,8 +4544,10 @@ }, { "cell_type": "markdown", - "id": "54968291", - "metadata": {}, + "id": "3585dfbf", + "metadata": { + "editable": true + }, "source": [ "## A top-down perspective on Neural networks\n", "\n", @@ -4313,8 +4589,10 @@ }, { "cell_type": "markdown", - "id": "4500b85e", - "metadata": {}, + "id": "dd7c4575", + "metadata": { + "editable": true + }, "source": [ "## Limitations of supervised learning with deep networks\n", "\n", @@ -4341,11 +4619,6 @@ } ], "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, "language_info": { "codemirror_mode": { "name": "ipython", diff --git a/doc/LectureNotes/_build/jupyter_execute/week42.py b/doc/LectureNotes/_build/jupyter_execute/week42.py index e0de0fafb..c97e0434c 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week42.py +++ b/doc/LectureNotes/_build/jupyter_execute/week42.py @@ -30,6 +30,10 @@ # # * These lecture notes # +# * [Video of lecture](https://youtu.be/0q5-PhovchQ) +# +# * [Whiteboard notes](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesOct19.pdf) +# # * [Aurelien Geron's chapters 10-11](https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf) # # * For a more in depth discussion on neural networks we recommend Goodfellow et al chapters 6 and 7. diff --git a/doc/LectureNotes/_build/jupyter_execute/week43.ipynb b/doc/LectureNotes/_build/jupyter_execute/week43.ipynb index 708272f23..9a7520f62 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week43.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/week43.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "082f351e", + "id": "42ce64ba", "metadata": { "editable": true }, @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "a99ac46a", + "id": "c987b86b", "metadata": { "editable": true }, @@ -29,7 +29,7 @@ }, { "cell_type": "markdown", - "id": "a65014fa", + "id": "0dc0d6ca", "metadata": { "editable": true }, @@ -54,6 +54,8 @@ "\n", " * Solving differential equations with Neural Networks and intro to **Tensorflow** with examples.\n", "\n", + " * [Video of lecture](https://youtu.be/_-AwbBh4G-8)\n", + "\n", " * Readings and Videos:\n", "\n", " * These lecture notes\n", @@ -75,7 +77,7 @@ }, { "cell_type": "markdown", - "id": "491f86f0", + "id": "f4d3253b", "metadata": { "editable": true }, @@ -89,7 +91,7 @@ }, { "cell_type": "markdown", - "id": "9878a552", + "id": "9744596b", "metadata": { "editable": true }, @@ -106,7 +108,7 @@ }, { "cell_type": "markdown", - "id": "acd9eaf4", + "id": "bd189f93", "metadata": { "editable": true }, @@ -116,7 +118,7 @@ }, { "cell_type": "markdown", - "id": "ac6985dd", + "id": "e901e3d4", "metadata": { "editable": true }, @@ -153,7 +155,7 @@ }, { "cell_type": "markdown", - "id": "c75ca508", + "id": "fca381f9", "metadata": { "editable": true }, @@ -191,7 +193,7 @@ }, { "cell_type": "markdown", - "id": "6b4c09b9", + "id": "f0252121", "metadata": { "editable": true }, @@ -203,7 +205,7 @@ }, { "cell_type": "markdown", - "id": "fe419b85", + "id": "f59fc5ee", "metadata": { "editable": true }, @@ -218,7 +220,7 @@ }, { "cell_type": "markdown", - "id": "79ddb224", + "id": "1d74ec2a", "metadata": { "editable": true }, @@ -248,7 +250,7 @@ }, { "cell_type": "markdown", - "id": "5b9b5e41", + "id": "8e6565f3", "metadata": { "editable": true }, @@ -260,7 +262,7 @@ }, { "cell_type": "markdown", - "id": "c319cc25", + "id": "7f55beb5", "metadata": { "editable": true }, @@ -273,7 +275,7 @@ }, { "cell_type": "markdown", - "id": "edf4b6c5", + "id": "ba515e4f", "metadata": { "editable": true }, @@ -283,7 +285,7 @@ }, { "cell_type": "markdown", - "id": "521c408b", + "id": "1007d026", "metadata": { "editable": true }, @@ -298,7 +300,7 @@ }, { "cell_type": "markdown", - "id": "22aa491a", + "id": "b1ee686a", "metadata": { "editable": true }, @@ -308,7 +310,7 @@ }, { "cell_type": "markdown", - "id": "f0ae234a", + "id": "2820cfdd", "metadata": { "editable": true }, @@ -321,7 +323,7 @@ }, { "cell_type": "markdown", - "id": "5c980f5b", + "id": "5b5f6d64", "metadata": { "editable": true }, @@ -331,7 +333,7 @@ }, { "cell_type": "markdown", - "id": "82fb504e", + "id": "0814e6d9", "metadata": { "editable": true }, @@ -346,7 +348,7 @@ }, { "cell_type": "markdown", - "id": "cfd54537", + "id": "9da10b99", "metadata": { "editable": true }, @@ -356,7 +358,7 @@ }, { "cell_type": "markdown", - "id": "2f823c42", + "id": "c0f3ede6", "metadata": { "editable": true }, @@ -371,7 +373,7 @@ }, { "cell_type": "markdown", - "id": "7cad12ae", + "id": "f00bc3ad", "metadata": { "editable": true }, @@ -381,7 +383,7 @@ }, { "cell_type": "markdown", - "id": "0e9795a8", + "id": "da749419", "metadata": { "editable": true }, @@ -394,7 +396,7 @@ }, { "cell_type": "markdown", - "id": "4ee23a12", + "id": "2e9135f9", "metadata": { "editable": true }, @@ -404,7 +406,7 @@ }, { "cell_type": "markdown", - "id": "1c156334", + "id": "a0d879df", "metadata": { "editable": true }, @@ -420,7 +422,7 @@ }, { "cell_type": "markdown", - "id": "24433060", + "id": "988a9a20", "metadata": { "editable": true }, @@ -430,7 +432,7 @@ }, { "cell_type": "markdown", - "id": "e2664fe2", + "id": "354ad4af", "metadata": { "editable": true }, @@ -443,7 +445,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "4966b41e", + "id": "1f04aac4", "metadata": { "collapsed": false, "editable": true @@ -524,7 +526,7 @@ }, { "cell_type": "markdown", - "id": "2d227b7a", + "id": "6e12253c", "metadata": { "editable": true }, @@ -534,7 +536,7 @@ }, { "cell_type": "markdown", - "id": "4a614a2f", + "id": "4ca80040", "metadata": { "editable": true }, @@ -545,7 +547,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "a2401c4c", + "id": "1b657a96", "metadata": { "collapsed": false, "editable": true @@ -578,7 +580,13 @@ "Learning rate = 1e-05\n", "Lambda = 1.0\n", "Accuracy score on data set: 0.5\n", - "\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Learning rate = 1e-05\n", "Lambda = 10.0\n", "Accuracy score on data set: 0.5\n", @@ -788,7 +796,7 @@ }, "metadata": { "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week43_30_2.png" + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week43_30_3.png" } }, "output_type": "display_data" @@ -853,7 +861,7 @@ }, { "cell_type": "markdown", - "id": "2b0654f9", + "id": "9f870fcf", "metadata": { "editable": true }, @@ -872,7 +880,7 @@ }, { "cell_type": "markdown", - "id": "36d7ed90", + "id": "75623483", "metadata": { "editable": true }, @@ -894,7 +902,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "f3149e44", + "id": "7884cf44", "metadata": { "collapsed": false, "editable": true @@ -1035,7 +1043,7 @@ }, { "cell_type": "markdown", - "id": "3b714e5a", + "id": "a9747db3", "metadata": { "editable": true }, @@ -1051,7 +1059,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "a187e1f4", + "id": "b394ebc2", "metadata": { "collapsed": false, "editable": true @@ -1064,7 +1072,7 @@ }, { "cell_type": "markdown", - "id": "14693dc1", + "id": "734ce228", "metadata": { "editable": true }, @@ -1076,7 +1084,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "6f791e05", + "id": "51396f90", "metadata": { "collapsed": false, "editable": true @@ -1114,7 +1122,7 @@ }, { "cell_type": "markdown", - "id": "02dca912", + "id": "44646fc6", "metadata": { "editable": true }, @@ -1130,7 +1138,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "8e150ca8", + "id": "912285f7", "metadata": { "collapsed": false, "editable": true @@ -1168,7 +1176,7 @@ }, { "cell_type": "markdown", - "id": "a2ba6469", + "id": "9297b955", "metadata": { "editable": true }, @@ -1181,7 +1189,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "2fd8a7d1", + "id": "39943643", "metadata": { "collapsed": false, "editable": true @@ -1213,7 +1221,7 @@ }, { "cell_type": "markdown", - "id": "811b2be7", + "id": "53f4d1d2", "metadata": { "editable": true }, @@ -1229,7 +1237,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "12fb8899", + "id": "90222b1a", "metadata": { "collapsed": false, "editable": true @@ -1287,7 +1295,7 @@ }, { "cell_type": "markdown", - "id": "9cd08f7a", + "id": "f3dd3611", "metadata": { "editable": true }, @@ -1302,7 +1310,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "3b609fd5", + "id": "ded43dca", "metadata": { "collapsed": false, "editable": true @@ -1344,7 +1352,7 @@ }, { "cell_type": "markdown", - "id": "7b55fa66", + "id": "771c53a1", "metadata": { "editable": true }, @@ -1368,7 +1376,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "c0f9ff63", + "id": "56ef870b", "metadata": { "collapsed": false, "editable": true @@ -1840,7 +1848,7 @@ }, { "cell_type": "markdown", - "id": "e406c59d", + "id": "cefab2ac", "metadata": { "editable": true }, @@ -1852,7 +1860,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "0c6d743d", + "id": "6d5b49a1", "metadata": { "collapsed": false, "editable": true @@ -1896,7 +1904,7 @@ }, { "cell_type": "markdown", - "id": "150ef783", + "id": "f857ae2b", "metadata": { "editable": true }, @@ -1912,7 +1920,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "585ea4aa", + "id": "2a92f970", "metadata": { "collapsed": false, "editable": true @@ -1927,7 +1935,7 @@ }, { "cell_type": "markdown", - "id": "169932be", + "id": "28236066", "metadata": { "editable": true }, @@ -1938,7 +1946,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "7559a5a7", + "id": "b3c6c87e", "metadata": { "collapsed": false, "editable": true @@ -2771,7 +2779,7 @@ }, { "cell_type": "markdown", - "id": "ee912b2a", + "id": "f5b6209f", "metadata": { "editable": true }, @@ -2787,7 +2795,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "b576d527", + "id": "59d5742b", "metadata": { "collapsed": false, "editable": true @@ -10819,7 +10827,7 @@ }, { "cell_type": "markdown", - "id": "e98b0dd4", + "id": "13e834ae", "metadata": { "editable": true }, @@ -10834,7 +10842,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "2508ac64", + "id": "ab390311", "metadata": { "collapsed": false, "editable": true @@ -10860,7 +10868,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "3b12683c", + "id": "f2d67c2e", "metadata": { "collapsed": false, "editable": true @@ -10875,7 +10883,7 @@ }, { "cell_type": "markdown", - "id": "e79ca9e9", + "id": "e350f60e", "metadata": { "editable": true }, @@ -10886,7 +10894,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "2238df76", + "id": "211a328d", "metadata": { "collapsed": false, "editable": true @@ -10896,14 +10904,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Adam: Eta=0.001, Lambda=0" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" + "Adam: Eta=0.001, Lambda=0\n" ] }, { @@ -18932,7 +18933,7 @@ }, { "cell_type": "markdown", - "id": "f698a332", + "id": "8425a8fb", "metadata": { "editable": true }, @@ -18943,7 +18944,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "467be649", + "id": "1070e9eb", "metadata": { "collapsed": false, "editable": true @@ -18963,7 +18964,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "65fb9800", + "id": "c7171327", "metadata": { "collapsed": false, "editable": true @@ -22289,14 +22290,11 @@ ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "Exception ignored in: \n", - "Traceback (most recent call last):\n", - " File \"/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/zmq/sugar/socket.py\", line 112, in __del__\n", - " warn(\n", - "ResourceWarning: unclosed socket \n" + "\r", + " [===============>------------------------] 41.40% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881 " ] }, { @@ -22304,7 +22302,7 @@ "output_type": "stream", "text": [ "\r", - " " + " [===============>------------------------] 41.50% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888 " ] }, { @@ -22312,7 +22310,4687 @@ "output_type": "stream", "text": [ "\r", - " [=======================================>] 100.0% " + " [===============>------------------------] 41.60% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " [===============>------------------------] 41.70% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " [===============>------------------------] 41.80% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " [===============>------------------------] 41.90% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.17 | val_acc: 0.895 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " [===============>------------------------] 42.00% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " [===============>------------------------] 42.10% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.17 | val_acc: 0.895 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " [===============>------------------------] 42.20% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " [===============>------------------------] 42.30% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " [===============>------------------------] 42.40% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " [================>-----------------------] 42.50% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " [================>-----------------------] 42.60% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " 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"stream", + "text": [ + "\r", + " [=======================================>] 100.0% | train_error: 0.175 | train_acc: 0.983 " + ] + } + ], "source": [ "from sklearn.datasets import load_digits\n", "\n", @@ -22377,7 +35079,7 @@ }, { "cell_type": "markdown", - "id": "00f7684a", + "id": "98281e67", "metadata": { "editable": true }, @@ -22390,12 +35092,8030 @@ { "cell_type": "code", "execution_count": 21, - "id": "8bec151f", + "id": "24031566", "metadata": { "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Adam: Eta=0.1, Lambda=0\n", + "\r", + " [----------------------------------------] 0.000% | train_error: 10.4 | train_acc: 0.500 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " [----------------------------------------] 0.1000% | train_error: 10.4 | train_acc: 0.500 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " 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train_acc: 1.00 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " [======================================>-] 98.70% | train_error: -0.0000000010 | train_acc: 1.00 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " [======================================>-] 98.80% | train_error: -0.0000000010 | train_acc: 1.00 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " [======================================>-] 98.90% | train_error: -0.0000000010 | train_acc: 1.00 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " [======================================>-] 99.00% | train_error: -0.0000000010 | train_acc: 1.00 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " [======================================>-] 99.10% | train_error: -0.0000000010 | train_acc: 1.00 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " [======================================>-] 99.20% | train_error: -0.0000000010 | train_acc: 1.00 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " [======================================>-] 99.30% | train_error: -0.0000000010 | train_acc: 1.00 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " [======================================>-] 99.40% | train_error: -0.0000000010 | train_acc: 1.00 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " [======================================>-] 99.50% | train_error: -0.0000000010 | train_acc: 1.00 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " [======================================>-] 99.60% | train_error: -0.0000000010 | train_acc: 1.00 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " [======================================>-] 99.70% | train_error: -0.0000000010 | train_acc: 1.00 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " [======================================>-] 99.80% | train_error: -0.0000000010 | train_acc: 1.00 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " [======================================>-] 99.90% | train_error: -0.0000000010 | train_acc: 1.00 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " [=======================================>] 100.0% | train_error: -0.0000000010 | train_acc: 1.00 " + ] + } + ], "source": [ "X = np.array([ [0, 0], [0, 1], [1, 0],[1, 1]],dtype=np.float64)\n", "\n", @@ -22413,7 +43133,7 @@ }, { "cell_type": "markdown", - "id": "c32f02fd", + "id": "8302830f", "metadata": { "editable": true }, @@ -22423,7 +43143,7 @@ }, { "cell_type": "markdown", - "id": "3434c341", + "id": "9003b71a", "metadata": { "editable": true }, @@ -22433,7 +43153,7 @@ }, { "cell_type": "markdown", - "id": "aea3ed79", + "id": "4446e61e", "metadata": { "editable": true }, @@ -22462,7 +43182,7 @@ }, { "cell_type": "markdown", - "id": "9aa79782", + "id": "de6e80a8", "metadata": { "editable": true }, @@ -22512,12 +43232,36 @@ { "cell_type": "code", "execution_count": 22, - "id": "cade684e", + "id": "011b021a", "metadata": { "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "inputs = (n_inputs, pixel_width, pixel_height) = (1797, 8, 8)\n", + "labels = (n_inputs) = (1797,)\n", + "X = (n_inputs, n_features) = (1797, 64)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
            " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week43_72_1.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "# import necessary packages\n", "import numpy as np\n", @@ -22565,7 +43309,7 @@ }, { "cell_type": "markdown", - "id": "380d9d9d", + "id": "4de43fe8", "metadata": { "editable": true }, @@ -22586,12 +43330,21 @@ { "cell_type": "code", "execution_count": 23, - "id": "73cf1180", + "id": "d9281e06", "metadata": { "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of training images: 1437\n", + "Number of test images: 360\n" + ] + } + ], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", @@ -22624,7 +43377,7 @@ }, { "cell_type": "markdown", - "id": "8a01257a", + "id": "5d86b543", "metadata": { "editable": true }, @@ -22668,7 +43421,7 @@ }, { "cell_type": "markdown", - "id": "54e95850", + "id": "02df2616", "metadata": { "editable": true }, @@ -22708,7 +43461,7 @@ }, { "cell_type": "markdown", - "id": "62c1a1b0", + "id": "0a17cfeb", "metadata": { "editable": true }, @@ -22729,7 +43482,7 @@ { "cell_type": "code", "execution_count": 24, - "id": "aa587add", + "id": "94dd542a", "metadata": { "collapsed": false, "editable": true @@ -22755,7 +43508,7 @@ }, { "cell_type": "markdown", - "id": "d4249afa", + "id": "7c13f0a8", "metadata": { "editable": true }, @@ -22783,7 +43536,7 @@ }, { "cell_type": "markdown", - "id": "b699e117", + "id": "d1bae3d4", "metadata": { "editable": true }, @@ -22820,12 +43573,49 @@ { "cell_type": "code", "execution_count": 25, - "id": "8a4dec51", + "id": "20c03478", "metadata": { "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "probabilities = (n_inputs, n_categories) = (1437, 10)" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "probability that image 0 is in category 0,1,2,...,9 = \n", + "[5.41511965e-04 2.17174962e-03 8.84355903e-03 1.44970586e-03\n", + " 1.10378326e-04 5.08318298e-09 2.03256632e-04 1.92507116e-03\n", + " 9.84443254e-01 3.11507992e-04]\n", + "probabilities sum up to: 1.0\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "predictions = (n_inputs) = (1437,)" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "prediction for image 0: 8\n", + "correct label for image 0: 6\n" + ] + } + ], "source": [ "# setup the feed-forward pass, subscript h = hidden layer\n", "\n", @@ -22866,7 +43656,7 @@ }, { "cell_type": "markdown", - "id": "c2fbafa2", + "id": "42c28741", "metadata": { "editable": true }, @@ -22897,7 +43687,7 @@ }, { "cell_type": "markdown", - "id": "297dd16a", + "id": "62954b00", "metadata": { "editable": true }, @@ -22935,7 +43725,7 @@ }, { "cell_type": "markdown", - "id": "9a4bdbd5", + "id": "9f94aff8", "metadata": { "editable": true }, @@ -22969,7 +43759,7 @@ }, { "cell_type": "markdown", - "id": "aa585f10", + "id": "bf73f8e5", "metadata": { "editable": true }, @@ -23010,12 +43800,34 @@ { "cell_type": "code", "execution_count": 26, - "id": "21ec5e9c", + "id": "9ffb0561", "metadata": { "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Old accuracy on training data: 0.1440501043841336\n" + ] + }, + { + "ename": "RuntimeWarning", + "evalue": "overflow encountered in exp", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeWarning\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [26]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 53\u001b[0m lmbd \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.01\u001b[39m\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m1000\u001b[39m):\n\u001b[1;32m 55\u001b[0m \u001b[38;5;66;03m# calculate gradients\u001b[39;00m\n\u001b[0;32m---> 56\u001b[0m dWo, dBo, dWh, dBh \u001b[38;5;241m=\u001b[39m \u001b[43mbackpropagation\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mY_train_onehot\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 58\u001b[0m \u001b[38;5;66;03m# regularization term gradients\u001b[39;00m\n\u001b[1;32m 59\u001b[0m dWo \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m lmbd \u001b[38;5;241m*\u001b[39m output_weights\n", + "Input \u001b[0;32mIn [26]\u001b[0m, in \u001b[0;36mbackpropagation\u001b[0;34m(X, Y)\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mbackpropagation\u001b[39m(X, Y):\n\u001b[0;32m---> 33\u001b[0m a_h, probabilities \u001b[38;5;241m=\u001b[39m \u001b[43mfeed_forward_train\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 35\u001b[0m \u001b[38;5;66;03m# error in the output layer\u001b[39;00m\n\u001b[1;32m 36\u001b[0m error_output \u001b[38;5;241m=\u001b[39m probabilities \u001b[38;5;241m-\u001b[39m Y\n", + "Input \u001b[0;32mIn [26]\u001b[0m, in \u001b[0;36mfeed_forward_train\u001b[0;34m(X)\u001b[0m\n\u001b[1;32m 18\u001b[0m z_h \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmatmul(X, hidden_weights) \u001b[38;5;241m+\u001b[39m hidden_bias\n\u001b[1;32m 19\u001b[0m \u001b[38;5;66;03m# activation in the hidden layer\u001b[39;00m\n\u001b[0;32m---> 20\u001b[0m a_h \u001b[38;5;241m=\u001b[39m \u001b[43msigmoid\u001b[49m\u001b[43m(\u001b[49m\u001b[43mz_h\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;66;03m# weighted sum of inputs to the output layer\u001b[39;00m\n\u001b[1;32m 23\u001b[0m z_o \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmatmul(a_h, output_weights) \u001b[38;5;241m+\u001b[39m output_bias\n", + "Input \u001b[0;32mIn [25]\u001b[0m, in \u001b[0;36msigmoid\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msigmoid\u001b[39m(x):\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;241m1\u001b[39m\u001b[38;5;241m/\u001b[39m(\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexp\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m)\n", + "\u001b[0;31mRuntimeWarning\u001b[0m: overflow encountered in exp" + ] + } + ], "source": [ "# to categorical turns our integer vector into a onehot representation\n", "from sklearn.metrics import accuracy_score\n", @@ -23089,7 +43901,7 @@ }, { "cell_type": "markdown", - "id": "fe383853", + "id": "3b19f8e5", "metadata": { "editable": true }, @@ -23110,7 +43922,7 @@ }, { "cell_type": "markdown", - "id": "48afb81d", + "id": "443db3c1", "metadata": { "editable": true }, @@ -23124,7 +43936,7 @@ { "cell_type": "code", "execution_count": 27, - "id": "223c5ea5", + "id": "5a7db6a1", "metadata": { "collapsed": false, "editable": true @@ -23234,7 +44046,7 @@ }, { "cell_type": "markdown", - "id": "8e4de167", + "id": "fe73ada1", "metadata": { "editable": true }, @@ -23253,7 +44065,7 @@ { "cell_type": "code", "execution_count": 28, - "id": "e299478f", + "id": "3c71da93", "metadata": { "collapsed": false, "editable": true @@ -23280,7 +44092,7 @@ }, { "cell_type": "markdown", - "id": "1c58f8b5", + "id": "c6d594b6", "metadata": { "editable": true }, @@ -23294,7 +44106,7 @@ { "cell_type": "code", "execution_count": 29, - "id": "e2e095b5", + "id": "bb8c3746", "metadata": { "collapsed": false, "editable": true @@ -23325,7 +44137,7 @@ }, { "cell_type": "markdown", - "id": "9bcbed3e", + "id": "c460a08f", "metadata": { "editable": true }, @@ -23336,7 +44148,7 @@ { "cell_type": "code", "execution_count": 30, - "id": "70cd9e5f", + "id": "ae408ce7", "metadata": { "collapsed": false, "editable": true @@ -23380,7 +44192,7 @@ }, { "cell_type": "markdown", - "id": "014fa73f", + "id": "255351d8", "metadata": { "editable": true }, @@ -23403,7 +44215,7 @@ { "cell_type": "code", "execution_count": 31, - "id": "f8e412f7", + "id": "45102bf4", "metadata": { "collapsed": false, "editable": true @@ -23430,7 +44242,7 @@ }, { "cell_type": "markdown", - "id": "d05866a6", + "id": "9ed166d7", "metadata": { "editable": true }, @@ -23441,7 +44253,7 @@ { "cell_type": "code", "execution_count": 32, - "id": "1af34a20", + "id": "e9fb271f", "metadata": { "collapsed": false, "editable": true @@ -23486,7 +44298,7 @@ }, { "cell_type": "markdown", - "id": "372abcde", + "id": "6cbb4072", "metadata": { "editable": true }, @@ -23504,7 +44316,7 @@ }, { "cell_type": "markdown", - "id": "6b3fc4a5", + "id": "2702932a", "metadata": { "editable": true }, @@ -23539,7 +44351,7 @@ { "cell_type": "code", "execution_count": 33, - "id": "5f88d47b", + "id": "a66fc7b6", "metadata": { "collapsed": false, "editable": true @@ -23551,7 +44363,7 @@ }, { "cell_type": "markdown", - "id": "fe89b2bd", + "id": "3c99a292", "metadata": { "editable": true }, @@ -23563,7 +44375,7 @@ { "cell_type": "code", "execution_count": 34, - "id": "51009ec1", + "id": "71e9f24b", "metadata": { "collapsed": false, "editable": true @@ -23576,7 +44388,7 @@ }, { "cell_type": "markdown", - "id": "6b00853a", + "id": "a623588f", "metadata": { "editable": true }, @@ -23587,7 +44399,7 @@ { "cell_type": "code", "execution_count": 35, - "id": "27d84846", + "id": "fa19ee75", "metadata": { "collapsed": false, "editable": true @@ -23600,7 +44412,7 @@ }, { "cell_type": "markdown", - "id": "efb95ce2", + "id": "de5fec4a", "metadata": { "editable": true }, @@ -23615,7 +44427,7 @@ { "cell_type": "code", "execution_count": 36, - "id": "ff0d76a6", + "id": "edb61bdb", "metadata": { "collapsed": false, "editable": true @@ -23627,7 +44439,7 @@ }, { "cell_type": "markdown", - "id": "16ceb812", + "id": "fd72dabb", "metadata": { "editable": true }, @@ -23639,7 +44451,7 @@ }, { "cell_type": "markdown", - "id": "d1be0e7f", + "id": "b8262ab5", "metadata": { "editable": true }, @@ -23652,7 +44464,7 @@ { "cell_type": "code", "execution_count": 37, - "id": "3f300c00", + "id": "0b88258b", "metadata": { "collapsed": false, "editable": true @@ -23707,7 +44519,7 @@ { "cell_type": "code", "execution_count": 38, - "id": "fa0d45c3", + "id": "5000582f", "metadata": { "collapsed": false, "editable": true @@ -23736,7 +44548,7 @@ { "cell_type": "code", "execution_count": 39, - "id": "8cdc8534", + "id": "a473cac3", "metadata": { "collapsed": false, "editable": true @@ -23766,7 +44578,7 @@ { "cell_type": "code", "execution_count": 40, - "id": "2fbe350d", + "id": "041ff977", "metadata": { "collapsed": false, "editable": true @@ -23793,7 +44605,7 @@ { "cell_type": "code", "execution_count": 41, - "id": "4eae49aa", + "id": "1821d39d", "metadata": { "collapsed": false, "editable": true @@ -23835,7 +44647,7 @@ }, { "cell_type": "markdown", - "id": "43439e76", + "id": "c8b43dbc", "metadata": { "editable": true }, @@ -23846,7 +44658,7 @@ { "cell_type": "code", "execution_count": 42, - "id": "dabd1a04", + "id": "f902c476", "metadata": { "collapsed": false, "editable": true @@ -24023,7 +44835,7 @@ }, { "cell_type": "markdown", - "id": "23e4f61b", + "id": "fa673f3a", "metadata": { "editable": true }, @@ -24051,7 +44863,7 @@ }, { "cell_type": "markdown", - "id": "c9bfa99f", + "id": "6c4727a9", "metadata": { "editable": true }, @@ -24074,7 +44886,7 @@ }, { "cell_type": "markdown", - "id": "7c5e58d3", + "id": "f24d5df1", "metadata": { "editable": true }, @@ -24105,7 +44917,7 @@ }, { "cell_type": "markdown", - "id": "6b470b84", + "id": "80cd048d", "metadata": { "editable": true }, @@ -24137,7 +44949,7 @@ }, { "cell_type": "markdown", - "id": "370e81e1", + "id": "6863dde1", "metadata": { "editable": true }, @@ -24175,7 +44987,7 @@ }, { "cell_type": "markdown", - "id": "0bdbe74e", + "id": "95f03af7", "metadata": { "editable": true }, @@ -24200,7 +45012,7 @@ }, { "cell_type": "markdown", - "id": "cfa436f9", + "id": "620cac32", "metadata": { "editable": true }, @@ -24212,7 +45024,7 @@ }, { "cell_type": "markdown", - "id": "1b53aede", + "id": "2dd91f71", "metadata": { "editable": true }, @@ -24235,7 +45047,7 @@ }, { "cell_type": "markdown", - "id": "3d2e808b", + "id": "bfa3871c", "metadata": { "editable": true }, @@ -24255,7 +45067,7 @@ }, { "cell_type": "markdown", - "id": "19b958c3", + "id": "9315a3fc", "metadata": { "editable": true }, @@ -24277,7 +45089,7 @@ }, { "cell_type": "markdown", - "id": "1f4fd499", + "id": "85eb5069", "metadata": { "editable": true }, @@ -24295,7 +45107,7 @@ }, { "cell_type": "markdown", - "id": "99d0d4c8", + "id": "d2d08622", "metadata": { "editable": true }, @@ -24314,7 +45126,7 @@ }, { "cell_type": "markdown", - "id": "ee14cc3a", + "id": "566bcca7", "metadata": { "editable": true }, @@ -24326,7 +45138,7 @@ }, { "cell_type": "markdown", - "id": "3f5a367a", + "id": "aedf225e", "metadata": { "editable": true }, @@ -24371,7 +45183,7 @@ }, { "cell_type": "markdown", - "id": "687050b9", + "id": "8723e9ba", "metadata": { "editable": true }, @@ -24401,7 +45213,7 @@ }, { "cell_type": "markdown", - "id": "0ccb8f74", + "id": "b641b576", "metadata": { "editable": true }, @@ -24428,7 +45240,7 @@ }, { "cell_type": "markdown", - "id": "97d510ab", + "id": "e919d2c8", "metadata": { "editable": true }, @@ -24442,7 +45254,7 @@ }, { "cell_type": "markdown", - "id": "fe825851", + "id": "12b84451", "metadata": { "editable": true }, @@ -24459,7 +45271,7 @@ }, { "cell_type": "markdown", - "id": "780538d0", + "id": "88407501", "metadata": { "editable": true }, @@ -24475,7 +45287,7 @@ }, { "cell_type": "markdown", - "id": "f373c843", + "id": "034cef5e", "metadata": { "editable": true }, @@ -24487,7 +45299,7 @@ }, { "cell_type": "markdown", - "id": "72db62ef", + "id": "86693b23", "metadata": { "editable": true }, @@ -24505,7 +45317,7 @@ }, { "cell_type": "markdown", - "id": "9f423f32", + "id": "5c8e1418", "metadata": { "editable": true }, @@ -24526,7 +45338,7 @@ }, { "cell_type": "markdown", - "id": "54daf83b", + "id": "4ca42ad5", "metadata": { "editable": true }, @@ -24543,7 +45355,7 @@ }, { "cell_type": "markdown", - "id": "30a7ebea", + "id": "a0808029", "metadata": { "editable": true }, @@ -24555,7 +45367,7 @@ }, { "cell_type": "markdown", - "id": "a3c57c9f", + "id": "8e242ee9", "metadata": { "editable": true }, @@ -24566,7 +45378,7 @@ }, { "cell_type": "markdown", - "id": "f5f5d8ac", + "id": "6cb613c0", "metadata": { "editable": true }, @@ -24583,7 +45395,7 @@ }, { "cell_type": "markdown", - "id": "1ce283c1", + "id": "ff0ace15", "metadata": { "editable": true }, @@ -24594,7 +45406,7 @@ }, { "cell_type": "markdown", - "id": "cd9ff0af", + "id": "d77df59b", "metadata": { "editable": true }, @@ -24610,7 +45422,7 @@ }, { "cell_type": "markdown", - "id": "28c63a03", + "id": "b34fce14", "metadata": { "editable": true }, @@ -24622,7 +45434,7 @@ }, { "cell_type": "markdown", - "id": "4269d121", + "id": "290ed299", "metadata": { "editable": true }, @@ -24639,7 +45451,7 @@ }, { "cell_type": "markdown", - "id": "6b0e72c3", + "id": "d386881a", "metadata": { "editable": true }, @@ -24651,7 +45463,7 @@ }, { "cell_type": "markdown", - "id": "2295a2d8", + "id": "cf4aca15", "metadata": { "editable": true }, @@ -24669,7 +45481,7 @@ }, { "cell_type": "markdown", - "id": "e5e41afe", + "id": "ee5531c2", "metadata": { "editable": true }, @@ -24679,7 +45491,7 @@ }, { "cell_type": "markdown", - "id": "2150df23", + "id": "64a52bc0", "metadata": { "editable": true }, @@ -24691,7 +45503,7 @@ }, { "cell_type": "markdown", - "id": "0903b255", + "id": "e0154c0f", "metadata": { "editable": true }, @@ -24708,7 +45520,7 @@ }, { "cell_type": "markdown", - "id": "fe2da1fe", + "id": "334e7f10", "metadata": { "editable": true }, @@ -24720,7 +45532,7 @@ }, { "cell_type": "markdown", - "id": "75fea33a", + "id": "66c1d55c", "metadata": { "editable": true }, @@ -24731,7 +45543,7 @@ }, { "cell_type": "markdown", - "id": "297320fa", + "id": "f03c7338", "metadata": { "editable": true }, @@ -24743,7 +45555,7 @@ }, { "cell_type": "markdown", - "id": "7905db70", + "id": "ca114c87", "metadata": { "editable": true }, @@ -24753,7 +45565,7 @@ }, { "cell_type": "markdown", - "id": "7dbc7af5", + "id": "801219a0", "metadata": { "editable": true }, @@ -24773,7 +45585,7 @@ }, { "cell_type": "markdown", - "id": "52a2f0e5", + "id": "569486c4", "metadata": { "editable": true }, @@ -24790,7 +45602,7 @@ }, { "cell_type": "markdown", - "id": "fc2672cb", + "id": "575831d8", "metadata": { "editable": true }, @@ -24809,7 +45621,7 @@ }, { "cell_type": "markdown", - "id": "273c2869", + "id": "f8682123", "metadata": { "editable": true }, @@ -24821,7 +45633,7 @@ }, { "cell_type": "markdown", - "id": "8fae3b2f", + "id": "e62b1005", "metadata": { "editable": true }, @@ -24831,7 +45643,7 @@ }, { "cell_type": "markdown", - "id": "dc98cc83", + "id": "4e33439d", "metadata": { "editable": true }, @@ -24848,7 +45660,7 @@ }, { "cell_type": "markdown", - "id": "dbd04093", + "id": "35159fc5", "metadata": { "editable": true }, @@ -24858,7 +45670,7 @@ }, { "cell_type": "markdown", - "id": "4fed45e1", + "id": "03f74ead", "metadata": { "editable": true }, @@ -24874,7 +45686,7 @@ }, { "cell_type": "markdown", - "id": "5708fcb2", + "id": "3599c2de", "metadata": { "editable": true }, @@ -24886,7 +45698,7 @@ }, { "cell_type": "markdown", - "id": "7997d84c", + "id": "5c50baf7", "metadata": { "editable": true }, @@ -24898,7 +45710,7 @@ }, { "cell_type": "markdown", - "id": "78213356", + "id": "636cb74c", "metadata": { "editable": true }, @@ -24910,7 +45722,7 @@ }, { "cell_type": "markdown", - "id": "27349563", + "id": "b4b301c2", "metadata": { "editable": true }, @@ -24920,7 +45732,7 @@ }, { "cell_type": "markdown", - "id": "2b61bb07", + "id": "5d873576", "metadata": { "editable": true }, @@ -24932,7 +45744,7 @@ }, { "cell_type": "markdown", - "id": "5fe5b8e2", + "id": "b744fd57", "metadata": { "editable": true }, @@ -24949,7 +45761,7 @@ }, { "cell_type": "markdown", - "id": "2de1c60a", + "id": "63290336", "metadata": { "editable": true }, @@ -24959,7 +45771,7 @@ }, { "cell_type": "markdown", - "id": "b65d6687", + "id": "f8790d32", "metadata": { "editable": true }, @@ -24971,7 +45783,7 @@ }, { "cell_type": "markdown", - "id": "ca8a5a81", + "id": "10ab609a", "metadata": { "editable": true }, @@ -24985,7 +45797,7 @@ }, { "cell_type": "markdown", - "id": "b8614294", + "id": "06e2efca", "metadata": { "editable": true }, @@ -25001,7 +45813,7 @@ }, { "cell_type": "markdown", - "id": "76363167", + "id": "bc8a8e04", "metadata": { "editable": true }, @@ -25013,7 +45825,7 @@ }, { "cell_type": "markdown", - "id": "802de3e1", + "id": "6564e278", "metadata": { "editable": true }, @@ -25035,7 +45847,7 @@ }, { "cell_type": "markdown", - "id": "de92874a", + "id": "f62120f5", "metadata": { "editable": true }, @@ -25047,7 +45859,7 @@ }, { "cell_type": "markdown", - "id": "b5209d15", + "id": "4f875005", "metadata": { "editable": true }, @@ -25070,7 +45882,7 @@ }, { "cell_type": "markdown", - "id": "ad9f3dfb", + "id": "8748bf88", "metadata": { "editable": true }, @@ -25086,7 +45898,7 @@ }, { "cell_type": "markdown", - "id": "ecf5c2d9", + "id": "ce8b1154", "metadata": { "editable": true }, @@ -25098,7 +45910,7 @@ }, { "cell_type": "markdown", - "id": "525fe8e7", + "id": "f6ae91fa", "metadata": { "editable": true }, @@ -25122,7 +45934,7 @@ }, { "cell_type": "markdown", - "id": "6938f171", + "id": "f33f3a2e", "metadata": { "editable": true }, @@ -25134,7 +45946,7 @@ }, { "cell_type": "markdown", - "id": "592ce735", + "id": "d90fb703", "metadata": { "editable": true }, @@ -25155,7 +45967,7 @@ }, { "cell_type": "markdown", - "id": "4e757d4b", + "id": "0e4df2e9", "metadata": { "editable": true }, @@ -25167,7 +45979,7 @@ }, { "cell_type": "markdown", - "id": "b949460f", + "id": "85cc6666", "metadata": { "editable": true }, @@ -25186,7 +45998,7 @@ }, { "cell_type": "markdown", - "id": "7dbdf166", + "id": "c4876b89", "metadata": { "editable": true }, @@ -25196,7 +46008,7 @@ }, { "cell_type": "markdown", - "id": "64247852", + "id": "7e2cf00e", "metadata": { "editable": true }, @@ -25210,7 +46022,7 @@ }, { "cell_type": "markdown", - "id": "5b12d0ef", + "id": "a49b485d", "metadata": { "editable": true }, @@ -25222,7 +46034,7 @@ }, { "cell_type": "markdown", - "id": "22b5cd55", + "id": "7c5a4275", "metadata": { "editable": true }, @@ -25234,7 +46046,7 @@ }, { "cell_type": "markdown", - "id": "1adf129e", + "id": "ee92110b", "metadata": { "editable": true }, @@ -25251,7 +46063,7 @@ }, { "cell_type": "markdown", - "id": "380767da", + "id": "c76e461e", "metadata": { "editable": true }, @@ -25263,7 +46075,7 @@ }, { "cell_type": "markdown", - "id": "c8f1251c", + "id": "df9bc42a", "metadata": { "editable": true }, @@ -25285,7 +46097,7 @@ }, { "cell_type": "markdown", - "id": "3d864d42", + "id": "418dcc35", "metadata": { "editable": true }, @@ -25300,7 +46112,7 @@ }, { "cell_type": "markdown", - "id": "53e11ad4", + "id": "145ede1b", "metadata": { "editable": true }, @@ -25311,7 +46123,7 @@ { "cell_type": "code", "execution_count": 43, - "id": "87126ea3", + "id": "98b44c55", "metadata": { "collapsed": false, "editable": true @@ -25466,7 +46278,7 @@ }, { "cell_type": "markdown", - "id": "85cdbffb", + "id": "47df9d6f", "metadata": { "editable": true }, @@ -25481,7 +46293,7 @@ { "cell_type": "code", "execution_count": 44, - "id": "ea147bd8", + "id": "1415bdf5", "metadata": { "collapsed": false, "editable": true @@ -25650,7 +46462,7 @@ }, { "cell_type": "markdown", - "id": "a9c208db", + "id": "b480dee1", "metadata": { "editable": true }, @@ -25663,7 +46475,7 @@ }, { "cell_type": "markdown", - "id": "5c9d7463", + "id": "3736290c", "metadata": { "editable": true }, @@ -25680,7 +46492,7 @@ }, { "cell_type": "markdown", - "id": "959a791d", + "id": "9fe04f20", "metadata": { "editable": true }, @@ -25696,7 +46508,7 @@ }, { "cell_type": "markdown", - "id": "4d9192ef", + "id": "8090a04f", "metadata": { "editable": true }, @@ -25709,7 +46521,7 @@ }, { "cell_type": "markdown", - "id": "cd3254e9", + "id": "22a5d518", "metadata": { "editable": true }, @@ -25726,7 +46538,7 @@ }, { "cell_type": "markdown", - "id": "13dfafca", + "id": "24312795", "metadata": { "editable": true }, @@ -25738,7 +46550,7 @@ }, { "cell_type": "markdown", - "id": "98e4108f", + "id": "c90e2f79", "metadata": { "editable": true }, @@ -25765,7 +46577,7 @@ }, { "cell_type": "markdown", - "id": "6ef89c8b", + "id": "112c3155", "metadata": { "editable": true }, @@ -25778,7 +46590,7 @@ { "cell_type": "code", "execution_count": 45, - "id": "5cb44c16", + "id": "73371f5c", "metadata": { "collapsed": false, "editable": true @@ -25952,7 +46764,7 @@ }, { "cell_type": "markdown", - "id": "3357aac9", + "id": "9ce89121", "metadata": { "editable": true }, @@ -25972,7 +46784,7 @@ }, { "cell_type": "markdown", - "id": "8766414a", + "id": "dfe02266", "metadata": { "editable": true }, @@ -25987,7 +46799,7 @@ }, { "cell_type": "markdown", - "id": "e5ba08c5", + "id": "f2f12c48", "metadata": { "editable": true }, @@ -26001,7 +46813,7 @@ }, { "cell_type": "markdown", - "id": "0ab225a5", + "id": "ec46581e", "metadata": { "editable": true }, @@ -26017,7 +46829,7 @@ }, { "cell_type": "markdown", - "id": "5449a58e", + "id": "02c7198e", "metadata": { "editable": true }, @@ -26027,7 +46839,7 @@ }, { "cell_type": "markdown", - "id": "a8b2c62b", + "id": "90c2533d", "metadata": { "editable": true }, @@ -26049,7 +46861,7 @@ }, { "cell_type": "markdown", - "id": "e96c80e9", + "id": "f3dddcdf", "metadata": { "editable": true }, @@ -26063,7 +46875,7 @@ { "cell_type": "code", "execution_count": 46, - "id": "72270271", + "id": "52ee1308", "metadata": { "collapsed": false, "editable": true @@ -26139,7 +46951,7 @@ }, { "cell_type": "markdown", - "id": "e91781d3", + "id": "6537fc0c", "metadata": { "editable": true }, @@ -26151,7 +46963,7 @@ }, { "cell_type": "markdown", - "id": "f004baeb", + "id": "bf0ace9e", "metadata": { "editable": true }, @@ -26168,7 +46980,7 @@ }, { "cell_type": "markdown", - "id": "4005279b", + "id": "212fac99", "metadata": { "editable": true }, @@ -26180,7 +46992,7 @@ }, { "cell_type": "markdown", - "id": "41750a28", + "id": "fa2547d7", "metadata": { "editable": true }, @@ -26195,7 +47007,7 @@ }, { "cell_type": "markdown", - "id": "7ac869b8", + "id": "4d3aa4c3", "metadata": { "editable": true }, @@ -26207,7 +47019,7 @@ }, { "cell_type": "markdown", - "id": "3f838e66", + "id": "872d2747", "metadata": { "editable": true }, @@ -26219,7 +47031,7 @@ }, { "cell_type": "markdown", - "id": "981f744e", + "id": "b51b983e", "metadata": { "editable": true }, @@ -26231,7 +47043,7 @@ }, { "cell_type": "markdown", - "id": "73504342", + "id": "f208cc09", "metadata": { "editable": true }, @@ -26241,7 +47053,7 @@ }, { "cell_type": "markdown", - "id": "65918eb3", + "id": "d584ffd3", "metadata": { "editable": true }, @@ -26258,7 +47070,7 @@ }, { "cell_type": "markdown", - "id": "e1f3e150", + "id": "99794ce5", "metadata": { "editable": true }, @@ -26270,7 +47082,7 @@ }, { "cell_type": "markdown", - "id": "b4d54816", + "id": "3f2f40d9", "metadata": { "editable": true }, @@ -26282,7 +47094,7 @@ }, { "cell_type": "markdown", - "id": "761ba010", + "id": "d5590dfb", "metadata": { "editable": true }, @@ -26292,7 +47104,7 @@ }, { "cell_type": "markdown", - "id": "de0a4270", + "id": "75ab0f4f", "metadata": { "editable": true }, @@ -26304,7 +47116,7 @@ }, { "cell_type": "markdown", - "id": "b3df6b28", + "id": "d83e4c37", "metadata": { "editable": true }, @@ -26315,7 +47127,7 @@ { "cell_type": "code", "execution_count": 47, - "id": "0ea196b5", + "id": "95731560", "metadata": { "collapsed": false, "editable": true @@ -26476,7 +47288,7 @@ }, { "cell_type": "markdown", - "id": "9229281a", + "id": "559cb774", "metadata": { "editable": true }, @@ -26498,7 +47310,7 @@ }, { "cell_type": "markdown", - "id": "c07a6324", + "id": "72b9c88d", "metadata": { "editable": true }, @@ -26515,7 +47327,7 @@ }, { "cell_type": "markdown", - "id": "28c6e156", + "id": "1c7a99a5", "metadata": { "editable": true }, @@ -26525,7 +47337,7 @@ }, { "cell_type": "markdown", - "id": "c8cb001c", + "id": "b46248f9", "metadata": { "editable": true }, @@ -26540,7 +47352,7 @@ }, { "cell_type": "markdown", - "id": "2ea0c3eb", + "id": "fa1d6871", "metadata": { "editable": true }, @@ -26550,7 +47362,7 @@ }, { "cell_type": "markdown", - "id": "91bd65f6", + "id": "caa3216a", "metadata": { "editable": true }, @@ -26565,7 +47377,7 @@ }, { "cell_type": "markdown", - "id": "8196883f", + "id": "936f3b89", "metadata": { "editable": true }, @@ -26576,7 +47388,7 @@ }, { "cell_type": "markdown", - "id": "a3dbac1d", + "id": "1e7fa7e3", "metadata": { "editable": true }, @@ -26596,7 +47408,7 @@ }, { "cell_type": "markdown", - "id": "fa4b53ba", + "id": "9c46ad0b", "metadata": { "editable": true }, @@ -26608,7 +47420,7 @@ }, { "cell_type": "markdown", - "id": "ae62439e", + "id": "ccf16979", "metadata": { "editable": true }, @@ -26645,7 +47457,7 @@ }, { "cell_type": "markdown", - "id": "3ab45a2b", + "id": "e940bd60", "metadata": { "editable": true }, @@ -26655,7 +47467,7 @@ }, { "cell_type": "markdown", - "id": "64114f21", + "id": "ee51ae7f", "metadata": { "editable": true }, @@ -26668,7 +47480,7 @@ { "cell_type": "code", "execution_count": 48, - "id": "16f3ae01", + "id": "c538fed6", "metadata": { "collapsed": false, "editable": true @@ -26869,7 +47681,7 @@ }, { "cell_type": "markdown", - "id": "496273dc", + "id": "9855543b", "metadata": { "editable": true }, @@ -26886,7 +47698,7 @@ }, { "cell_type": "markdown", - "id": "c5fafc34", + "id": "a1b8cd5d", "metadata": { "editable": true }, @@ -26903,7 +47715,7 @@ }, { "cell_type": "markdown", - "id": "9da50613", + "id": "75d6ae90", "metadata": { "editable": true }, @@ -26913,7 +47725,7 @@ }, { "cell_type": "markdown", - "id": "7d878f57", + "id": "83d6d123", "metadata": { "editable": true }, @@ -26928,7 +47740,7 @@ }, { "cell_type": "markdown", - "id": "f733da79", + "id": "7d9f4892", "metadata": { "editable": true }, @@ -26942,7 +47754,7 @@ }, { "cell_type": "markdown", - "id": "75200533", + "id": "bc1c8c1b", "metadata": { "editable": true }, @@ -26955,7 +47767,7 @@ }, { "cell_type": "markdown", - "id": "9eca0d3c", + "id": "6fdbc09b", "metadata": { "editable": true }, @@ -26975,7 +47787,7 @@ }, { "cell_type": "markdown", - "id": "6763a3ba", + "id": "15f911ae", "metadata": { "editable": true }, @@ -26987,7 +47799,7 @@ }, { "cell_type": "markdown", - "id": "7ad92188", + "id": "73d815fc", "metadata": { "editable": true }, @@ -26999,7 +47811,7 @@ }, { "cell_type": "markdown", - "id": "74a848a9", + "id": "a8b12b31", "metadata": { "editable": true }, @@ -27011,7 +47823,7 @@ }, { "cell_type": "markdown", - "id": "604cb0d2", + "id": "0bca5395", "metadata": { "editable": true }, @@ -27021,7 +47833,7 @@ }, { "cell_type": "markdown", - "id": "a375f71b", + "id": "189bac4d", "metadata": { "editable": true }, @@ -27033,7 +47845,7 @@ }, { "cell_type": "markdown", - "id": "db03df01", + "id": "9b596e36", "metadata": { "editable": true }, @@ -27045,7 +47857,7 @@ }, { "cell_type": "markdown", - "id": "ad1e5bb9", + "id": "3dc31dd6", "metadata": { "editable": true }, @@ -27057,7 +47869,7 @@ }, { "cell_type": "markdown", - "id": "3e5c1a4b", + "id": "263f026e", "metadata": { "editable": true }, @@ -27067,7 +47879,7 @@ }, { "cell_type": "markdown", - "id": "907a6d4c", + "id": "717dae65", "metadata": { "editable": true }, @@ -27083,7 +47895,7 @@ }, { "cell_type": "markdown", - "id": "20d93513", + "id": "0f898a32", "metadata": { "editable": true }, @@ -27093,7 +47905,7 @@ }, { "cell_type": "markdown", - "id": "c7780ae9", + "id": "47634dd9", "metadata": { "editable": true }, @@ -27105,7 +47917,7 @@ }, { "cell_type": "markdown", - "id": "ed12bd6d", + "id": "6af0489e", "metadata": { "editable": true }, @@ -27122,7 +47934,7 @@ }, { "cell_type": "markdown", - "id": "5be52dcf", + "id": "03dabab2", "metadata": { "editable": true }, @@ -27132,7 +47944,7 @@ }, { "cell_type": "markdown", - "id": "f759b8d1", + "id": "796edf7a", "metadata": { "editable": true }, @@ -27148,7 +47960,7 @@ }, { "cell_type": "markdown", - "id": "014834ff", + "id": "78d4195f", "metadata": { "editable": true }, @@ -27162,7 +47974,7 @@ }, { "cell_type": "markdown", - "id": "7bbb7493", + "id": "13f63286", "metadata": { "editable": true }, @@ -27181,7 +47993,7 @@ { "cell_type": "code", "execution_count": 49, - "id": "4019a7dd", + "id": "e05767f4", "metadata": { "collapsed": false, "editable": true @@ -27236,7 +48048,7 @@ }, { "cell_type": "markdown", - "id": "d5cd516b", + "id": "d4ebc584", "metadata": { "editable": true }, @@ -27266,7 +48078,7 @@ }, { "cell_type": "markdown", - "id": "8affc5a2", + "id": "124b550e", "metadata": { "editable": true }, @@ -27295,7 +48107,7 @@ { "cell_type": "code", "execution_count": 50, - "id": "6803b65e", + "id": "53c4c879", "metadata": { "collapsed": false, "editable": true @@ -27342,7 +48154,7 @@ }, { "cell_type": "markdown", - "id": "59db5670", + "id": "b11a26b8", "metadata": { "editable": true }, @@ -27368,7 +48180,7 @@ { "cell_type": "code", "execution_count": 51, - "id": "eec1a5f5", + "id": "fa2d8508", "metadata": { "collapsed": false, "editable": true @@ -27602,7 +48414,7 @@ }, { "cell_type": "markdown", - "id": "256d8ad9", + "id": "4dfabc5e", "metadata": { "editable": true }, @@ -27614,7 +48426,7 @@ }, { "cell_type": "markdown", - "id": "4772948f", + "id": "8f6f58e6", "metadata": { "editable": true }, @@ -27626,7 +48438,7 @@ }, { "cell_type": "markdown", - "id": "323bc655", + "id": "8a4abe82", "metadata": { "editable": true }, @@ -27638,7 +48450,7 @@ }, { "cell_type": "markdown", - "id": "fbe75888", + "id": "57c34190", "metadata": { "editable": true }, @@ -27655,7 +48467,7 @@ }, { "cell_type": "markdown", - "id": "29fc55b0", + "id": "8c5b8137", "metadata": { "editable": true }, @@ -27665,7 +48477,7 @@ }, { "cell_type": "markdown", - "id": "5f9c311c", + "id": "08cb8e62", "metadata": { "editable": true }, @@ -27677,7 +48489,7 @@ }, { "cell_type": "markdown", - "id": "583599b9", + "id": "f7b1bf8c", "metadata": { "editable": true }, @@ -27694,7 +48506,7 @@ }, { "cell_type": "markdown", - "id": "3fe68796", + "id": "1127e6e8", "metadata": { "editable": true }, @@ -27705,7 +48517,7 @@ }, { "cell_type": "markdown", - "id": "858bcccf", + "id": "7a099129", "metadata": { "editable": true }, @@ -27725,7 +48537,7 @@ }, { "cell_type": "markdown", - "id": "9a46e1cb", + "id": "772c7eec", "metadata": { "editable": true }, @@ -27735,7 +48547,7 @@ }, { "cell_type": "markdown", - "id": "f0e68c83", + "id": "be8649c8", "metadata": { "editable": true }, @@ -27761,7 +48573,7 @@ }, { "cell_type": "markdown", - "id": "c80f96fe", + "id": "27484fe1", "metadata": { "editable": true }, @@ -27777,7 +48589,7 @@ }, { "cell_type": "markdown", - "id": "a82fb339", + "id": "16f8dd35", "metadata": { "editable": true }, @@ -27788,7 +48600,7 @@ { "cell_type": "code", "execution_count": 52, - "id": "744f5688", + "id": "51011a0f", "metadata": { "collapsed": false, "editable": true @@ -28019,7 +48831,7 @@ }, { "cell_type": "markdown", - "id": "b1e76cca", + "id": "fff9e254", "metadata": { "editable": true }, diff --git a/doc/LectureNotes/_build/jupyter_execute/week43.py b/doc/LectureNotes/_build/jupyter_execute/week43.py index 7d622d690..7a4a2bfbb 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week43.py +++ b/doc/LectureNotes/_build/jupyter_execute/week43.py @@ -32,6 +32,8 @@ # # * Solving differential equations with Neural Networks and intro to **Tensorflow** with examples. # +# * [Video of lecture](https://youtu.be/_-AwbBh4G-8) +# # * Readings and Videos: # # * These lecture notes diff --git a/doc/LectureNotes/week41.ipynb b/doc/LectureNotes/week41.ipynb index 83113fdee..e7bdb5237 100644 --- a/doc/LectureNotes/week41.ipynb +++ b/doc/LectureNotes/week41.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "2ab4355a", + "id": "9a4eccc2", "metadata": { "editable": true }, @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "bb11e4db", + "id": "243c5d47", "metadata": { "editable": true }, @@ -27,7 +27,7 @@ }, { "cell_type": "markdown", - "id": "1d2fb3fd", + "id": "0cae636e", "metadata": { "editable": true }, @@ -52,6 +52,10 @@ "\n", " * Building our own Feed-forward Neural Network\n", "\n", + " * [Video of lecture notes](https://youtu.be/5-RRTO9uDvI)\n", + "\n", + " * [Whiteboard notes](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesOct12.pdf)\n", + "\n", " * Readings and Videos:\n", "\n", " * These lecture notes\n", @@ -71,7 +75,7 @@ }, { "cell_type": "markdown", - "id": "9dcb51d0", + "id": "51ff64f7", "metadata": { "editable": true }, @@ -81,7 +85,7 @@ }, { "cell_type": "markdown", - "id": "b2dde1a5", + "id": "07ff4601", "metadata": { "editable": true }, @@ -99,7 +103,7 @@ }, { "cell_type": "markdown", - "id": "9dbb7580", + "id": "4806cddf", "metadata": { "editable": true }, @@ -123,7 +127,7 @@ }, { "cell_type": "markdown", - "id": "9b37b818", + "id": "ccfdcf2b", "metadata": { "editable": true }, @@ -141,7 +145,7 @@ }, { "cell_type": "markdown", - "id": "f0c18c43", + "id": "900fd38b", "metadata": { "editable": true }, @@ -181,7 +185,7 @@ }, { "cell_type": "markdown", - "id": "4d476078", + "id": "15e0098f", "metadata": { "editable": true }, @@ -210,7 +214,7 @@ }, { "cell_type": "markdown", - "id": "6577603f", + "id": "70d3fe5d", "metadata": { "editable": true }, @@ -231,7 +235,7 @@ }, { "cell_type": "markdown", - "id": "3f765006", + "id": "838210ba", "metadata": { "editable": true }, @@ -260,7 +264,7 @@ }, { "cell_type": "markdown", - "id": "20de12ca", + "id": "8a78d22d", "metadata": { "editable": true }, @@ -281,7 +285,7 @@ }, { "cell_type": "markdown", - "id": "48751fb1", + "id": "4ca0b479", "metadata": { "editable": true }, @@ -302,7 +306,7 @@ }, { "cell_type": "markdown", - "id": "4bc57cc0", + "id": "b80ebf1a", "metadata": { "editable": true }, @@ -319,7 +323,7 @@ }, { "cell_type": "markdown", - "id": "f194e3be", + "id": "749545b4", "metadata": { "editable": true }, @@ -340,7 +344,7 @@ }, { "cell_type": "markdown", - "id": "11dceaa5", + "id": "f524768e", "metadata": { "editable": true }, @@ -356,7 +360,7 @@ }, { "cell_type": "markdown", - "id": "9564ced4", + "id": "6c2f8b44", "metadata": { "editable": true }, @@ -373,7 +377,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "605a35fc", + "id": "8dfb9c06", "metadata": { "collapsed": false, "editable": true @@ -414,7 +418,7 @@ }, { "cell_type": "markdown", - "id": "7682eeab", + "id": "b0033599", "metadata": { "editable": true }, @@ -424,7 +428,7 @@ }, { "cell_type": "markdown", - "id": "8bbee5fe", + "id": "4663d22e", "metadata": { "editable": true }, @@ -435,7 +439,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "9ede7953", + "id": "846ae942", "metadata": { "collapsed": false, "editable": true @@ -497,7 +501,7 @@ }, { "cell_type": "markdown", - "id": "110b8ae5", + "id": "22786e56", "metadata": { "editable": true }, @@ -507,7 +511,7 @@ }, { "cell_type": "markdown", - "id": "7e5f94af", + "id": "b6bdebe1", "metadata": { "editable": true }, @@ -518,7 +522,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "f5aee400", + "id": "fdfb8244", "metadata": { "collapsed": false, "editable": true @@ -538,7 +542,7 @@ }, { "cell_type": "markdown", - "id": "812e072e", + "id": "13c5155d", "metadata": { "editable": true }, @@ -550,7 +554,7 @@ }, { "cell_type": "markdown", - "id": "64b04fac", + "id": "47b4c719", "metadata": { "editable": true }, @@ -562,7 +566,7 @@ }, { "cell_type": "markdown", - "id": "7f8be757", + "id": "d65d78c7", "metadata": { "editable": true }, @@ -577,7 +581,7 @@ }, { "cell_type": "markdown", - "id": "a3a920b2", + "id": "b8605d0d", "metadata": { "editable": true }, @@ -589,7 +593,7 @@ }, { "cell_type": "markdown", - "id": "dacd264b", + "id": "be0a2d56", "metadata": { "editable": true }, @@ -606,7 +610,7 @@ }, { "cell_type": "markdown", - "id": "5cb2ca57", + "id": "8a8ec932", "metadata": { "editable": true }, @@ -621,7 +625,7 @@ }, { "cell_type": "markdown", - "id": "c33f37be", + "id": "30b5df75", "metadata": { "editable": true }, @@ -639,7 +643,7 @@ }, { "cell_type": "markdown", - "id": "8747dd79", + "id": "9906f339", "metadata": { "editable": true }, @@ -651,7 +655,7 @@ }, { "cell_type": "markdown", - "id": "238b66fc", + "id": "705c18d2", "metadata": { "editable": true }, @@ -669,7 +673,7 @@ }, { "cell_type": "markdown", - "id": "ec349a1b", + "id": "c0779fc6", "metadata": { "editable": true }, @@ -682,7 +686,7 @@ }, { "cell_type": "markdown", - "id": "ab5abd1e", + "id": "128a41e3", "metadata": { "editable": true }, @@ -694,7 +698,7 @@ }, { "cell_type": "markdown", - "id": "f4cebe1e", + "id": "e8efb8a6", "metadata": { "editable": true }, @@ -712,7 +716,7 @@ }, { "cell_type": "markdown", - "id": "dd700242", + "id": "f975dcc6", "metadata": { "editable": true }, @@ -730,7 +734,7 @@ }, { "cell_type": "markdown", - "id": "6e309498", + "id": "7c3843f7", "metadata": { "editable": true }, @@ -740,7 +744,7 @@ }, { "cell_type": "markdown", - "id": "aad2882a", + "id": "0008d41a", "metadata": { "editable": true }, @@ -758,7 +762,7 @@ }, { "cell_type": "markdown", - "id": "dc802cd2", + "id": "e8dc6d51", "metadata": { "editable": true }, @@ -777,7 +781,7 @@ }, { "cell_type": "markdown", - "id": "57e22c56", + "id": "1455a093", "metadata": { "editable": true }, @@ -790,7 +794,7 @@ }, { "cell_type": "markdown", - "id": "6e91389e", + "id": "c2affab6", "metadata": { "editable": true }, @@ -808,7 +812,7 @@ }, { "cell_type": "markdown", - "id": "8755afdf", + "id": "c7910d23", "metadata": { "editable": true }, @@ -819,7 +823,7 @@ }, { "cell_type": "markdown", - "id": "f1dc0077", + "id": "ec9e660d", "metadata": { "editable": true }, @@ -838,7 +842,7 @@ }, { "cell_type": "markdown", - "id": "71fb8777", + "id": "29c77377", "metadata": { "editable": true }, @@ -856,7 +860,7 @@ }, { "cell_type": "markdown", - "id": "96280b34", + "id": "f52146ef", "metadata": { "editable": true }, @@ -869,7 +873,7 @@ }, { "cell_type": "markdown", - "id": "6be3363c", + "id": "7f9f65ce", "metadata": { "editable": true }, @@ -889,7 +893,7 @@ }, { "cell_type": "markdown", - "id": "d1eb96a8", + "id": "7aec05b7", "metadata": { "editable": true }, @@ -922,7 +926,7 @@ }, { "cell_type": "markdown", - "id": "044d5e68", + "id": "524f3145", "metadata": { "editable": true }, @@ -934,7 +938,7 @@ }, { "cell_type": "markdown", - "id": "23a2c440", + "id": "67ea322c", "metadata": { "editable": true }, @@ -953,7 +957,7 @@ }, { "cell_type": "markdown", - "id": "5f6921f1", + "id": "fb2a1836", "metadata": { "editable": true }, @@ -967,7 +971,7 @@ }, { "cell_type": "markdown", - "id": "bc3e047f", + "id": "c6e37074", "metadata": { "editable": true }, @@ -990,7 +994,7 @@ }, { "cell_type": "markdown", - "id": "f2ff2ee2", + "id": "516427eb", "metadata": { "editable": true }, @@ -1009,7 +1013,7 @@ }, { "cell_type": "markdown", - "id": "a336177e", + "id": "607e0e1f", "metadata": { "editable": true }, @@ -1021,7 +1025,7 @@ }, { "cell_type": "markdown", - "id": "c9a263c9", + "id": "4147fa7c", "metadata": { "editable": true }, @@ -1031,7 +1035,7 @@ }, { "cell_type": "markdown", - "id": "f102c33c", + "id": "4c880e2a", "metadata": { "editable": true }, @@ -1043,7 +1047,7 @@ }, { "cell_type": "markdown", - "id": "92eeded3", + "id": "56c9cd1b", "metadata": { "editable": true }, @@ -1060,7 +1064,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "17339021", + "id": "187aa1c5", "metadata": { "collapsed": false, "editable": true @@ -1142,7 +1146,7 @@ }, { "cell_type": "markdown", - "id": "8c915f6d", + "id": "e3195fd0", "metadata": { "editable": true }, @@ -1180,7 +1184,7 @@ }, { "cell_type": "markdown", - "id": "cdcb2b32", + "id": "f15de9c4", "metadata": { "editable": true }, @@ -1210,7 +1214,7 @@ }, { "cell_type": "markdown", - "id": "566b0dcb", + "id": "9d79d680", "metadata": { "editable": true }, @@ -1231,7 +1235,7 @@ }, { "cell_type": "markdown", - "id": "66c76764", + "id": "31e1a9b1", "metadata": { "editable": true }, @@ -1243,7 +1247,7 @@ }, { "cell_type": "markdown", - "id": "e3d0bfa4", + "id": "890921b9", "metadata": { "editable": true }, @@ -1258,7 +1262,7 @@ }, { "cell_type": "markdown", - "id": "01cf3c8d", + "id": "fe55dffb", "metadata": { "editable": true }, @@ -1275,7 +1279,7 @@ }, { "cell_type": "markdown", - "id": "1417eea3", + "id": "61548fd5", "metadata": { "editable": true }, @@ -1287,7 +1291,7 @@ }, { "cell_type": "markdown", - "id": "3c4e10cf", + "id": "7b437827", "metadata": { "editable": true }, @@ -1300,7 +1304,7 @@ }, { "cell_type": "markdown", - "id": "d9dc403d", + "id": "88aeaf0f", "metadata": { "editable": true }, @@ -1312,7 +1316,7 @@ }, { "cell_type": "markdown", - "id": "83b73b28", + "id": "d81878b1", "metadata": { "editable": true }, @@ -1326,7 +1330,7 @@ }, { "cell_type": "markdown", - "id": "0516dfcf", + "id": "aa3e6b55", "metadata": { "editable": true }, @@ -1338,7 +1342,7 @@ }, { "cell_type": "markdown", - "id": "7cebeb6f", + "id": "282fc0f1", "metadata": { "editable": true }, @@ -1350,7 +1354,7 @@ }, { "cell_type": "markdown", - "id": "e94a91e9", + "id": "5007c640", "metadata": { "editable": true }, @@ -1362,7 +1366,7 @@ }, { "cell_type": "markdown", - "id": "343a68be", + "id": "ed8b0010", "metadata": { "editable": true }, @@ -1372,7 +1376,7 @@ }, { "cell_type": "markdown", - "id": "1eb93ade", + "id": "3174619f", "metadata": { "editable": true }, @@ -1384,7 +1388,7 @@ }, { "cell_type": "markdown", - "id": "b4294825", + "id": "448a714d", "metadata": { "editable": true }, @@ -1394,7 +1398,7 @@ }, { "cell_type": "markdown", - "id": "3c84d05d", + "id": "862da704", "metadata": { "editable": true }, @@ -1406,7 +1410,7 @@ }, { "cell_type": "markdown", - "id": "acd76b39", + "id": "9958b437", "metadata": { "editable": true }, @@ -1420,7 +1424,7 @@ }, { "cell_type": "markdown", - "id": "f0383586", + "id": "be3a329b", "metadata": { "editable": true }, @@ -1432,7 +1436,7 @@ }, { "cell_type": "markdown", - "id": "81c5b644", + "id": "2237bf2b", "metadata": { "editable": true }, @@ -1442,7 +1446,7 @@ }, { "cell_type": "markdown", - "id": "31fde909", + "id": "74e6185e", "metadata": { "editable": true }, @@ -1454,7 +1458,7 @@ }, { "cell_type": "markdown", - "id": "4e4e2612", + "id": "462a7694", "metadata": { "editable": true }, @@ -1464,7 +1468,7 @@ }, { "cell_type": "markdown", - "id": "9c9e5698", + "id": "cde99728", "metadata": { "editable": true }, @@ -1476,7 +1480,7 @@ }, { "cell_type": "markdown", - "id": "421c9de0", + "id": "11c5d932", "metadata": { "editable": true }, @@ -1488,7 +1492,7 @@ }, { "cell_type": "markdown", - "id": "10597fa3", + "id": "d46a1cc5", "metadata": { "editable": true }, @@ -1500,7 +1504,7 @@ }, { "cell_type": "markdown", - "id": "0cf3157b", + "id": "7f634831", "metadata": { "editable": true }, @@ -1510,7 +1514,7 @@ }, { "cell_type": "markdown", - "id": "01128c99", + "id": "c2245dab", "metadata": { "editable": true }, @@ -1522,7 +1526,7 @@ }, { "cell_type": "markdown", - "id": "097ee928", + "id": "fbda1939", "metadata": { "editable": true }, @@ -1532,7 +1536,7 @@ }, { "cell_type": "markdown", - "id": "cb2ff4ec", + "id": "54e8fb49", "metadata": { "editable": true }, @@ -1544,7 +1548,7 @@ }, { "cell_type": "markdown", - "id": "ee3423ea", + "id": "0fa8b43d", "metadata": { "editable": true }, @@ -1568,7 +1572,7 @@ }, { "cell_type": "markdown", - "id": "49a7e0e3", + "id": "00455d1e", "metadata": { "editable": true }, @@ -1580,7 +1584,7 @@ }, { "cell_type": "markdown", - "id": "8efd3f58", + "id": "8a503b44", "metadata": { "editable": true }, @@ -1590,7 +1594,7 @@ }, { "cell_type": "markdown", - "id": "dd61c9f6", + "id": "28aaa847", "metadata": { "editable": true }, @@ -1602,7 +1606,7 @@ }, { "cell_type": "markdown", - "id": "7574ecb2", + "id": "ca7f309e", "metadata": { "editable": true }, @@ -1614,7 +1618,7 @@ }, { "cell_type": "markdown", - "id": "103823da", + "id": "79b5d957", "metadata": { "editable": true }, @@ -1626,7 +1630,7 @@ }, { "cell_type": "markdown", - "id": "8fffaff6", + "id": "ba24b0ba", "metadata": { "editable": true }, @@ -1636,7 +1640,7 @@ }, { "cell_type": "markdown", - "id": "ae94d046", + "id": "2f98d5ae", "metadata": { "editable": true }, @@ -1648,7 +1652,7 @@ }, { "cell_type": "markdown", - "id": "3874588e", + "id": "596bd7eb", "metadata": { "editable": true }, @@ -1658,7 +1662,7 @@ }, { "cell_type": "markdown", - "id": "296c98ed", + "id": "9144dcb7", "metadata": { "editable": true }, @@ -1672,7 +1676,7 @@ }, { "cell_type": "markdown", - "id": "19e9a098", + "id": "4f2e52cb", "metadata": { "editable": true }, @@ -1690,7 +1694,7 @@ }, { "cell_type": "markdown", - "id": "edb4be27", + "id": "3b94d154", "metadata": { "editable": true }, @@ -1700,7 +1704,7 @@ }, { "cell_type": "markdown", - "id": "f85640ae", + "id": "18f353fd", "metadata": { "editable": true }, @@ -1718,7 +1722,7 @@ }, { "cell_type": "markdown", - "id": "ef718563", + "id": "631f4feb", "metadata": { "editable": true }, @@ -1728,7 +1732,7 @@ }, { "cell_type": "markdown", - "id": "69a17671", + "id": "1793bcd7", "metadata": { "editable": true }, @@ -1746,7 +1750,7 @@ }, { "cell_type": "markdown", - "id": "84ef0653", + "id": "0cc3885e", "metadata": { "editable": true }, @@ -1773,7 +1777,7 @@ }, { "cell_type": "markdown", - "id": "fb567e6f", + "id": "7f697fec", "metadata": { "editable": true }, @@ -1785,7 +1789,7 @@ }, { "cell_type": "markdown", - "id": "42773e4f", + "id": "42a9acc8", "metadata": { "editable": true }, @@ -1797,7 +1801,7 @@ }, { "cell_type": "markdown", - "id": "c01f5515", + "id": "ac01943b", "metadata": { "editable": true }, @@ -1807,7 +1811,7 @@ }, { "cell_type": "markdown", - "id": "b23962dc", + "id": "a6ec5df0", "metadata": { "editable": true }, @@ -1819,7 +1823,7 @@ }, { "cell_type": "markdown", - "id": "c84de332", + "id": "8b0f324a", "metadata": { "editable": true }, @@ -1829,7 +1833,7 @@ }, { "cell_type": "markdown", - "id": "ed77e307", + "id": "4ae4c890", "metadata": { "editable": true }, @@ -1841,7 +1845,7 @@ }, { "cell_type": "markdown", - "id": "ab04fbdc", + "id": "b8af6a3b", "metadata": { "editable": true }, @@ -1851,7 +1855,7 @@ }, { "cell_type": "markdown", - "id": "d0ce3d50", + "id": "c5a36652", "metadata": { "editable": true }, @@ -1863,7 +1867,7 @@ }, { "cell_type": "markdown", - "id": "a217439f", + "id": "eb9f0a42", "metadata": { "editable": true }, @@ -1875,7 +1879,7 @@ }, { "cell_type": "markdown", - "id": "71731044", + "id": "cd78653c", "metadata": { "editable": true }, @@ -1898,7 +1902,7 @@ }, { "cell_type": "markdown", - "id": "a2afecfd", + "id": "adab4f13", "metadata": { "editable": true }, @@ -1910,7 +1914,7 @@ }, { "cell_type": "markdown", - "id": "0ffa0ea9", + "id": "9091074a", "metadata": { "editable": true }, @@ -1920,7 +1924,7 @@ }, { "cell_type": "markdown", - "id": "025daba8", + "id": "da04b8a8", "metadata": { "editable": true }, @@ -1932,7 +1936,7 @@ }, { "cell_type": "markdown", - "id": "935b27ef", + "id": "d707b6e0", "metadata": { "editable": true }, @@ -1942,7 +1946,7 @@ }, { "cell_type": "markdown", - "id": "50179be2", + "id": "f26ac396", "metadata": { "editable": true }, @@ -1954,7 +1958,7 @@ }, { "cell_type": "markdown", - "id": "90daf72c", + "id": "1d5d1ed0", "metadata": { "editable": true }, @@ -1966,7 +1970,7 @@ }, { "cell_type": "markdown", - "id": "63e9545c", + "id": "d686cbb0", "metadata": { "editable": true }, @@ -1977,7 +1981,7 @@ }, { "cell_type": "markdown", - "id": "e8fbaffb", + "id": "31554688", "metadata": { "editable": true }, @@ -2000,7 +2004,7 @@ }, { "cell_type": "markdown", - "id": "59b2fcba", + "id": "a34466dc", "metadata": { "editable": true }, @@ -2012,7 +2016,7 @@ }, { "cell_type": "markdown", - "id": "db7fd0df", + "id": "742b0899", "metadata": { "editable": true }, @@ -2022,7 +2026,7 @@ }, { "cell_type": "markdown", - "id": "fef9229b", + "id": "813c7f07", "metadata": { "editable": true }, @@ -2034,7 +2038,7 @@ }, { "cell_type": "markdown", - "id": "720ee2fe", + "id": "88fccb02", "metadata": { "editable": true }, @@ -2044,7 +2048,7 @@ }, { "cell_type": "markdown", - "id": "df965f5a", + "id": "a5d5593c", "metadata": { "editable": true }, @@ -2056,7 +2060,7 @@ }, { "cell_type": "markdown", - "id": "29bef69b", + "id": "1b5aeaa8", "metadata": { "editable": true }, @@ -2068,7 +2072,7 @@ }, { "cell_type": "markdown", - "id": "8f8670bc", + "id": "29156297", "metadata": { "editable": true }, @@ -2079,7 +2083,7 @@ }, { "cell_type": "markdown", - "id": "b37be623", + "id": "752b2b7a", "metadata": { "editable": true }, @@ -2102,7 +2106,7 @@ }, { "cell_type": "markdown", - "id": "6ebda602", + "id": "29bb348b", "metadata": { "editable": true }, @@ -2114,7 +2118,7 @@ }, { "cell_type": "markdown", - "id": "6f2371ba", + "id": "2cbb4812", "metadata": { "editable": true }, @@ -2124,7 +2128,7 @@ }, { "cell_type": "markdown", - "id": "b3458652", + "id": "73fa8d95", "metadata": { "editable": true }, @@ -2136,7 +2140,7 @@ }, { "cell_type": "markdown", - "id": "4a46987f", + "id": "d815b24a", "metadata": { "editable": true }, @@ -2146,7 +2150,7 @@ }, { "cell_type": "markdown", - "id": "2b898b0d", + "id": "43d2f617", "metadata": { "editable": true }, @@ -2158,7 +2162,7 @@ }, { "cell_type": "markdown", - "id": "a1af5bcf", + "id": "bf7af7f5", "metadata": { "editable": true }, @@ -2170,7 +2174,7 @@ }, { "cell_type": "markdown", - "id": "8d7a0d4d", + "id": "9a5e5eaf", "metadata": { "editable": true }, @@ -2181,7 +2185,7 @@ }, { "cell_type": "markdown", - "id": "37f6a409", + "id": "e62f23f4", "metadata": { "editable": true }, @@ -2209,7 +2213,7 @@ }, { "cell_type": "markdown", - "id": "e7939992", + "id": "42d7a94e", "metadata": { "editable": true }, @@ -2221,7 +2225,7 @@ }, { "cell_type": "markdown", - "id": "d754aefd", + "id": "5bfea505", "metadata": { "editable": true }, @@ -2231,7 +2235,7 @@ }, { "cell_type": "markdown", - "id": "7711b16e", + "id": "9f6b8143", "metadata": { "editable": true }, @@ -2243,7 +2247,7 @@ }, { "cell_type": "markdown", - "id": "e831f3b5", + "id": "194f2cbe", "metadata": { "editable": true }, @@ -2254,7 +2258,7 @@ }, { "cell_type": "markdown", - "id": "428cb018", + "id": "20cd18e6", "metadata": { "editable": true }, @@ -2266,7 +2270,7 @@ }, { "cell_type": "markdown", - "id": "303e8c9e", + "id": "b3e52796", "metadata": { "editable": true }, @@ -2279,7 +2283,7 @@ }, { "cell_type": "markdown", - "id": "42c3514b", + "id": "7cb68d89", "metadata": { "editable": true }, @@ -2304,7 +2308,7 @@ }, { "cell_type": "markdown", - "id": "6675c087", + "id": "0a98f80a", "metadata": { "editable": true }, @@ -2317,7 +2321,7 @@ }, { "cell_type": "markdown", - "id": "73cfb814", + "id": "b86447a6", "metadata": { "editable": true }, @@ -2329,7 +2333,7 @@ }, { "cell_type": "markdown", - "id": "c32b2b0e", + "id": "942e0060", "metadata": { "editable": true }, @@ -2341,7 +2345,7 @@ }, { "cell_type": "markdown", - "id": "15c86254", + "id": "99eae6f4", "metadata": { "editable": true }, @@ -2351,7 +2355,7 @@ }, { "cell_type": "markdown", - "id": "9da11ce8", + "id": "872017e1", "metadata": { "editable": true }, @@ -2363,7 +2367,7 @@ }, { "cell_type": "markdown", - "id": "47275fce", + "id": "f55259c0", "metadata": { "editable": true }, @@ -2375,7 +2379,7 @@ }, { "cell_type": "markdown", - "id": "22860f6c", + "id": "db983bc4", "metadata": { "editable": true }, @@ -2387,7 +2391,7 @@ }, { "cell_type": "markdown", - "id": "2c90d0da", + "id": "165881cf", "metadata": { "editable": true }, @@ -2399,7 +2403,7 @@ }, { "cell_type": "markdown", - "id": "12ae44b0", + "id": "50ecaa83", "metadata": { "editable": true }, @@ -2409,7 +2413,7 @@ }, { "cell_type": "markdown", - "id": "7d921bf4", + "id": "b16d30f7", "metadata": { "editable": true }, @@ -2421,7 +2425,7 @@ }, { "cell_type": "markdown", - "id": "5e48ee91", + "id": "2d4c1829", "metadata": { "editable": true }, @@ -2431,7 +2435,7 @@ }, { "cell_type": "markdown", - "id": "6add3f84", + "id": "4d7a8c79", "metadata": { "editable": true }, @@ -2443,7 +2447,7 @@ }, { "cell_type": "markdown", - "id": "22759cfd", + "id": "7c4a0d89", "metadata": { "editable": true }, @@ -2456,7 +2460,7 @@ }, { "cell_type": "markdown", - "id": "d18311a9", + "id": "96bccb9e", "metadata": { "editable": true }, @@ -2468,7 +2472,7 @@ }, { "cell_type": "markdown", - "id": "9761e98e", + "id": "01f9ae68", "metadata": { "editable": true }, @@ -2478,7 +2482,7 @@ }, { "cell_type": "markdown", - "id": "01a4059b", + "id": "590fc8bd", "metadata": { "editable": true }, @@ -2490,7 +2494,7 @@ }, { "cell_type": "markdown", - "id": "56bf706a", + "id": "f3dc3a43", "metadata": { "editable": true }, @@ -2501,7 +2505,7 @@ }, { "cell_type": "markdown", - "id": "60613b46", + "id": "7dd1bbaa", "metadata": { "editable": true }, @@ -2513,7 +2517,7 @@ }, { "cell_type": "markdown", - "id": "e97cc842", + "id": "dc472c94", "metadata": { "editable": true }, @@ -2523,7 +2527,7 @@ }, { "cell_type": "markdown", - "id": "9c431661", + "id": "372c5019", "metadata": { "editable": true }, @@ -2535,7 +2539,7 @@ }, { "cell_type": "markdown", - "id": "1903bf14", + "id": "783cc2c1", "metadata": { "editable": true }, @@ -2545,7 +2549,7 @@ }, { "cell_type": "markdown", - "id": "dab43f84", + "id": "d37a48b8", "metadata": { "editable": true }, @@ -2556,7 +2560,7 @@ }, { "cell_type": "markdown", - "id": "03d7a2cf", + "id": "e6790133", "metadata": { "editable": true }, @@ -2569,7 +2573,7 @@ }, { "cell_type": "markdown", - "id": "1813356c", + "id": "97578009", "metadata": { "editable": true }, @@ -2579,7 +2583,7 @@ }, { "cell_type": "markdown", - "id": "debe1289", + "id": "13822f62", "metadata": { "editable": true }, @@ -2591,7 +2595,7 @@ }, { "cell_type": "markdown", - "id": "6fc65621", + "id": "10bf9fb7", "metadata": { "editable": true }, @@ -2601,7 +2605,7 @@ }, { "cell_type": "markdown", - "id": "8c86ad26", + "id": "cf59a594", "metadata": { "editable": true }, @@ -2613,7 +2617,7 @@ }, { "cell_type": "markdown", - "id": "86ecd366", + "id": "80a4d3d7", "metadata": { "editable": true }, @@ -2623,7 +2627,7 @@ }, { "cell_type": "markdown", - "id": "d68a68c2", + "id": "91baac68", "metadata": { "editable": true }, @@ -2647,7 +2651,7 @@ }, { "cell_type": "markdown", - "id": "c9ec79c6", + "id": "b4d8a71e", "metadata": { "editable": true }, @@ -2697,7 +2701,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "b07f9bb8", + "id": "ad941e6f", "metadata": { "collapsed": false, "editable": true @@ -2750,7 +2754,7 @@ }, { "cell_type": "markdown", - "id": "6b776a41", + "id": "c2f821dc", "metadata": { "editable": true }, @@ -2771,7 +2775,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "3e399ed6", + "id": "d368bcd4", "metadata": { "collapsed": false, "editable": true @@ -2809,7 +2813,7 @@ }, { "cell_type": "markdown", - "id": "2c165e0e", + "id": "c0e21c2d", "metadata": { "editable": true }, @@ -2853,7 +2857,7 @@ }, { "cell_type": "markdown", - "id": "94075506", + "id": "ba8404a4", "metadata": { "editable": true }, @@ -2893,7 +2897,7 @@ }, { "cell_type": "markdown", - "id": "0bb70540", + "id": "5ea9e948", "metadata": { "editable": true }, @@ -2914,7 +2918,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "f3894282", + "id": "890bc8b7", "metadata": { "collapsed": false, "editable": true @@ -2940,7 +2944,7 @@ }, { "cell_type": "markdown", - "id": "25c08547", + "id": "1acc9cfe", "metadata": { "editable": true }, @@ -2968,7 +2972,7 @@ }, { "cell_type": "markdown", - "id": "96a5c9b5", + "id": "e13f966a", "metadata": { "editable": true }, @@ -3005,7 +3009,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "948f1d5c", + "id": "8df5db3a", "metadata": { "collapsed": false, "editable": true @@ -3051,7 +3055,7 @@ }, { "cell_type": "markdown", - "id": "6a48b541", + "id": "aa73fc1e", "metadata": { "editable": true }, @@ -3082,7 +3086,7 @@ }, { "cell_type": "markdown", - "id": "92ae85de", + "id": "74081e44", "metadata": { "editable": true }, @@ -3120,7 +3124,7 @@ }, { "cell_type": "markdown", - "id": "50af9f08", + "id": "1e1c4fe3", "metadata": { "editable": true }, @@ -3154,7 +3158,7 @@ }, { "cell_type": "markdown", - "id": "947d899e", + "id": "6f66a9ad", "metadata": { "editable": true }, @@ -3195,7 +3199,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "9ac16dfb", + "id": "b4ba72fe", "metadata": { "collapsed": false, "editable": true @@ -3274,7 +3278,7 @@ }, { "cell_type": "markdown", - "id": "15fa07c3", + "id": "1bf824b2", "metadata": { "editable": true }, @@ -3295,7 +3299,7 @@ }, { "cell_type": "markdown", - "id": "e8e7304b", + "id": "c98cec8d", "metadata": { "editable": true }, @@ -3309,7 +3313,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "da41ccbe", + "id": "26d4691e", "metadata": { "collapsed": false, "editable": true @@ -3419,7 +3423,7 @@ }, { "cell_type": "markdown", - "id": "6ed32b94", + "id": "24f75613", "metadata": { "editable": true }, @@ -3438,7 +3442,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "a2566347", + "id": "52a987ae", "metadata": { "collapsed": false, "editable": true @@ -3465,7 +3469,7 @@ }, { "cell_type": "markdown", - "id": "779ebe17", + "id": "1605eb14", "metadata": { "editable": true }, @@ -3479,7 +3483,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "f55e622e", + "id": "952131d2", "metadata": { "collapsed": false, "editable": true @@ -3510,7 +3514,7 @@ }, { "cell_type": "markdown", - "id": "4a2ea09c", + "id": "182a8a49", "metadata": { "editable": true }, @@ -3521,7 +3525,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "d1dc32a5", + "id": "d3d1b7d5", "metadata": { "collapsed": false, "editable": true @@ -3565,7 +3569,7 @@ }, { "cell_type": "markdown", - "id": "fb02adc3", + "id": "aa705900", "metadata": { "editable": true }, @@ -3588,7 +3592,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "94531c6e", + "id": "09c32314", "metadata": { "collapsed": false, "editable": true @@ -3615,7 +3619,7 @@ }, { "cell_type": "markdown", - "id": "1373fc77", + "id": "fde4721e", "metadata": { "editable": true }, @@ -3626,7 +3630,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "2693a59e", + "id": "e855729e", "metadata": { "collapsed": false, "editable": true @@ -3671,7 +3675,7 @@ }, { "cell_type": "markdown", - "id": "a7e93079", + "id": "765ac227", "metadata": { "editable": true }, @@ -3698,7 +3702,7 @@ }, { "cell_type": "markdown", - "id": "5ed2f79a", + "id": "02d395cd", "metadata": { "editable": true }, @@ -3736,7 +3740,7 @@ }, { "cell_type": "markdown", - "id": "cbf09e4f", + "id": "0fba1e7d", "metadata": { "editable": true }, @@ -3748,7 +3752,7 @@ }, { "cell_type": "markdown", - "id": "fbb06edc", + "id": "9a3c4e68", "metadata": { "editable": true }, @@ -3763,7 +3767,7 @@ }, { "cell_type": "markdown", - "id": "9859c6a0", + "id": "545fccde", "metadata": { "editable": true }, @@ -3773,7 +3777,7 @@ }, { "cell_type": "markdown", - "id": "d71c8649", + "id": "678395d0", "metadata": { "editable": true }, @@ -3786,7 +3790,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "e8dc02bd", + "id": "7c64daef", "metadata": { "collapsed": false, "editable": true @@ -3862,7 +3866,7 @@ }, { "cell_type": "markdown", - "id": "87cfac77", + "id": "c35ff8f2", "metadata": { "editable": true }, @@ -3872,7 +3876,7 @@ }, { "cell_type": "markdown", - "id": "fa50c112", + "id": "3b5396c0", "metadata": { "editable": true }, @@ -3883,7 +3887,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "6673546e", + "id": "3d4e508b", "metadata": { "collapsed": false, "editable": true diff --git a/doc/LectureNotes/week42.ipynb b/doc/LectureNotes/week42.ipynb index 6a5cf09c3..99df66e64 100644 --- a/doc/LectureNotes/week42.ipynb +++ b/doc/LectureNotes/week42.ipynb @@ -2,8 +2,10 @@ "cells": [ { "cell_type": "markdown", - "id": "50ce4eae", - "metadata": {}, + "id": "bbaa1aec", + "metadata": { + "editable": true + }, "source": [ "\n", @@ -12,8 +14,10 @@ }, { "cell_type": "markdown", - "id": "f46bd6b4", - "metadata": {}, + "id": "d981139a", + "metadata": { + "editable": true + }, "source": [ "# Week 42 Constructing a Neural Network code with introduction to Tensor flow\n", "**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and Facility for Rare Isotope Beams, Michigan State University\n", @@ -23,8 +27,10 @@ }, { "cell_type": "markdown", - "id": "8c0fa4d7", - "metadata": {}, + "id": "d3d2fdcb", + "metadata": { + "editable": true + }, "source": [ "## Plan for week 42\n", "\n", @@ -46,6 +52,10 @@ "\n", " * These lecture notes\n", "\n", + " * [Video of lecture](https://youtu.be/0q5-PhovchQ)\n", + "\n", + " * [Whiteboard notes](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesOct19.pdf)\n", + "\n", " * [Aurelien Geron's chapters 10-11](https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf)\n", "\n", " * For a more in depth discussion on neural networks we recommend Goodfellow et al chapters 6 and 7. \n", @@ -63,16 +73,20 @@ }, { "cell_type": "markdown", - "id": "89b6b637", - "metadata": {}, + "id": "8e0c0ad3", + "metadata": { + "editable": true + }, "source": [ "## Lecture Thursday October 19" ] }, { "cell_type": "markdown", - "id": "3a32ad82", - "metadata": {}, + "id": "6d072b79", + "metadata": { + "editable": true + }, "source": [ "## Review of the back propagation algorithm\n", "\n", @@ -84,8 +98,10 @@ }, { "cell_type": "markdown", - "id": "4f9291ee", - "metadata": {}, + "id": "6a5894d2", + "metadata": { + "editable": true + }, "source": [ "## Setting up the Back propagation algorithm\n", "\n", @@ -105,8 +121,10 @@ }, { "cell_type": "markdown", - "id": "7753981f", - "metadata": {}, + "id": "47296efd", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\delta_j^L = f'(z_j^L)\\frac{\\partial {\\cal C}}{\\partial (a_j^L)}.\n", @@ -115,16 +133,20 @@ }, { "cell_type": "markdown", - "id": "8b093c71", - "metadata": {}, + "id": "598d3a19", + "metadata": { + "editable": true + }, "source": [ "Then we compute the back propagate error for each $l=L-1,L-2,\\dots,2$ as" ] }, { "cell_type": "markdown", - "id": "96ca25bd", - "metadata": {}, + "id": "7077d9c2", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\delta_j^l = \\sum_k \\delta_k^{l+1}w_{kj}^{l+1}f'(z_j^l).\n", @@ -133,16 +155,20 @@ }, { "cell_type": "markdown", - "id": "a156d8bd", - "metadata": {}, + "id": "c62043b3", + "metadata": { + "editable": true + }, "source": [ "Finally, we update the weights and the biases using gradient descent for each $l=L-1,L-2,\\dots,2$ and update the weights and biases according to the rules" ] }, { "cell_type": "markdown", - "id": "f35c8afe", - "metadata": {}, + "id": "3307a4bc", + "metadata": { + "editable": true + }, "source": [ "$$\n", "w_{jk}^l\\leftarrow = w_{jk}^l- \\eta \\delta_j^la_k^{l-1},\n", @@ -151,8 +177,10 @@ }, { "cell_type": "markdown", - "id": "ffa6d322", - "metadata": {}, + "id": "50db23f1", + "metadata": { + "editable": true + }, "source": [ "$$\n", "b_j^l \\leftarrow b_j^l-\\eta \\frac{\\partial {\\cal C}}{\\partial b_j^l}=b_j^l-\\eta \\delta_j^l,\n", @@ -161,8 +189,10 @@ }, { "cell_type": "markdown", - "id": "7b6e59f6", - "metadata": {}, + "id": "0cf89ca4", + "metadata": { + "editable": true + }, "source": [ "The parameter $\\eta$ is the learning parameter discussed in connection with the gradient descent methods.\n", "Here it is convenient to use stochastic gradient descent (see the examples below) with mini-batches with an outer loop that steps through multiple epochs of training." @@ -170,8 +200,10 @@ }, { "cell_type": "markdown", - "id": "e93ff00c", - "metadata": {}, + "id": "d5374d6f", + "metadata": { + "editable": true + }, "source": [ "## Setting up a Multi-layer perceptron model for classification\n", "\n", @@ -196,8 +228,10 @@ }, { "cell_type": "markdown", - "id": "3c437395", - "metadata": {}, + "id": "fc8ce130", + "metadata": { + "editable": true + }, "source": [ "$$\n", "P(y = 0 \\mid \\boldsymbol{x}, \\boldsymbol{\\theta}) = \\frac{1}{1 + \\exp{(- \\boldsymbol{x}})} ,\n", @@ -206,16 +240,20 @@ }, { "cell_type": "markdown", - "id": "3d7b1140", - "metadata": {}, + "id": "8eaf0c3c", + "metadata": { + "editable": true + }, "source": [ "and" ] }, { "cell_type": "markdown", - "id": "e3df5aec", - "metadata": {}, + "id": "3caeb6b3", + "metadata": { + "editable": true + }, "source": [ "$$\n", "P(y = 1 \\mid \\boldsymbol{x}, \\boldsymbol{\\theta}) = 1 - P(y = 0 \\mid \\boldsymbol{x}, \\boldsymbol{\\theta}) ,\n", @@ -224,8 +262,10 @@ }, { "cell_type": "markdown", - "id": "63345646", - "metadata": {}, + "id": "cb5b4f3d", + "metadata": { + "editable": true + }, "source": [ "where $y \\in \\{0, 1\\}$ and $\\boldsymbol{\\theta}$ represents the weights and biases\n", "of our network." @@ -233,8 +273,10 @@ }, { "cell_type": "markdown", - "id": "6ac465b3", - "metadata": {}, + "id": "6edbd945", + "metadata": { + "editable": true + }, "source": [ "## Defining the cost function\n", "\n", @@ -243,8 +285,10 @@ }, { "cell_type": "markdown", - "id": "cf06b4a0", - "metadata": {}, + "id": "3e039295", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\mathcal{C}(\\boldsymbol{\\theta}) = - \\ln P(\\mathcal{D} \\mid \\boldsymbol{\\theta}) = - \\sum_{i=1}^n\n", @@ -254,8 +298,10 @@ }, { "cell_type": "markdown", - "id": "719f761f", - "metadata": {}, + "id": "60d3b57c", + "metadata": { + "editable": true + }, "source": [ "This last equality means that we can interpret our *cost* function as a sum over the *loss* function\n", "for each point in the dataset $\\mathcal{L}_i(\\boldsymbol{\\theta})$. \n", @@ -277,8 +323,10 @@ }, { "cell_type": "markdown", - "id": "342de1d9", - "metadata": {}, + "id": "9045875f", + "metadata": { + "editable": true + }, "source": [ "$$\n", "P(y_{ic} = 1 \\mid \\boldsymbol{x}_i, \\boldsymbol{\\theta}) = \\frac{\\exp{((\\boldsymbol{a}_i^{hidden})^T \\boldsymbol{w}_c)}}\n", @@ -288,8 +336,10 @@ }, { "cell_type": "markdown", - "id": "211a69ba", - "metadata": {}, + "id": "1d37a3a2", + "metadata": { + "editable": true + }, "source": [ "which reduces to the logistic function in the binary case. \n", "The likelihood of this $C$-class classifier\n", @@ -298,8 +348,10 @@ }, { "cell_type": "markdown", - "id": "5f0cd5a2", - "metadata": {}, + "id": "429c3549", + "metadata": { + "editable": true + }, "source": [ "$$\n", "P(\\mathcal{D} \\mid \\boldsymbol{\\theta}) = \\prod_{i=1}^n \\prod_{c=0}^{C-1} [P(y_{ic} = 1)]^{y_{ic}} .\n", @@ -308,16 +360,20 @@ }, { "cell_type": "markdown", - "id": "fe018e32", - "metadata": {}, + "id": "cde118d9", + "metadata": { + "editable": true + }, "source": [ "Again we take the negative log-likelihood to define our cost function:" ] }, { "cell_type": "markdown", - "id": "9d48faca", - "metadata": {}, + "id": "16740280", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\mathcal{C}(\\boldsymbol{\\theta}) = - \\log{P(\\mathcal{D} \\mid \\boldsymbol{\\theta})}.\n", @@ -326,8 +382,10 @@ }, { "cell_type": "markdown", - "id": "897c8b0c", - "metadata": {}, + "id": "a4b60c6f", + "metadata": { + "editable": true + }, "source": [ "See the logistic regression lectures for a full definition of the cost function.\n", "\n", @@ -336,8 +394,10 @@ }, { "cell_type": "markdown", - "id": "68347a7f", - "metadata": {}, + "id": "36cce044", + "metadata": { + "editable": true + }, "source": [ "## Example: binary classification problem\n", "\n", @@ -346,8 +406,10 @@ }, { "cell_type": "markdown", - "id": "8425d868", - "metadata": {}, + "id": "d2cc5185", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\mathcal{C}(\\boldsymbol{\\beta}) = - \\sum_{i=1}^n \\left(y_i\\log{p(y_i \\vert x_i,\\boldsymbol{\\beta})}+(1-y_i)\\log{1-p(y_i \\vert x_i,\\boldsymbol{\\beta})}\\right),\n", @@ -356,16 +418,20 @@ }, { "cell_type": "markdown", - "id": "9108d4ac", - "metadata": {}, + "id": "6f62ac34", + "metadata": { + "editable": true + }, "source": [ "where we had defined the logistic (sigmoid) function" ] }, { "cell_type": "markdown", - "id": "77e0ec3b", - "metadata": {}, + "id": "980d2595", + "metadata": { + "editable": true + }, "source": [ "$$\n", "p(y_i =1\\vert x_i,\\boldsymbol{\\beta})=\\frac{\\exp{(\\beta_0+\\beta_1 x_i)}}{1+\\exp{(\\beta_0+\\beta_1 x_i)}},\n", @@ -374,16 +440,20 @@ }, { "cell_type": "markdown", - "id": "64ed867c", - "metadata": {}, + "id": "07f96bba", + "metadata": { + "editable": true + }, "source": [ "and" ] }, { "cell_type": "markdown", - "id": "51819578", - "metadata": {}, + "id": "ab7ef463", + "metadata": { + "editable": true + }, "source": [ "$$\n", "p(y_i =0\\vert x_i,\\boldsymbol{\\beta})=1-p(y_i =1\\vert x_i,\\boldsymbol{\\beta}).\n", @@ -392,8 +462,10 @@ }, { "cell_type": "markdown", - "id": "db6532a5", - "metadata": {}, + "id": "712f14c5", + "metadata": { + "editable": true + }, "source": [ "The parameters $\\boldsymbol{\\beta}$ were defined using a minimization method like gradient descent or Newton-Raphson's method. \n", "\n", @@ -403,8 +475,10 @@ }, { "cell_type": "markdown", - "id": "24e5e213", - "metadata": {}, + "id": "efb3f21c", + "metadata": { + "editable": true + }, "source": [ "$$\n", "a_i^l = y_i = \\frac{\\exp{(z_i^l)}}{1+\\exp{(z_i^l)}},\n", @@ -413,16 +487,20 @@ }, { "cell_type": "markdown", - "id": "d398c961", - "metadata": {}, + "id": "661dd5e4", + "metadata": { + "editable": true + }, "source": [ "with" ] }, { "cell_type": "markdown", - "id": "236d161c", - "metadata": {}, + "id": "545879f3", + "metadata": { + "editable": true + }, "source": [ "$$\n", "z_i^l = \\sum_{j}w_{ij}^l a_j^{l-1}+b_i^l,\n", @@ -431,8 +509,10 @@ }, { "cell_type": "markdown", - "id": "25e3004d", - "metadata": {}, + "id": "20187a39", + "metadata": { + "editable": true + }, "source": [ "where the superscript $l-1$ indicates that these are the outputs from layer $l-1$.\n", "Our cost function at the final layer $l=L$ is now" @@ -440,8 +520,10 @@ }, { "cell_type": "markdown", - "id": "9440c725", - "metadata": {}, + "id": "ecd3c551", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\mathcal{C}(\\boldsymbol{W}) = - \\sum_{i=1}^n \\left(t_i\\log{a_i^L}+(1-t_i)\\log{(1-a_i^L)}\\right),\n", @@ -450,16 +532,20 @@ }, { "cell_type": "markdown", - "id": "782f5282", - "metadata": {}, + "id": "03d1bd2b", + "metadata": { + "editable": true + }, "source": [ "where we have defined the targets $t_i$. The derivatives of the cost function with respect to the output $a_i^L$ are then easily calculated and we get" ] }, { "cell_type": "markdown", - "id": "0e8498a5", - "metadata": {}, + "id": "1baaf3b0", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\frac{\\partial \\mathcal{C}(\\boldsymbol{W})}{\\partial a_i^L} = \\frac{a_i^L-t_i}{a_i^L(1-a_i^L)}.\n", @@ -468,16 +554,20 @@ }, { "cell_type": "markdown", - "id": "68398b35", - "metadata": {}, + "id": "9114b454", + "metadata": { + "editable": true + }, "source": [ "In case we use another activation function than the logistic one, we need to evaluate other derivatives." ] }, { "cell_type": "markdown", - "id": "19887152", - "metadata": {}, + "id": "19b41dd4", + "metadata": { + "editable": true + }, "source": [ "## The Softmax function\n", "In case we employ the more general case given by the Softmax equation, we need to evaluate the derivative of the activation function with respect to the activation $z_i^l$, that is we need" @@ -485,8 +575,10 @@ }, { "cell_type": "markdown", - "id": "80e8dc5d", - "metadata": {}, + "id": "bc1b97c5", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\frac{\\partial f(z_i^l)}{\\partial w_{jk}^l} =\n", @@ -496,16 +588,20 @@ }, { "cell_type": "markdown", - "id": "68d33776", - "metadata": {}, + "id": "52f2e768", + "metadata": { + "editable": true + }, "source": [ "For the Softmax function we have" ] }, { "cell_type": "markdown", - "id": "3c86943c", - "metadata": {}, + "id": "1a60c363", + "metadata": { + "editable": true + }, "source": [ "$$\n", "f(z_i^l) = \\frac{\\exp{(z_i^l)}}{\\sum_{m=1}^K\\exp{(z_m^l)}}.\n", @@ -514,16 +610,20 @@ }, { "cell_type": "markdown", - "id": "efe53876", - "metadata": {}, + "id": "93eb34b6", + "metadata": { + "editable": true + }, "source": [ "Its derivative with respect to $z_j^l$ gives" ] }, { "cell_type": "markdown", - "id": "fce5b9b2", - "metadata": {}, + "id": "aa26229f", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\frac{\\partial f(z_i^l)}{\\partial z_j^l}= f(z_i^l)\\left(\\delta_{ij}-f(z_j^l)\\right),\n", @@ -532,16 +632,20 @@ }, { "cell_type": "markdown", - "id": "97210471", - "metadata": {}, + "id": "1f075e8c", + "metadata": { + "editable": true + }, "source": [ "which in case of the simply binary model reduces to having $i=j$." ] }, { "cell_type": "markdown", - "id": "4f515591", - "metadata": {}, + "id": "b0cb8b0e", + "metadata": { + "editable": true + }, "source": [ "## Developing a code for doing neural networks with back propagation\n", "\n", @@ -562,8 +666,10 @@ }, { "cell_type": "markdown", - "id": "ec34f212", - "metadata": {}, + "id": "c4cd71b6", + "metadata": { + "editable": true + }, "source": [ "## Collect and pre-process data\n", "\n", @@ -610,8 +716,11 @@ { "cell_type": "code", "execution_count": 1, - "id": "e389e60e", - "metadata": {}, + "id": "ca43227b", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "%matplotlib inline\n", @@ -662,8 +771,10 @@ }, { "cell_type": "markdown", - "id": "9a264b82", - "metadata": {}, + "id": "79f4798f", + "metadata": { + "editable": true + }, "source": [ "## Train and test datasets\n", "\n", @@ -681,8 +792,11 @@ { "cell_type": "code", "execution_count": 2, - "id": "8750ea41", - "metadata": {}, + "id": "38e01634", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", @@ -716,8 +830,10 @@ }, { "cell_type": "markdown", - "id": "d3897eca", - "metadata": {}, + "id": "faca5ec2", + "metadata": { + "editable": true + }, "source": [ "## Define model and architecture\n", "\n", @@ -758,8 +874,10 @@ }, { "cell_type": "markdown", - "id": "ad593a03", - "metadata": {}, + "id": "e720f042", + "metadata": { + "editable": true + }, "source": [ "## Layers\n", "\n", @@ -796,8 +914,10 @@ }, { "cell_type": "markdown", - "id": "e37b3844", - "metadata": {}, + "id": "b4a3815d", + "metadata": { + "editable": true + }, "source": [ "## Weights and biases\n", "\n", @@ -815,8 +935,11 @@ { "cell_type": "code", "execution_count": 3, - "id": "3d909fc7", - "metadata": {}, + "id": "5c7ae6ce", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "# building our neural network\n", @@ -838,8 +961,10 @@ }, { "cell_type": "markdown", - "id": "b89c2d9f", - "metadata": {}, + "id": "bc289dbd", + "metadata": { + "editable": true + }, "source": [ "## Feed-forward pass\n", "\n", @@ -864,8 +989,10 @@ }, { "cell_type": "markdown", - "id": "435c0ced", - "metadata": {}, + "id": "3e93f012", + "metadata": { + "editable": true + }, "source": [ "## Matrix multiplications\n", "\n", @@ -899,8 +1026,11 @@ { "cell_type": "code", "execution_count": 4, - "id": "3037d7ab", - "metadata": {}, + "id": "31084597", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "# setup the feed-forward pass, subscript h = hidden layer\n", @@ -942,8 +1072,10 @@ }, { "cell_type": "markdown", - "id": "61c33a3f", - "metadata": {}, + "id": "93ca9a82", + "metadata": { + "editable": true + }, "source": [ "## Choose cost function and optimizer\n", "\n", @@ -971,8 +1103,10 @@ }, { "cell_type": "markdown", - "id": "665f44ff", - "metadata": {}, + "id": "59ad4e01", + "metadata": { + "editable": true + }, "source": [ "## Optimizing the cost function\n", "\n", @@ -1007,8 +1141,10 @@ }, { "cell_type": "markdown", - "id": "2d0168d0", - "metadata": {}, + "id": "d017d149", + "metadata": { + "editable": true + }, "source": [ "## Regularization\n", "\n", @@ -1039,8 +1175,10 @@ }, { "cell_type": "markdown", - "id": "9c0a8db3", - "metadata": {}, + "id": "3b624b6e", + "metadata": { + "editable": true + }, "source": [ "## Matrix multiplication\n", "\n", @@ -1078,8 +1216,11 @@ { "cell_type": "code", "execution_count": 5, - "id": "0bf3739e", - "metadata": {}, + "id": "39eabb7a", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "# to categorical turns our integer vector into a onehot representation\n", @@ -1154,8 +1295,10 @@ }, { "cell_type": "markdown", - "id": "33e198f3", - "metadata": {}, + "id": "22c14a38", + "metadata": { + "editable": true + }, "source": [ "## Improving performance\n", "\n", @@ -1173,8 +1316,10 @@ }, { "cell_type": "markdown", - "id": "932f6c5e", - "metadata": {}, + "id": "33d33cf6", + "metadata": { + "editable": true + }, "source": [ "## Full object-oriented implementation\n", "\n", @@ -1185,8 +1330,11 @@ { "cell_type": "code", "execution_count": 6, - "id": "91e351de", - "metadata": {}, + "id": "a5009498", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "class NeuralNetwork:\n", @@ -1292,8 +1440,10 @@ }, { "cell_type": "markdown", - "id": "e8c2feb6", - "metadata": {}, + "id": "68639daa", + "metadata": { + "editable": true + }, "source": [ "## Evaluate model performance on test data\n", "\n", @@ -1309,8 +1459,11 @@ { "cell_type": "code", "execution_count": 7, - "id": "1534af1b", - "metadata": {}, + "id": "487c6612", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "epochs = 100\n", @@ -1333,8 +1486,10 @@ }, { "cell_type": "markdown", - "id": "85627e28", - "metadata": {}, + "id": "c3b10024", + "metadata": { + "editable": true + }, "source": [ "## Adjust hyperparameters\n", "\n", @@ -1345,8 +1500,11 @@ { "cell_type": "code", "execution_count": 8, - "id": "19382903", - "metadata": {}, + "id": "7ab55f7a", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "eta_vals = np.logspace(-5, 1, 7)\n", @@ -1373,8 +1531,10 @@ }, { "cell_type": "markdown", - "id": "12bc42df", - "metadata": {}, + "id": "aa9d91e2", + "metadata": { + "editable": true + }, "source": [ "## Visualization" ] @@ -1382,8 +1542,11 @@ { "cell_type": "code", "execution_count": 9, - "id": "ec0dc239", - "metadata": {}, + "id": "ce6b84ae", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "# visual representation of grid search\n", @@ -1423,8 +1586,10 @@ }, { "cell_type": "markdown", - "id": "4dd39506", - "metadata": {}, + "id": "1d50ccf4", + "metadata": { + "editable": true + }, "source": [ "## scikit-learn implementation\n", "\n", @@ -1444,8 +1609,11 @@ { "cell_type": "code", "execution_count": 10, - "id": "d9dbb807", - "metadata": {}, + "id": "05cc9271", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "from sklearn.neural_network import MLPClassifier\n", @@ -1468,8 +1636,10 @@ }, { "cell_type": "markdown", - "id": "214af3ab", - "metadata": {}, + "id": "9f51a74d", + "metadata": { + "editable": true + }, "source": [ "## Visualization" ] @@ -1477,8 +1647,11 @@ { "cell_type": "code", "execution_count": 11, - "id": "d57415ac", - "metadata": {}, + "id": "38a896b8", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "# optional\n", @@ -1519,8 +1692,10 @@ }, { "cell_type": "markdown", - "id": "fe7af77c", - "metadata": {}, + "id": "f8ee3eb3", + "metadata": { + "editable": true + }, "source": [ "## Testing our code for the XOR, OR and AND gates\n", "\n", @@ -1544,8 +1719,10 @@ }, { "cell_type": "markdown", - "id": "7e12b1cf", - "metadata": {}, + "id": "5c0e406c", + "metadata": { + "editable": true + }, "source": [ "## The AND and XOR Gates\n", "\n", @@ -1580,8 +1757,10 @@ }, { "cell_type": "markdown", - "id": "4b5002b4", - "metadata": {}, + "id": "f52ee7dd", + "metadata": { + "editable": true + }, "source": [ "## Representing the Data Sets\n", "\n", @@ -1590,8 +1769,10 @@ }, { "cell_type": "markdown", - "id": "a44df1a3", - "metadata": {}, + "id": "f2634e6f", + "metadata": { + "editable": true + }, "source": [ "$$\n", "\\boldsymbol{X}=\\begin{bmatrix} 0 & 0 \\\\\n", @@ -1603,16 +1784,20 @@ }, { "cell_type": "markdown", - "id": "acdb4e08", - "metadata": {}, + "id": "a39715fe", + "metadata": { + "editable": true + }, "source": [ "while the vector of outputs is $\\boldsymbol{y}^T=[0,1,1,0]$ for the XOR gate, $\\boldsymbol{y}^T=[0,0,0,1]$ for the AND gate and $\\boldsymbol{y}^T=[0,1,1,1]$ for the OR gate." ] }, { "cell_type": "markdown", - "id": "0567fd0f", - "metadata": {}, + "id": "8bff01b2", + "metadata": { + "editable": true + }, "source": [ "## Setting up the Neural Network\n", "\n", @@ -1622,8 +1807,11 @@ { "cell_type": "code", "execution_count": 12, - "id": "412401df", - "metadata": {}, + "id": "9d94da1e", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "\"\"\"\n", @@ -1695,16 +1883,20 @@ }, { "cell_type": "markdown", - "id": "53c52dab", - "metadata": {}, + "id": "fdbce6ba", + "metadata": { + "editable": true + }, "source": [ "Not an impressive result, but this was our first forward pass with randomly assigned weights. Let us now add the full network with the back-propagation algorithm discussed above." ] }, { "cell_type": "markdown", - "id": "7d192a02", - "metadata": {}, + "id": "f5cbb06d", + "metadata": { + "editable": true + }, "source": [ "## The Code using Scikit-Learn" ] @@ -1712,8 +1904,11 @@ { "cell_type": "code", "execution_count": 13, - "id": "766d5af6", - "metadata": {}, + "id": "edf69ad3", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "# import necessary packages\n", @@ -1777,8 +1972,10 @@ }, { "cell_type": "markdown", - "id": "9b182ae1", - "metadata": {}, + "id": "d5e5d8a0", + "metadata": { + "editable": true + }, "source": [ "## Building neural networks in Tensorflow and Keras\n", "\n", @@ -1793,8 +1990,10 @@ }, { "cell_type": "markdown", - "id": "60683ec5", - "metadata": {}, + "id": "8326a878", + "metadata": { + "editable": true + }, "source": [ "## Tensorflow\n", "\n", @@ -1826,8 +2025,11 @@ { "cell_type": "code", "execution_count": 14, - "id": "8a0c6901", - "metadata": {}, + "id": "dd988cfc", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "pip3 install tensorflow" @@ -1835,8 +2037,10 @@ }, { "cell_type": "markdown", - "id": "b66e0227", - "metadata": {}, + "id": "d2f8d6f3", + "metadata": { + "editable": true + }, "source": [ "and/or if you use **anaconda**, just write (or install from the graphical user interface)\n", "(current release of CPU-only TensorFlow)" @@ -1845,8 +2049,11 @@ { "cell_type": "code", "execution_count": 15, - "id": "df994f58", - "metadata": {}, + "id": "fdaffccc", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "conda create -n tf tensorflow\n", @@ -1855,8 +2062,10 @@ }, { "cell_type": "markdown", - "id": "b9005559", - "metadata": {}, + "id": "80db034a", + "metadata": { + "editable": true + }, "source": [ "To install the current release of GPU TensorFlow" ] @@ -1864,8 +2073,11 @@ { "cell_type": "code", "execution_count": 16, - "id": "7287b5eb", - "metadata": {}, + "id": "e632c541", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "conda create -n tf-gpu tensorflow-gpu\n", @@ -1874,8 +2086,10 @@ }, { "cell_type": "markdown", - "id": "c066b083", - "metadata": {}, + "id": "605ac1fd", + "metadata": { + "editable": true + }, "source": [ "## Using Keras\n", "\n", @@ -1887,8 +2101,11 @@ { "cell_type": "code", "execution_count": 17, - "id": "6582adea", - "metadata": {}, + "id": "11e69ce0", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "conda install keras" @@ -1896,8 +2113,10 @@ }, { "cell_type": "markdown", - "id": "7d305596", - "metadata": {}, + "id": "176bf3ac", + "metadata": { + "editable": true + }, "source": [ "You can look up the [instructions here](https://keras.io/) for more information.\n", "\n", @@ -1906,8 +2125,10 @@ }, { "cell_type": "markdown", - "id": "a4508850", - "metadata": {}, + "id": "7a085449", + "metadata": { + "editable": true + }, "source": [ "## Collect and pre-process data\n", "\n", @@ -1917,8 +2138,11 @@ { "cell_type": "code", "execution_count": 18, - "id": "5f2256f6", - "metadata": {}, + "id": "d8bae540", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "# import necessary packages\n", @@ -1969,8 +2193,11 @@ { "cell_type": "code", "execution_count": 19, - "id": "a5dfa0e9", - "metadata": {}, + "id": "5608d691", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "from tensorflow.keras.layers import Input\n", @@ -1995,8 +2222,11 @@ { "cell_type": "code", "execution_count": 20, - "id": "dd935ce0", - "metadata": {}, + "id": "7bce7422", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "\n", @@ -2022,8 +2252,11 @@ { "cell_type": "code", "execution_count": 21, - "id": "67158cb2", - "metadata": {}, + "id": "65a68468", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "DNN_keras = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)\n", @@ -2046,8 +2279,11 @@ { "cell_type": "code", "execution_count": 22, - "id": "86d74ee3", - "metadata": {}, + "id": "45ae200f", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "# optional\n", @@ -2085,8 +2321,10 @@ }, { "cell_type": "markdown", - "id": "563d3f68", - "metadata": {}, + "id": "b955ad39", + "metadata": { + "editable": true + }, "source": [ "## The Breast Cancer Data, now with Keras" ] @@ -2094,8 +2332,11 @@ { "cell_type": "code", "execution_count": 23, - "id": "34e6467a", - "metadata": {}, + "id": "8ed2e257", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "\n", @@ -2268,8 +2509,10 @@ }, { "cell_type": "markdown", - "id": "09879108", - "metadata": {}, + "id": "107bab25", + "metadata": { + "editable": true + }, "source": [ "## Fine-tuning neural network hyperparameters\n", "\n", @@ -2294,8 +2537,10 @@ }, { "cell_type": "markdown", - "id": "f8ec1769", - "metadata": {}, + "id": "53ad43ce", + "metadata": { + "editable": true + }, "source": [ "## Hidden layers\n", "\n", @@ -2315,8 +2560,10 @@ }, { "cell_type": "markdown", - "id": "43cc1fe5", - "metadata": {}, + "id": "6f613497", + "metadata": { + "editable": true + }, "source": [ "## Which activation function should I use?\n", "\n", @@ -2344,8 +2591,10 @@ }, { "cell_type": "markdown", - "id": "a9cbce9f", - "metadata": {}, + "id": "91843fee", + "metadata": { + "editable": true + }, "source": [ "## Is the Logistic activation function (Sigmoid) our choice?\n", "\n", @@ -2374,8 +2623,10 @@ }, { "cell_type": "markdown", - "id": "2dfb3f9a", - "metadata": {}, + "id": "83535426", + "metadata": { + "editable": true + }, "source": [ "## The derivative of the Logistic funtion\n", "\n", @@ -2410,8 +2661,10 @@ }, { "cell_type": "markdown", - "id": "f806c047", - "metadata": {}, + "id": "37bdca60", + "metadata": { + "editable": true + }, "source": [ "## The RELU function family\n", "\n", @@ -2433,8 +2686,10 @@ }, { "cell_type": "markdown", - "id": "ef9e2a08", - "metadata": {}, + "id": "11ae19d2", + "metadata": { + "editable": true + }, "source": [ "$$\n", "ELU(z) = \\left\\{\\begin{array}{cc} \\alpha\\left( \\exp{(z)}-1\\right) & z < 0,\\\\ z & z \\ge 0.\\end{array}\\right.\n", @@ -2443,8 +2698,10 @@ }, { "cell_type": "markdown", - "id": "2e2750d8", - "metadata": {}, + "id": "f3a54f08", + "metadata": { + "editable": true + }, "source": [ "## Which activation function should we use?\n", "\n", @@ -2464,8 +2721,10 @@ }, { "cell_type": "markdown", - "id": "4e566f13", - "metadata": {}, + "id": "4dd226db", + "metadata": { + "editable": true + }, "source": [ "## More on activation functions, output layers\n", "\n", @@ -2482,8 +2741,10 @@ }, { "cell_type": "markdown", - "id": "03205666", - "metadata": {}, + "id": "f9521d8f", + "metadata": { + "editable": true + }, "source": [ "## Batch Normalization\n", "\n", @@ -2502,8 +2763,10 @@ }, { "cell_type": "markdown", - "id": "ad7c3e53", - "metadata": {}, + "id": "080c7f12", + "metadata": { + "editable": true + }, "source": [ "## Dropout\n", "\n", @@ -2518,8 +2781,10 @@ }, { "cell_type": "markdown", - "id": "c3b98a7c", - "metadata": {}, + "id": "963e7d21", + "metadata": { + "editable": true + }, "source": [ "## Gradient Clipping\n", "\n", @@ -2535,8 +2800,10 @@ }, { "cell_type": "markdown", - "id": "e7f21477", - "metadata": {}, + "id": "0b69f45e", + "metadata": { + "editable": true + }, "source": [ "## A very nice website on Neural Networks\n", "\n", @@ -2545,8 +2812,10 @@ }, { "cell_type": "markdown", - "id": "54968291", - "metadata": {}, + "id": "3585dfbf", + "metadata": { + "editable": true + }, "source": [ "## A top-down perspective on Neural networks\n", "\n", @@ -2588,8 +2857,10 @@ }, { "cell_type": "markdown", - "id": "4500b85e", - "metadata": {}, + "id": "dd7c4575", + "metadata": { + "editable": true + }, "source": [ "## Limitations of supervised learning with deep networks\n", "\n", @@ -2615,25 +2886,7 @@ ] } ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.10" - } - }, + "metadata": {}, "nbformat": 4, "nbformat_minor": 5 } diff --git a/doc/LectureNotes/week43.ipynb b/doc/LectureNotes/week43.ipynb index 4e2246cb2..94b84df7a 100644 --- a/doc/LectureNotes/week43.ipynb +++ b/doc/LectureNotes/week43.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "082f351e", + "id": "42ce64ba", "metadata": { "editable": true }, @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "a99ac46a", + "id": "c987b86b", "metadata": { "editable": true }, @@ -29,7 +29,7 @@ }, { "cell_type": "markdown", - "id": "a65014fa", + "id": "0dc0d6ca", "metadata": { "editable": true }, @@ -54,6 +54,8 @@ "\n", " * Solving differential equations with Neural Networks and intro to **Tensorflow** with examples.\n", "\n", + " * [Video of lecture](https://youtu.be/_-AwbBh4G-8)\n", + "\n", " * Readings and Videos:\n", "\n", " * These lecture notes\n", @@ -75,7 +77,7 @@ }, { "cell_type": "markdown", - "id": "491f86f0", + "id": "f4d3253b", "metadata": { "editable": true }, @@ -89,7 +91,7 @@ }, { "cell_type": "markdown", - "id": "9878a552", + "id": "9744596b", "metadata": { "editable": true }, @@ -106,7 +108,7 @@ }, { "cell_type": "markdown", - "id": "acd9eaf4", + "id": "bd189f93", "metadata": { "editable": true }, @@ -116,7 +118,7 @@ }, { "cell_type": "markdown", - "id": "ac6985dd", + "id": "e901e3d4", "metadata": { "editable": true }, @@ -153,7 +155,7 @@ }, { "cell_type": "markdown", - "id": "c75ca508", + "id": "fca381f9", "metadata": { "editable": true }, @@ -191,7 +193,7 @@ }, { "cell_type": "markdown", - "id": "6b4c09b9", + "id": "f0252121", "metadata": { "editable": true }, @@ -203,7 +205,7 @@ }, { "cell_type": "markdown", - "id": "fe419b85", + "id": "f59fc5ee", "metadata": { "editable": true }, @@ -218,7 +220,7 @@ }, { "cell_type": "markdown", - "id": "79ddb224", + "id": "1d74ec2a", "metadata": { "editable": true }, @@ -248,7 +250,7 @@ }, { "cell_type": "markdown", - "id": "5b9b5e41", + "id": "8e6565f3", "metadata": { "editable": true }, @@ -260,7 +262,7 @@ }, { "cell_type": "markdown", - "id": "c319cc25", + "id": "7f55beb5", "metadata": { "editable": true }, @@ -273,7 +275,7 @@ }, { "cell_type": "markdown", - "id": "edf4b6c5", + "id": "ba515e4f", "metadata": { "editable": true }, @@ -283,7 +285,7 @@ }, { "cell_type": "markdown", - "id": "521c408b", + "id": "1007d026", "metadata": { "editable": true }, @@ -298,7 +300,7 @@ }, { "cell_type": "markdown", - "id": "22aa491a", + "id": "b1ee686a", "metadata": { "editable": true }, @@ -308,7 +310,7 @@ }, { "cell_type": "markdown", - "id": "f0ae234a", + "id": "2820cfdd", "metadata": { "editable": true }, @@ -321,7 +323,7 @@ }, { "cell_type": "markdown", - "id": "5c980f5b", + "id": "5b5f6d64", "metadata": { "editable": true }, @@ -331,7 +333,7 @@ }, { "cell_type": "markdown", - "id": "82fb504e", + "id": "0814e6d9", "metadata": { "editable": true }, @@ -346,7 +348,7 @@ }, { "cell_type": "markdown", - "id": "cfd54537", + "id": "9da10b99", "metadata": { "editable": true }, @@ -356,7 +358,7 @@ }, { "cell_type": "markdown", - "id": "2f823c42", + "id": "c0f3ede6", "metadata": { "editable": true }, @@ -371,7 +373,7 @@ }, { "cell_type": "markdown", - "id": "7cad12ae", + "id": "f00bc3ad", "metadata": { "editable": true }, @@ -381,7 +383,7 @@ }, { "cell_type": "markdown", - "id": "0e9795a8", + "id": "da749419", "metadata": { "editable": true }, @@ -394,7 +396,7 @@ }, { "cell_type": "markdown", - "id": "4ee23a12", + "id": "2e9135f9", "metadata": { "editable": true }, @@ -404,7 +406,7 @@ }, { "cell_type": "markdown", - "id": "1c156334", + "id": "a0d879df", "metadata": { "editable": true }, @@ -420,7 +422,7 @@ }, { "cell_type": "markdown", - "id": "24433060", + "id": "988a9a20", "metadata": { "editable": true }, @@ -430,7 +432,7 @@ }, { "cell_type": "markdown", - "id": "e2664fe2", + "id": "354ad4af", "metadata": { "editable": true }, @@ -443,7 +445,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "4966b41e", + "id": "1f04aac4", "metadata": { "collapsed": false, "editable": true @@ -513,7 +515,7 @@ }, { "cell_type": "markdown", - "id": "2d227b7a", + "id": "6e12253c", "metadata": { "editable": true }, @@ -523,7 +525,7 @@ }, { "cell_type": "markdown", - "id": "4a614a2f", + "id": "4ca80040", "metadata": { "editable": true }, @@ -534,7 +536,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "a2401c4c", + "id": "1b657a96", "metadata": { "collapsed": false, "editable": true @@ -599,7 +601,7 @@ }, { "cell_type": "markdown", - "id": "2b0654f9", + "id": "9f870fcf", "metadata": { "editable": true }, @@ -618,7 +620,7 @@ }, { "cell_type": "markdown", - "id": "36d7ed90", + "id": "75623483", "metadata": { "editable": true }, @@ -640,7 +642,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "f3149e44", + "id": "7884cf44", "metadata": { "collapsed": false, "editable": true @@ -781,7 +783,7 @@ }, { "cell_type": "markdown", - "id": "3b714e5a", + "id": "a9747db3", "metadata": { "editable": true }, @@ -797,7 +799,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "a187e1f4", + "id": "b394ebc2", "metadata": { "collapsed": false, "editable": true @@ -810,7 +812,7 @@ }, { "cell_type": "markdown", - "id": "14693dc1", + "id": "734ce228", "metadata": { "editable": true }, @@ -822,7 +824,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "6f791e05", + "id": "51396f90", "metadata": { "collapsed": false, "editable": true @@ -844,7 +846,7 @@ }, { "cell_type": "markdown", - "id": "02dca912", + "id": "44646fc6", "metadata": { "editable": true }, @@ -860,7 +862,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "8e150ca8", + "id": "912285f7", "metadata": { "collapsed": false, "editable": true @@ -898,7 +900,7 @@ }, { "cell_type": "markdown", - "id": "a2ba6469", + "id": "9297b955", "metadata": { "editable": true }, @@ -911,7 +913,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "2fd8a7d1", + "id": "39943643", "metadata": { "collapsed": false, "editable": true @@ -932,7 +934,7 @@ }, { "cell_type": "markdown", - "id": "811b2be7", + "id": "53f4d1d2", "metadata": { "editable": true }, @@ -948,7 +950,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "12fb8899", + "id": "90222b1a", "metadata": { "collapsed": false, "editable": true @@ -1006,7 +1008,7 @@ }, { "cell_type": "markdown", - "id": "9cd08f7a", + "id": "f3dd3611", "metadata": { "editable": true }, @@ -1021,7 +1023,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "3b609fd5", + "id": "ded43dca", "metadata": { "collapsed": false, "editable": true @@ -1042,7 +1044,7 @@ }, { "cell_type": "markdown", - "id": "7b55fa66", + "id": "771c53a1", "metadata": { "editable": true }, @@ -1066,7 +1068,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "c0f9ff63", + "id": "56ef870b", "metadata": { "collapsed": false, "editable": true @@ -1538,7 +1540,7 @@ }, { "cell_type": "markdown", - "id": "e406c59d", + "id": "cefab2ac", "metadata": { "editable": true }, @@ -1550,7 +1552,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "0c6d743d", + "id": "6d5b49a1", "metadata": { "collapsed": false, "editable": true @@ -1594,7 +1596,7 @@ }, { "cell_type": "markdown", - "id": "150ef783", + "id": "f857ae2b", "metadata": { "editable": true }, @@ -1610,7 +1612,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "585ea4aa", + "id": "2a92f970", "metadata": { "collapsed": false, "editable": true @@ -1625,7 +1627,7 @@ }, { "cell_type": "markdown", - "id": "169932be", + "id": "28236066", "metadata": { "editable": true }, @@ -1636,7 +1638,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "7559a5a7", + "id": "b3c6c87e", "metadata": { "collapsed": false, "editable": true @@ -1651,7 +1653,7 @@ }, { "cell_type": "markdown", - "id": "ee912b2a", + "id": "f5b6209f", "metadata": { "editable": true }, @@ -1667,7 +1669,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "b576d527", + "id": "59d5742b", "metadata": { "collapsed": false, "editable": true @@ -1681,7 +1683,7 @@ }, { "cell_type": "markdown", - "id": "e98b0dd4", + "id": "13e834ae", "metadata": { "editable": true }, @@ -1696,7 +1698,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "2508ac64", + "id": "ab390311", "metadata": { "collapsed": false, "editable": true @@ -1722,7 +1724,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "3b12683c", + "id": "f2d67c2e", "metadata": { "collapsed": false, "editable": true @@ -1737,7 +1739,7 @@ }, { "cell_type": "markdown", - "id": "e79ca9e9", + "id": "e350f60e", "metadata": { "editable": true }, @@ -1748,7 +1750,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "2238df76", + "id": "211a328d", "metadata": { "collapsed": false, "editable": true @@ -1763,7 +1765,7 @@ }, { "cell_type": "markdown", - "id": "f698a332", + "id": "8425a8fb", "metadata": { "editable": true }, @@ -1774,7 +1776,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "467be649", + "id": "1070e9eb", "metadata": { "collapsed": false, "editable": true @@ -1794,7 +1796,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "65fb9800", + "id": "c7171327", "metadata": { "collapsed": false, "editable": true @@ -1809,7 +1811,7 @@ }, { "cell_type": "markdown", - "id": "93cdd2a4", + "id": "9c37cc8a", "metadata": { "editable": true }, @@ -1824,7 +1826,7 @@ { "cell_type": "code", "execution_count": 20, - "id": "df4b428c", + "id": "f33e3872", "metadata": { "collapsed": false, "editable": true @@ -1861,7 +1863,7 @@ }, { "cell_type": "markdown", - "id": "00f7684a", + "id": "98281e67", "metadata": { "editable": true }, @@ -1874,7 +1876,7 @@ { "cell_type": "code", "execution_count": 21, - "id": "8bec151f", + "id": "24031566", "metadata": { "collapsed": false, "editable": true @@ -1897,7 +1899,7 @@ }, { "cell_type": "markdown", - "id": "c32f02fd", + "id": "8302830f", "metadata": { "editable": true }, @@ -1907,7 +1909,7 @@ }, { "cell_type": "markdown", - "id": "3434c341", + "id": "9003b71a", "metadata": { "editable": true }, @@ -1917,7 +1919,7 @@ }, { "cell_type": "markdown", - "id": "aea3ed79", + "id": "4446e61e", "metadata": { "editable": true }, @@ -1946,7 +1948,7 @@ }, { "cell_type": "markdown", - "id": "9aa79782", + "id": "de6e80a8", "metadata": { "editable": true }, @@ -1996,7 +1998,7 @@ { "cell_type": "code", "execution_count": 22, - "id": "cade684e", + "id": "011b021a", "metadata": { "collapsed": false, "editable": true @@ -2049,7 +2051,7 @@ }, { "cell_type": "markdown", - "id": "380d9d9d", + "id": "4de43fe8", "metadata": { "editable": true }, @@ -2070,7 +2072,7 @@ { "cell_type": "code", "execution_count": 23, - "id": "73cf1180", + "id": "d9281e06", "metadata": { "collapsed": false, "editable": true @@ -2108,7 +2110,7 @@ }, { "cell_type": "markdown", - "id": "8a01257a", + "id": "5d86b543", "metadata": { "editable": true }, @@ -2152,7 +2154,7 @@ }, { "cell_type": "markdown", - "id": "54e95850", + "id": "02df2616", "metadata": { "editable": true }, @@ -2192,7 +2194,7 @@ }, { "cell_type": "markdown", - "id": "62c1a1b0", + "id": "0a17cfeb", "metadata": { "editable": true }, @@ -2213,7 +2215,7 @@ { "cell_type": "code", "execution_count": 24, - "id": "aa587add", + "id": "94dd542a", "metadata": { "collapsed": false, "editable": true @@ -2239,7 +2241,7 @@ }, { "cell_type": "markdown", - "id": "d4249afa", + "id": "7c13f0a8", "metadata": { "editable": true }, @@ -2267,7 +2269,7 @@ }, { "cell_type": "markdown", - "id": "b699e117", + "id": "d1bae3d4", "metadata": { "editable": true }, @@ -2304,7 +2306,7 @@ { "cell_type": "code", "execution_count": 25, - "id": "8a4dec51", + "id": "20c03478", "metadata": { "collapsed": false, "editable": true @@ -2350,7 +2352,7 @@ }, { "cell_type": "markdown", - "id": "c2fbafa2", + "id": "42c28741", "metadata": { "editable": true }, @@ -2381,7 +2383,7 @@ }, { "cell_type": "markdown", - "id": "297dd16a", + "id": "62954b00", "metadata": { "editable": true }, @@ -2419,7 +2421,7 @@ }, { "cell_type": "markdown", - "id": "9a4bdbd5", + "id": "9f94aff8", "metadata": { "editable": true }, @@ -2453,7 +2455,7 @@ }, { "cell_type": "markdown", - "id": "aa585f10", + "id": "bf73f8e5", "metadata": { "editable": true }, @@ -2494,7 +2496,7 @@ { "cell_type": "code", "execution_count": 26, - "id": "21ec5e9c", + "id": "9ffb0561", "metadata": { "collapsed": false, "editable": true @@ -2573,7 +2575,7 @@ }, { "cell_type": "markdown", - "id": "fe383853", + "id": "3b19f8e5", "metadata": { "editable": true }, @@ -2594,7 +2596,7 @@ }, { "cell_type": "markdown", - "id": "48afb81d", + "id": "443db3c1", "metadata": { "editable": true }, @@ -2608,7 +2610,7 @@ { "cell_type": "code", "execution_count": 27, - "id": "223c5ea5", + "id": "5a7db6a1", "metadata": { "collapsed": false, "editable": true @@ -2718,7 +2720,7 @@ }, { "cell_type": "markdown", - "id": "8e4de167", + "id": "fe73ada1", "metadata": { "editable": true }, @@ -2737,7 +2739,7 @@ { "cell_type": "code", "execution_count": 28, - "id": "e299478f", + "id": "3c71da93", "metadata": { "collapsed": false, "editable": true @@ -2764,7 +2766,7 @@ }, { "cell_type": "markdown", - "id": "1c58f8b5", + "id": "c6d594b6", "metadata": { "editable": true }, @@ -2778,7 +2780,7 @@ { "cell_type": "code", "execution_count": 29, - "id": "e2e095b5", + "id": "bb8c3746", "metadata": { "collapsed": false, "editable": true @@ -2809,7 +2811,7 @@ }, { "cell_type": "markdown", - "id": "9bcbed3e", + "id": "c460a08f", "metadata": { "editable": true }, @@ -2820,7 +2822,7 @@ { "cell_type": "code", "execution_count": 30, - "id": "70cd9e5f", + "id": "ae408ce7", "metadata": { "collapsed": false, "editable": true @@ -2864,7 +2866,7 @@ }, { "cell_type": "markdown", - "id": "014fa73f", + "id": "255351d8", "metadata": { "editable": true }, @@ -2887,7 +2889,7 @@ { "cell_type": "code", "execution_count": 31, - "id": "f8e412f7", + "id": "45102bf4", "metadata": { "collapsed": false, "editable": true @@ -2914,7 +2916,7 @@ }, { "cell_type": "markdown", - "id": "d05866a6", + "id": "9ed166d7", "metadata": { "editable": true }, @@ -2925,7 +2927,7 @@ { "cell_type": "code", "execution_count": 32, - "id": "1af34a20", + "id": "e9fb271f", "metadata": { "collapsed": false, "editable": true @@ -2970,7 +2972,7 @@ }, { "cell_type": "markdown", - "id": "372abcde", + "id": "6cbb4072", "metadata": { "editable": true }, @@ -2988,7 +2990,7 @@ }, { "cell_type": "markdown", - "id": "6b3fc4a5", + "id": "2702932a", "metadata": { "editable": true }, @@ -3023,7 +3025,7 @@ { "cell_type": "code", "execution_count": 33, - "id": "5f88d47b", + "id": "a66fc7b6", "metadata": { "collapsed": false, "editable": true @@ -3035,7 +3037,7 @@ }, { "cell_type": "markdown", - "id": "fe89b2bd", + "id": "3c99a292", "metadata": { "editable": true }, @@ -3047,7 +3049,7 @@ { "cell_type": "code", "execution_count": 34, - "id": "51009ec1", + "id": "71e9f24b", "metadata": { "collapsed": false, "editable": true @@ -3060,7 +3062,7 @@ }, { "cell_type": "markdown", - "id": "6b00853a", + "id": "a623588f", "metadata": { "editable": true }, @@ -3071,7 +3073,7 @@ { "cell_type": "code", "execution_count": 35, - "id": "27d84846", + "id": "fa19ee75", "metadata": { "collapsed": false, "editable": true @@ -3084,7 +3086,7 @@ }, { "cell_type": "markdown", - "id": "efb95ce2", + "id": "de5fec4a", "metadata": { "editable": true }, @@ -3099,7 +3101,7 @@ { "cell_type": "code", "execution_count": 36, - "id": "ff0d76a6", + "id": "edb61bdb", "metadata": { "collapsed": false, "editable": true @@ -3111,7 +3113,7 @@ }, { "cell_type": "markdown", - "id": "16ceb812", + "id": "fd72dabb", "metadata": { "editable": true }, @@ -3123,7 +3125,7 @@ }, { "cell_type": "markdown", - "id": "d1be0e7f", + "id": "b8262ab5", "metadata": { "editable": true }, @@ -3136,7 +3138,7 @@ { "cell_type": "code", "execution_count": 37, - "id": "3f300c00", + "id": "0b88258b", "metadata": { "collapsed": false, "editable": true @@ -3191,7 +3193,7 @@ { "cell_type": "code", "execution_count": 38, - "id": "fa0d45c3", + "id": "5000582f", "metadata": { "collapsed": false, "editable": true @@ -3220,7 +3222,7 @@ { "cell_type": "code", "execution_count": 39, - "id": "8cdc8534", + "id": "a473cac3", "metadata": { "collapsed": false, "editable": true @@ -3250,7 +3252,7 @@ { "cell_type": "code", "execution_count": 40, - "id": "2fbe350d", + "id": "041ff977", "metadata": { "collapsed": false, "editable": true @@ -3277,7 +3279,7 @@ { "cell_type": "code", "execution_count": 41, - "id": "4eae49aa", + "id": "1821d39d", "metadata": { "collapsed": false, "editable": true @@ -3319,7 +3321,7 @@ }, { "cell_type": "markdown", - "id": "43439e76", + "id": "c8b43dbc", "metadata": { "editable": true }, @@ -3330,7 +3332,7 @@ { "cell_type": "code", "execution_count": 42, - "id": "dabd1a04", + "id": "f902c476", "metadata": { "collapsed": false, "editable": true @@ -3507,7 +3509,7 @@ }, { "cell_type": "markdown", - "id": "23e4f61b", + "id": "fa673f3a", "metadata": { "editable": true }, @@ -3535,7 +3537,7 @@ }, { "cell_type": "markdown", - "id": "c9bfa99f", + "id": "6c4727a9", "metadata": { "editable": true }, @@ -3558,7 +3560,7 @@ }, { "cell_type": "markdown", - "id": "7c5e58d3", + "id": "f24d5df1", "metadata": { "editable": true }, @@ -3589,7 +3591,7 @@ }, { "cell_type": "markdown", - "id": "6b470b84", + "id": "80cd048d", "metadata": { "editable": true }, @@ -3621,7 +3623,7 @@ }, { "cell_type": "markdown", - "id": "370e81e1", + "id": "6863dde1", "metadata": { "editable": true }, @@ -3659,7 +3661,7 @@ }, { "cell_type": "markdown", - "id": "0bdbe74e", + "id": "95f03af7", "metadata": { "editable": true }, @@ -3684,7 +3686,7 @@ }, { "cell_type": "markdown", - "id": "cfa436f9", + "id": "620cac32", "metadata": { "editable": true }, @@ -3696,7 +3698,7 @@ }, { "cell_type": "markdown", - "id": "1b53aede", + "id": "2dd91f71", "metadata": { "editable": true }, @@ -3719,7 +3721,7 @@ }, { "cell_type": "markdown", - "id": "3d2e808b", + "id": "bfa3871c", "metadata": { "editable": true }, @@ -3739,7 +3741,7 @@ }, { "cell_type": "markdown", - "id": "19b958c3", + "id": "9315a3fc", "metadata": { "editable": true }, @@ -3761,7 +3763,7 @@ }, { "cell_type": "markdown", - "id": "1f4fd499", + "id": "85eb5069", "metadata": { "editable": true }, @@ -3779,7 +3781,7 @@ }, { "cell_type": "markdown", - "id": "99d0d4c8", + "id": "d2d08622", "metadata": { "editable": true }, @@ -3798,7 +3800,7 @@ }, { "cell_type": "markdown", - "id": "ee14cc3a", + "id": "566bcca7", "metadata": { "editable": true }, @@ -3810,7 +3812,7 @@ }, { "cell_type": "markdown", - "id": "3f5a367a", + "id": "aedf225e", "metadata": { "editable": true }, @@ -3855,7 +3857,7 @@ }, { "cell_type": "markdown", - "id": "687050b9", + "id": "8723e9ba", "metadata": { "editable": true }, @@ -3885,7 +3887,7 @@ }, { "cell_type": "markdown", - "id": "0ccb8f74", + "id": "b641b576", "metadata": { "editable": true }, @@ -3912,7 +3914,7 @@ }, { "cell_type": "markdown", - "id": "97d510ab", + "id": "e919d2c8", "metadata": { "editable": true }, @@ -3926,7 +3928,7 @@ }, { "cell_type": "markdown", - "id": "fe825851", + "id": "12b84451", "metadata": { "editable": true }, @@ -3943,7 +3945,7 @@ }, { "cell_type": "markdown", - "id": "780538d0", + "id": "88407501", "metadata": { "editable": true }, @@ -3959,7 +3961,7 @@ }, { "cell_type": "markdown", - "id": "f373c843", + "id": "034cef5e", "metadata": { "editable": true }, @@ -3971,7 +3973,7 @@ }, { "cell_type": "markdown", - "id": "72db62ef", + "id": "86693b23", "metadata": { "editable": true }, @@ -3989,7 +3991,7 @@ }, { "cell_type": "markdown", - "id": "9f423f32", + "id": "5c8e1418", "metadata": { "editable": true }, @@ -4010,7 +4012,7 @@ }, { "cell_type": "markdown", - "id": "54daf83b", + "id": "4ca42ad5", "metadata": { "editable": true }, @@ -4027,7 +4029,7 @@ }, { "cell_type": "markdown", - "id": "30a7ebea", + "id": "a0808029", "metadata": { "editable": true }, @@ -4039,7 +4041,7 @@ }, { "cell_type": "markdown", - "id": "a3c57c9f", + "id": "8e242ee9", "metadata": { "editable": true }, @@ -4050,7 +4052,7 @@ }, { "cell_type": "markdown", - "id": "f5f5d8ac", + "id": "6cb613c0", "metadata": { "editable": true }, @@ -4067,7 +4069,7 @@ }, { "cell_type": "markdown", - "id": "1ce283c1", + "id": "ff0ace15", "metadata": { "editable": true }, @@ -4078,7 +4080,7 @@ }, { "cell_type": "markdown", - "id": "cd9ff0af", + "id": "d77df59b", "metadata": { "editable": true }, @@ -4094,7 +4096,7 @@ }, { "cell_type": "markdown", - "id": "28c63a03", + "id": "b34fce14", "metadata": { "editable": true }, @@ -4106,7 +4108,7 @@ }, { "cell_type": "markdown", - "id": "4269d121", + "id": "290ed299", "metadata": { "editable": true }, @@ -4123,7 +4125,7 @@ }, { "cell_type": "markdown", - "id": "6b0e72c3", + "id": "d386881a", "metadata": { "editable": true }, @@ -4135,7 +4137,7 @@ }, { "cell_type": "markdown", - "id": "2295a2d8", + "id": "cf4aca15", "metadata": { "editable": true }, @@ -4153,7 +4155,7 @@ }, { "cell_type": "markdown", - "id": "e5e41afe", + "id": "ee5531c2", "metadata": { "editable": true }, @@ -4163,7 +4165,7 @@ }, { "cell_type": "markdown", - "id": "2150df23", + "id": "64a52bc0", "metadata": { "editable": true }, @@ -4175,7 +4177,7 @@ }, { "cell_type": "markdown", - "id": "0903b255", + "id": "e0154c0f", "metadata": { "editable": true }, @@ -4192,7 +4194,7 @@ }, { "cell_type": "markdown", - "id": "fe2da1fe", + "id": "334e7f10", "metadata": { "editable": true }, @@ -4204,7 +4206,7 @@ }, { "cell_type": "markdown", - "id": "75fea33a", + "id": "66c1d55c", "metadata": { "editable": true }, @@ -4215,7 +4217,7 @@ }, { "cell_type": "markdown", - "id": "297320fa", + "id": "f03c7338", "metadata": { "editable": true }, @@ -4227,7 +4229,7 @@ }, { "cell_type": "markdown", - "id": "7905db70", + "id": "ca114c87", "metadata": { "editable": true }, @@ -4237,7 +4239,7 @@ }, { "cell_type": "markdown", - "id": "7dbc7af5", + "id": "801219a0", "metadata": { "editable": true }, @@ -4257,7 +4259,7 @@ }, { "cell_type": "markdown", - "id": "52a2f0e5", + "id": "569486c4", "metadata": { "editable": true }, @@ -4274,7 +4276,7 @@ }, { "cell_type": "markdown", - "id": "fc2672cb", + "id": "575831d8", "metadata": { "editable": true }, @@ -4293,7 +4295,7 @@ }, { "cell_type": "markdown", - "id": "273c2869", + "id": "f8682123", "metadata": { "editable": true }, @@ -4305,7 +4307,7 @@ }, { "cell_type": "markdown", - "id": "8fae3b2f", + "id": "e62b1005", "metadata": { "editable": true }, @@ -4315,7 +4317,7 @@ }, { "cell_type": "markdown", - "id": "dc98cc83", + "id": "4e33439d", "metadata": { "editable": true }, @@ -4332,7 +4334,7 @@ }, { "cell_type": "markdown", - "id": "dbd04093", + "id": "35159fc5", "metadata": { "editable": true }, @@ -4342,7 +4344,7 @@ }, { "cell_type": "markdown", - "id": "4fed45e1", + "id": "03f74ead", "metadata": { "editable": true }, @@ -4358,7 +4360,7 @@ }, { "cell_type": "markdown", - "id": "5708fcb2", + "id": "3599c2de", "metadata": { "editable": true }, @@ -4370,7 +4372,7 @@ }, { "cell_type": "markdown", - "id": "7997d84c", + "id": "5c50baf7", "metadata": { "editable": true }, @@ -4382,7 +4384,7 @@ }, { "cell_type": "markdown", - "id": "78213356", + "id": "636cb74c", "metadata": { "editable": true }, @@ -4394,7 +4396,7 @@ }, { "cell_type": "markdown", - "id": "27349563", + "id": "b4b301c2", "metadata": { "editable": true }, @@ -4404,7 +4406,7 @@ }, { "cell_type": "markdown", - "id": "2b61bb07", + "id": "5d873576", "metadata": { "editable": true }, @@ -4416,7 +4418,7 @@ }, { "cell_type": "markdown", - "id": "5fe5b8e2", + "id": "b744fd57", "metadata": { "editable": true }, @@ -4433,7 +4435,7 @@ }, { "cell_type": "markdown", - "id": "2de1c60a", + "id": "63290336", "metadata": { "editable": true }, @@ -4443,7 +4445,7 @@ }, { "cell_type": "markdown", - "id": "b65d6687", + "id": "f8790d32", "metadata": { "editable": true }, @@ -4455,7 +4457,7 @@ }, { "cell_type": "markdown", - "id": "ca8a5a81", + "id": "10ab609a", "metadata": { "editable": true }, @@ -4469,7 +4471,7 @@ }, { "cell_type": "markdown", - "id": "b8614294", + "id": "06e2efca", "metadata": { "editable": true }, @@ -4485,7 +4487,7 @@ }, { "cell_type": "markdown", - "id": "76363167", + "id": "bc8a8e04", "metadata": { "editable": true }, @@ -4497,7 +4499,7 @@ }, { "cell_type": "markdown", - "id": "802de3e1", + "id": "6564e278", "metadata": { "editable": true }, @@ -4519,7 +4521,7 @@ }, { "cell_type": "markdown", - "id": "de92874a", + "id": "f62120f5", "metadata": { "editable": true }, @@ -4531,7 +4533,7 @@ }, { "cell_type": "markdown", - "id": "b5209d15", + "id": "4f875005", "metadata": { "editable": true }, @@ -4554,7 +4556,7 @@ }, { "cell_type": "markdown", - "id": "ad9f3dfb", + "id": "8748bf88", "metadata": { "editable": true }, @@ -4570,7 +4572,7 @@ }, { "cell_type": "markdown", - "id": "ecf5c2d9", + "id": "ce8b1154", "metadata": { "editable": true }, @@ -4582,7 +4584,7 @@ }, { "cell_type": "markdown", - "id": "525fe8e7", + "id": "f6ae91fa", "metadata": { "editable": true }, @@ -4606,7 +4608,7 @@ }, { "cell_type": "markdown", - "id": "6938f171", + "id": "f33f3a2e", "metadata": { "editable": true }, @@ -4618,7 +4620,7 @@ }, { "cell_type": "markdown", - "id": "592ce735", + "id": "d90fb703", "metadata": { "editable": true }, @@ -4639,7 +4641,7 @@ }, { "cell_type": "markdown", - "id": "4e757d4b", + "id": "0e4df2e9", "metadata": { "editable": true }, @@ -4651,7 +4653,7 @@ }, { "cell_type": "markdown", - "id": "b949460f", + "id": "85cc6666", "metadata": { "editable": true }, @@ -4670,7 +4672,7 @@ }, { "cell_type": "markdown", - "id": "7dbdf166", + "id": "c4876b89", "metadata": { "editable": true }, @@ -4680,7 +4682,7 @@ }, { "cell_type": "markdown", - "id": "64247852", + "id": "7e2cf00e", "metadata": { "editable": true }, @@ -4694,7 +4696,7 @@ }, { "cell_type": "markdown", - "id": "5b12d0ef", + "id": "a49b485d", "metadata": { "editable": true }, @@ -4706,7 +4708,7 @@ }, { "cell_type": "markdown", - "id": "22b5cd55", + "id": "7c5a4275", "metadata": { "editable": true }, @@ -4718,7 +4720,7 @@ }, { "cell_type": "markdown", - "id": "1adf129e", + "id": "ee92110b", "metadata": { "editable": true }, @@ -4735,7 +4737,7 @@ }, { "cell_type": "markdown", - "id": "380767da", + "id": "c76e461e", "metadata": { "editable": true }, @@ -4747,7 +4749,7 @@ }, { "cell_type": "markdown", - "id": "c8f1251c", + "id": "df9bc42a", "metadata": { "editable": true }, @@ -4769,7 +4771,7 @@ }, { "cell_type": "markdown", - "id": "3d864d42", + "id": "418dcc35", "metadata": { "editable": true }, @@ -4784,7 +4786,7 @@ }, { "cell_type": "markdown", - "id": "53e11ad4", + "id": "145ede1b", "metadata": { "editable": true }, @@ -4795,7 +4797,7 @@ { "cell_type": "code", "execution_count": 43, - "id": "87126ea3", + "id": "98b44c55", "metadata": { "collapsed": false, "editable": true @@ -4950,7 +4952,7 @@ }, { "cell_type": "markdown", - "id": "85cdbffb", + "id": "47df9d6f", "metadata": { "editable": true }, @@ -4965,7 +4967,7 @@ { "cell_type": "code", "execution_count": 44, - "id": "ea147bd8", + "id": "1415bdf5", "metadata": { "collapsed": false, "editable": true @@ -5134,7 +5136,7 @@ }, { "cell_type": "markdown", - "id": "a9c208db", + "id": "b480dee1", "metadata": { "editable": true }, @@ -5147,7 +5149,7 @@ }, { "cell_type": "markdown", - "id": "5c9d7463", + "id": "3736290c", "metadata": { "editable": true }, @@ -5164,7 +5166,7 @@ }, { "cell_type": "markdown", - "id": "959a791d", + "id": "9fe04f20", "metadata": { "editable": true }, @@ -5180,7 +5182,7 @@ }, { "cell_type": "markdown", - "id": "4d9192ef", + "id": "8090a04f", "metadata": { "editable": true }, @@ -5193,7 +5195,7 @@ }, { "cell_type": "markdown", - "id": "cd3254e9", + "id": "22a5d518", "metadata": { "editable": true }, @@ -5210,7 +5212,7 @@ }, { "cell_type": "markdown", - "id": "13dfafca", + "id": "24312795", "metadata": { "editable": true }, @@ -5222,7 +5224,7 @@ }, { "cell_type": "markdown", - "id": "98e4108f", + "id": "c90e2f79", "metadata": { "editable": true }, @@ -5249,7 +5251,7 @@ }, { "cell_type": "markdown", - "id": "6ef89c8b", + "id": "112c3155", "metadata": { "editable": true }, @@ -5262,7 +5264,7 @@ { "cell_type": "code", "execution_count": 45, - "id": "5cb44c16", + "id": "73371f5c", "metadata": { "collapsed": false, "editable": true @@ -5436,7 +5438,7 @@ }, { "cell_type": "markdown", - "id": "3357aac9", + "id": "9ce89121", "metadata": { "editable": true }, @@ -5456,7 +5458,7 @@ }, { "cell_type": "markdown", - "id": "8766414a", + "id": "dfe02266", "metadata": { "editable": true }, @@ -5471,7 +5473,7 @@ }, { "cell_type": "markdown", - "id": "e5ba08c5", + "id": "f2f12c48", "metadata": { "editable": true }, @@ -5485,7 +5487,7 @@ }, { "cell_type": "markdown", - "id": "0ab225a5", + "id": "ec46581e", "metadata": { "editable": true }, @@ -5501,7 +5503,7 @@ }, { "cell_type": "markdown", - "id": "5449a58e", + "id": "02c7198e", "metadata": { "editable": true }, @@ -5511,7 +5513,7 @@ }, { "cell_type": "markdown", - "id": "a8b2c62b", + "id": "90c2533d", "metadata": { "editable": true }, @@ -5533,7 +5535,7 @@ }, { "cell_type": "markdown", - "id": "e96c80e9", + "id": "f3dddcdf", "metadata": { "editable": true }, @@ -5547,7 +5549,7 @@ { "cell_type": "code", "execution_count": 46, - "id": "72270271", + "id": "52ee1308", "metadata": { "collapsed": false, "editable": true @@ -5623,7 +5625,7 @@ }, { "cell_type": "markdown", - "id": "e91781d3", + "id": "6537fc0c", "metadata": { "editable": true }, @@ -5635,7 +5637,7 @@ }, { "cell_type": "markdown", - "id": "f004baeb", + "id": "bf0ace9e", "metadata": { "editable": true }, @@ -5652,7 +5654,7 @@ }, { "cell_type": "markdown", - "id": "4005279b", + "id": "212fac99", "metadata": { "editable": true }, @@ -5664,7 +5666,7 @@ }, { "cell_type": "markdown", - "id": "41750a28", + "id": "fa2547d7", "metadata": { "editable": true }, @@ -5679,7 +5681,7 @@ }, { "cell_type": "markdown", - "id": "7ac869b8", + "id": "4d3aa4c3", "metadata": { "editable": true }, @@ -5691,7 +5693,7 @@ }, { "cell_type": "markdown", - "id": "3f838e66", + "id": "872d2747", "metadata": { "editable": true }, @@ -5703,7 +5705,7 @@ }, { "cell_type": "markdown", - "id": "981f744e", + "id": "b51b983e", "metadata": { "editable": true }, @@ -5715,7 +5717,7 @@ }, { "cell_type": "markdown", - "id": "73504342", + "id": "f208cc09", "metadata": { "editable": true }, @@ -5725,7 +5727,7 @@ }, { "cell_type": "markdown", - "id": "65918eb3", + "id": "d584ffd3", "metadata": { "editable": true }, @@ -5742,7 +5744,7 @@ }, { "cell_type": "markdown", - "id": "e1f3e150", + "id": "99794ce5", "metadata": { "editable": true }, @@ -5754,7 +5756,7 @@ }, { "cell_type": "markdown", - "id": "b4d54816", + "id": "3f2f40d9", "metadata": { "editable": true }, @@ -5766,7 +5768,7 @@ }, { "cell_type": "markdown", - "id": "761ba010", + "id": "d5590dfb", "metadata": { "editable": true }, @@ -5776,7 +5778,7 @@ }, { "cell_type": "markdown", - "id": "de0a4270", + "id": "75ab0f4f", "metadata": { "editable": true }, @@ -5788,7 +5790,7 @@ }, { "cell_type": "markdown", - "id": "b3df6b28", + "id": "d83e4c37", "metadata": { "editable": true }, @@ -5799,7 +5801,7 @@ { "cell_type": "code", "execution_count": 47, - "id": "0ea196b5", + "id": "95731560", "metadata": { "collapsed": false, "editable": true @@ -5960,7 +5962,7 @@ }, { "cell_type": "markdown", - "id": "9229281a", + "id": "559cb774", "metadata": { "editable": true }, @@ -5982,7 +5984,7 @@ }, { "cell_type": "markdown", - "id": "c07a6324", + "id": "72b9c88d", "metadata": { "editable": true }, @@ -5999,7 +6001,7 @@ }, { "cell_type": "markdown", - "id": "28c6e156", + "id": "1c7a99a5", "metadata": { "editable": true }, @@ -6009,7 +6011,7 @@ }, { "cell_type": "markdown", - "id": "c8cb001c", + "id": "b46248f9", "metadata": { "editable": true }, @@ -6024,7 +6026,7 @@ }, { "cell_type": "markdown", - "id": "2ea0c3eb", + "id": "fa1d6871", "metadata": { "editable": true }, @@ -6034,7 +6036,7 @@ }, { "cell_type": "markdown", - "id": "91bd65f6", + "id": "caa3216a", "metadata": { "editable": true }, @@ -6049,7 +6051,7 @@ }, { "cell_type": "markdown", - "id": "8196883f", + "id": "936f3b89", "metadata": { "editable": true }, @@ -6060,7 +6062,7 @@ }, { "cell_type": "markdown", - "id": "a3dbac1d", + "id": "1e7fa7e3", "metadata": { "editable": true }, @@ -6080,7 +6082,7 @@ }, { "cell_type": "markdown", - "id": "fa4b53ba", + "id": "9c46ad0b", "metadata": { "editable": true }, @@ -6092,7 +6094,7 @@ }, { "cell_type": "markdown", - "id": "ae62439e", + "id": "ccf16979", "metadata": { "editable": true }, @@ -6129,7 +6131,7 @@ }, { "cell_type": "markdown", - "id": "3ab45a2b", + "id": "e940bd60", "metadata": { "editable": true }, @@ -6139,7 +6141,7 @@ }, { "cell_type": "markdown", - "id": "64114f21", + "id": "ee51ae7f", "metadata": { "editable": true }, @@ -6152,7 +6154,7 @@ { "cell_type": "code", "execution_count": 48, - "id": "16f3ae01", + "id": "c538fed6", "metadata": { "collapsed": false, "editable": true @@ -6353,7 +6355,7 @@ }, { "cell_type": "markdown", - "id": "496273dc", + "id": "9855543b", "metadata": { "editable": true }, @@ -6370,7 +6372,7 @@ }, { "cell_type": "markdown", - "id": "c5fafc34", + "id": "a1b8cd5d", "metadata": { "editable": true }, @@ -6387,7 +6389,7 @@ }, { "cell_type": "markdown", - "id": "9da50613", + "id": "75d6ae90", "metadata": { "editable": true }, @@ -6397,7 +6399,7 @@ }, { "cell_type": "markdown", - "id": "7d878f57", + "id": "83d6d123", "metadata": { "editable": true }, @@ -6412,7 +6414,7 @@ }, { "cell_type": "markdown", - "id": "f733da79", + "id": "7d9f4892", "metadata": { "editable": true }, @@ -6426,7 +6428,7 @@ }, { "cell_type": "markdown", - "id": "75200533", + "id": "bc1c8c1b", "metadata": { "editable": true }, @@ -6439,7 +6441,7 @@ }, { "cell_type": "markdown", - "id": "9eca0d3c", + "id": "6fdbc09b", "metadata": { "editable": true }, @@ -6459,7 +6461,7 @@ }, { "cell_type": "markdown", - "id": "6763a3ba", + "id": "15f911ae", "metadata": { "editable": true }, @@ -6471,7 +6473,7 @@ }, { "cell_type": "markdown", - "id": "7ad92188", + "id": "73d815fc", "metadata": { "editable": true }, @@ -6483,7 +6485,7 @@ }, { "cell_type": "markdown", - "id": "74a848a9", + "id": "a8b12b31", "metadata": { "editable": true }, @@ -6495,7 +6497,7 @@ }, { "cell_type": "markdown", - "id": "604cb0d2", + "id": "0bca5395", "metadata": { "editable": true }, @@ -6505,7 +6507,7 @@ }, { "cell_type": "markdown", - "id": "a375f71b", + "id": "189bac4d", "metadata": { "editable": true }, @@ -6517,7 +6519,7 @@ }, { "cell_type": "markdown", - "id": "db03df01", + "id": "9b596e36", "metadata": { "editable": true }, @@ -6529,7 +6531,7 @@ }, { "cell_type": "markdown", - "id": "ad1e5bb9", + "id": "3dc31dd6", "metadata": { "editable": true }, @@ -6541,7 +6543,7 @@ }, { "cell_type": "markdown", - "id": "3e5c1a4b", + "id": "263f026e", "metadata": { "editable": true }, @@ -6551,7 +6553,7 @@ }, { "cell_type": "markdown", - "id": "907a6d4c", + "id": "717dae65", "metadata": { "editable": true }, @@ -6567,7 +6569,7 @@ }, { "cell_type": "markdown", - "id": "20d93513", + "id": "0f898a32", "metadata": { "editable": true }, @@ -6577,7 +6579,7 @@ }, { "cell_type": "markdown", - "id": "c7780ae9", + "id": "47634dd9", "metadata": { "editable": true }, @@ -6589,7 +6591,7 @@ }, { "cell_type": "markdown", - "id": "ed12bd6d", + "id": "6af0489e", "metadata": { "editable": true }, @@ -6606,7 +6608,7 @@ }, { "cell_type": "markdown", - "id": "5be52dcf", + "id": "03dabab2", "metadata": { "editable": true }, @@ -6616,7 +6618,7 @@ }, { "cell_type": "markdown", - "id": "f759b8d1", + "id": "796edf7a", "metadata": { "editable": true }, @@ -6632,7 +6634,7 @@ }, { "cell_type": "markdown", - "id": "014834ff", + "id": "78d4195f", "metadata": { "editable": true }, @@ -6646,7 +6648,7 @@ }, { "cell_type": "markdown", - "id": "7bbb7493", + "id": "13f63286", "metadata": { "editable": true }, @@ -6665,7 +6667,7 @@ { "cell_type": "code", "execution_count": 49, - "id": "4019a7dd", + "id": "e05767f4", "metadata": { "collapsed": false, "editable": true @@ -6720,7 +6722,7 @@ }, { "cell_type": "markdown", - "id": "d5cd516b", + "id": "d4ebc584", "metadata": { "editable": true }, @@ -6750,7 +6752,7 @@ }, { "cell_type": "markdown", - "id": "8affc5a2", + "id": "124b550e", "metadata": { "editable": true }, @@ -6779,7 +6781,7 @@ { "cell_type": "code", "execution_count": 50, - "id": "6803b65e", + "id": "53c4c879", "metadata": { "collapsed": false, "editable": true @@ -6826,7 +6828,7 @@ }, { "cell_type": "markdown", - "id": "59db5670", + "id": "b11a26b8", "metadata": { "editable": true }, @@ -6852,7 +6854,7 @@ { "cell_type": "code", "execution_count": 51, - "id": "eec1a5f5", + "id": "fa2d8508", "metadata": { "collapsed": false, "editable": true @@ -7086,7 +7088,7 @@ }, { "cell_type": "markdown", - "id": "256d8ad9", + "id": "4dfabc5e", "metadata": { "editable": true }, @@ -7098,7 +7100,7 @@ }, { "cell_type": "markdown", - "id": "4772948f", + "id": "8f6f58e6", "metadata": { "editable": true }, @@ -7110,7 +7112,7 @@ }, { "cell_type": "markdown", - "id": "323bc655", + "id": "8a4abe82", "metadata": { "editable": true }, @@ -7122,7 +7124,7 @@ }, { "cell_type": "markdown", - "id": "fbe75888", + "id": "57c34190", "metadata": { "editable": true }, @@ -7139,7 +7141,7 @@ }, { "cell_type": "markdown", - "id": "29fc55b0", + "id": "8c5b8137", "metadata": { "editable": true }, @@ -7149,7 +7151,7 @@ }, { "cell_type": "markdown", - "id": "5f9c311c", + "id": "08cb8e62", "metadata": { "editable": true }, @@ -7161,7 +7163,7 @@ }, { "cell_type": "markdown", - "id": "583599b9", + "id": "f7b1bf8c", "metadata": { "editable": true }, @@ -7178,7 +7180,7 @@ }, { "cell_type": "markdown", - "id": "3fe68796", + "id": "1127e6e8", "metadata": { "editable": true }, @@ -7189,7 +7191,7 @@ }, { "cell_type": "markdown", - "id": "858bcccf", + "id": "7a099129", "metadata": { "editable": true }, @@ -7209,7 +7211,7 @@ }, { "cell_type": "markdown", - "id": "9a46e1cb", + "id": "772c7eec", "metadata": { "editable": true }, @@ -7219,7 +7221,7 @@ }, { "cell_type": "markdown", - "id": "f0e68c83", + "id": "be8649c8", "metadata": { "editable": true }, @@ -7245,7 +7247,7 @@ }, { "cell_type": "markdown", - "id": "c80f96fe", + "id": "27484fe1", "metadata": { "editable": true }, @@ -7261,7 +7263,7 @@ }, { "cell_type": "markdown", - "id": "a82fb339", + "id": "16f8dd35", "metadata": { "editable": true }, @@ -7272,7 +7274,7 @@ { "cell_type": "code", "execution_count": 52, - "id": "744f5688", + "id": "51011a0f", "metadata": { "collapsed": false, "editable": true @@ -7503,7 +7505,7 @@ }, { "cell_type": "markdown", - "id": "b1e76cca", + "id": "fff9e254", "metadata": { "editable": true },