From 2013318a7d67b1e325b8fdca11019d9017a2a564 Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Thu, 20 Oct 2022 09:04:30 +0200 Subject: [PATCH] update --- doc/pub/week41/ipynb/week41.ipynb | 3598 +++++++++++++++++++++++++---- doc/pub/week42/ipynb/week42.ipynb | 1289 ++++------- doc/src/week41/programs/testtf.py | 49 + 3 files changed, 3717 insertions(+), 1219 deletions(-) create mode 100644 doc/src/week41/programs/testtf.py diff --git a/doc/pub/week41/ipynb/week41.ipynb b/doc/pub/week41/ipynb/week41.ipynb index 4d45f09ac..c276098a3 100644 --- a/doc/pub/week41/ipynb/week41.ipynb +++ b/doc/pub/week41/ipynb/week41.ipynb @@ -3,9 +3,7 @@ { "cell_type": "markdown", "id": "ad99bc77", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", @@ -15,9 +13,7 @@ { "cell_type": "markdown", "id": "b600d246", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "# Week 41 Constructing a Neural Network code, Tensor flow and start Convolutional Neural Networks\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", @@ -30,9 +26,7 @@ { "cell_type": "markdown", "id": "8637f719", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Plan for week 41\n", "\n", @@ -52,9 +46,7 @@ { "cell_type": "markdown", "id": "1eb37715", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Videos on Neural Networks\n", "\n", @@ -68,9 +60,7 @@ { "cell_type": "markdown", "id": "57d9f9f4", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Review of the back propagation algorithm\n", "\n", @@ -83,9 +73,7 @@ { "cell_type": "markdown", "id": "928c65c1", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Setting up the Back propagation algorithm\n", "\n", @@ -106,9 +94,7 @@ { "cell_type": "markdown", "id": "c820227e", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\delta_j^L = f'(z_j^L)\\frac{\\partial {\\cal C}}{\\partial (a_j^L)}.\n", @@ -118,9 +104,7 @@ { "cell_type": "markdown", "id": "94267523", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "Then we compute the back propagate error for each $l=L-1,L-2,\\dots,2$ as" ] @@ -128,9 +112,7 @@ { "cell_type": "markdown", "id": "0d835709", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\delta_j^l = \\sum_k \\delta_k^{l+1}w_{kj}^{l+1}f'(z_j^l).\n", @@ -140,9 +122,7 @@ { "cell_type": "markdown", "id": "28995178", - "metadata": { - "editable": true - }, + "metadata": {}, "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" ] @@ -150,9 +130,7 @@ { "cell_type": "markdown", "id": "f8e000ce", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "w_{jk}^l\\leftarrow = w_{jk}^l- \\eta \\delta_j^la_k^{l-1},\n", @@ -162,9 +140,7 @@ { "cell_type": "markdown", "id": "f4bbf4d4", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\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", @@ -174,9 +150,7 @@ { "cell_type": "markdown", "id": "f4b9ddfa", - "metadata": { - "editable": true - }, + "metadata": {}, "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." @@ -185,9 +159,7 @@ { "cell_type": "markdown", "id": "9d9e1324", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Setting up a Multi-layer perceptron model for classification\n", "\n", @@ -213,9 +185,7 @@ { "cell_type": "markdown", "id": "986a1be8", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "P(y = 0 \\mid \\boldsymbol{x}, \\boldsymbol{\\theta}) = \\frac{1}{1 + \\exp{(- \\boldsymbol{x}})} ,\n", @@ -225,9 +195,7 @@ { "cell_type": "markdown", "id": "9eb4dacf", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "and" ] @@ -235,9 +203,7 @@ { "cell_type": "markdown", "id": "8277b759", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "P(y = 1 \\mid \\boldsymbol{x}, \\boldsymbol{\\theta}) = 1 - P(y = 0 \\mid \\boldsymbol{x}, \\boldsymbol{\\theta}) ,\n", @@ -247,9 +213,7 @@ { "cell_type": "markdown", "id": "ce81bbac", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where $y \\in \\{0, 1\\}$ and $\\boldsymbol{\\theta}$ represents the weights and biases\n", "of our network." @@ -258,9 +222,7 @@ { "cell_type": "markdown", "id": "dae87872", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Defining the cost function\n", "\n", @@ -270,9 +232,7 @@ { "cell_type": "markdown", "id": "305aa69e", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\mathcal{C}(\\boldsymbol{\\theta}) = - \\ln P(\\mathcal{D} \\mid \\boldsymbol{\\theta}) = - \\sum_{i=1}^n\n", @@ -283,9 +243,7 @@ { "cell_type": "markdown", "id": "0b9f595b", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "This last equality means that we can interpret our *cost* function as a sum over the *loss* function\n", "for each point in the dataset $\\mathcal{L}_i(\\boldsymbol{\\theta})$. \n", @@ -308,9 +266,7 @@ { "cell_type": "markdown", "id": "df7a99c3", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "P(y_{ic} = 1 \\mid \\boldsymbol{x}_i, \\boldsymbol{\\theta}) = \\frac{\\exp{((\\boldsymbol{a}_i^{hidden})^T \\boldsymbol{w}_c)}}\n", @@ -321,9 +277,7 @@ { "cell_type": "markdown", "id": "990ad682", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "which reduces to the logistic function in the binary case. \n", "The likelihood of this $C$-class classifier\n", @@ -333,9 +287,7 @@ { "cell_type": "markdown", "id": "9f46e3f7", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "P(\\mathcal{D} \\mid \\boldsymbol{\\theta}) = \\prod_{i=1}^n \\prod_{c=0}^{C-1} [P(y_{ic} = 1)]^{y_{ic}} .\n", @@ -345,9 +297,7 @@ { "cell_type": "markdown", "id": "130feb29", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "Again we take the negative log-likelihood to define our cost function:" ] @@ -355,9 +305,7 @@ { "cell_type": "markdown", "id": "c5b8dd70", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\mathcal{C}(\\boldsymbol{\\theta}) = - \\log{P(\\mathcal{D} \\mid \\boldsymbol{\\theta})}.\n", @@ -367,9 +315,7 @@ { "cell_type": "markdown", "id": "9692ca73", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "See the logistic regression lectures for a full definition of the cost function.\n", "\n", @@ -379,9 +325,7 @@ { "cell_type": "markdown", "id": "32190b69", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Example: binary classification problem\n", "\n", @@ -391,9 +335,7 @@ { "cell_type": "markdown", "id": "0355eaed", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\mathcal{C}(\\boldsymbol{\\beta}) = - \\sum_{i=1}^n \\left(y_i\\log{p(y_i \\vert x_i,\\boldsymbol{\\beta})}+(1-y_i)\\log{1-p(y_i \\vert x_i,\\boldsymbol{\\beta})}\\right),\n", @@ -403,9 +345,7 @@ { "cell_type": "markdown", "id": "a10d8c23", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where we had defined the logistic (sigmoid) function" ] @@ -413,9 +353,7 @@ { "cell_type": "markdown", "id": "48469a18", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\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", @@ -425,9 +363,7 @@ { "cell_type": "markdown", "id": "dd40a298", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "and" ] @@ -435,9 +371,7 @@ { "cell_type": "markdown", "id": "e80ef21d", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "p(y_i =0\\vert x_i,\\boldsymbol{\\beta})=1-p(y_i =1\\vert x_i,\\boldsymbol{\\beta}).\n", @@ -447,9 +381,7 @@ { "cell_type": "markdown", "id": "90e9d3d9", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "The parameters $\\boldsymbol{\\beta}$ were defined using a minimization method like gradient descent or Newton-Raphson's method. \n", "\n", @@ -460,9 +392,7 @@ { "cell_type": "markdown", "id": "56ecaeeb", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "a_i^l = y_i = \\frac{\\exp{(z_i^l)}}{1+\\exp{(z_i^l)}},\n", @@ -472,9 +402,7 @@ { "cell_type": "markdown", "id": "2a8bd9d7", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "with" ] @@ -482,9 +410,7 @@ { "cell_type": "markdown", "id": "4a7b30e6", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "z_i^l = \\sum_{j}w_{ij}^l a_j^{l-1}+b_i^l,\n", @@ -494,9 +420,7 @@ { "cell_type": "markdown", "id": "1fbb407d", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where the superscript $l-1$ indicates that these are the outputs from layer $l-1$.\n", "Our cost function at the final layer $l=L$ is now" @@ -505,9 +429,7 @@ { "cell_type": "markdown", "id": "85e01cbc", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\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", @@ -517,9 +439,7 @@ { "cell_type": "markdown", "id": "0ced7306", - "metadata": { - "editable": true - }, + "metadata": {}, "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" ] @@ -527,9 +447,7 @@ { "cell_type": "markdown", "id": "a59f5fa2", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\frac{\\partial \\mathcal{C}(\\boldsymbol{W})}{\\partial a_i^L} = \\frac{a_i^L-t_i}{a_i^L(1-a_i^L)}.\n", @@ -539,9 +457,7 @@ { "cell_type": "markdown", "id": "01925119", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "In case we use another activation function than the logistic one, we need to evaluate other derivatives." ] @@ -549,9 +465,7 @@ { "cell_type": "markdown", "id": "2e5de297", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The Softmax function\n", "In case we employ the more general case given by the Softmax equation, we need to evaluate the derivative of the activation function with respect to the activation $z_i^l$, that is we need" @@ -560,9 +474,7 @@ { "cell_type": "markdown", "id": "aebd954a", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\frac{\\partial f(z_i^l)}{\\partial w_{jk}^l} =\n", @@ -573,9 +485,7 @@ { "cell_type": "markdown", "id": "169213b3", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "For the Softmax function we have" ] @@ -583,9 +493,7 @@ { "cell_type": "markdown", "id": "214da92a", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "f(z_i^l) = \\frac{\\exp{(z_i^l)}}{\\sum_{m=1}^K\\exp{(z_m^l)}}.\n", @@ -595,9 +503,7 @@ { "cell_type": "markdown", "id": "c7b95b12", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "Its derivative with respect to $z_j^l$ gives" ] @@ -605,9 +511,7 @@ { "cell_type": "markdown", "id": "343e174c", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\frac{\\partial f(z_i^l)}{\\partial z_j^l}= f(z_i^l)\\left(\\delta_{ij}-f(z_j^l)\\right),\n", @@ -617,9 +521,7 @@ { "cell_type": "markdown", "id": "ca1a02a0", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "which in case of the simply binary model reduces to having $i=j$." ] @@ -627,9 +529,7 @@ { "cell_type": "markdown", "id": "975ee3db", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Developing a code for doing neural networks with back propagation\n", "\n", @@ -651,9 +551,7 @@ { "cell_type": "markdown", "id": "d04c9ad0", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Collect and pre-process data\n", "\n", @@ -701,10 +599,7 @@ "cell_type": "code", "execution_count": 1, "id": "9b1e2335", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", @@ -756,9 +651,7 @@ { "cell_type": "markdown", "id": "9bb279fa", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Train and test datasets\n", "\n", @@ -777,10 +670,7 @@ "cell_type": "code", "execution_count": 2, "id": "63b5f925", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", @@ -815,9 +705,7 @@ { "cell_type": "markdown", "id": "3f10e308", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Define model and architecture\n", "\n", @@ -859,9 +747,7 @@ { "cell_type": "markdown", "id": "dc52eae6", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Layers\n", "\n", @@ -899,9 +785,7 @@ { "cell_type": "markdown", "id": "0a8b89e7", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Weights and biases\n", "\n", @@ -920,10 +804,7 @@ "cell_type": "code", "execution_count": 3, "id": "21742490", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# building our neural network\n", @@ -946,9 +827,7 @@ { "cell_type": "markdown", "id": "4c5cca55", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Feed-forward pass\n", "\n", @@ -974,9 +853,7 @@ { "cell_type": "markdown", "id": "4d6e5e7c", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Matrix multiplications\n", "\n", @@ -1011,10 +888,7 @@ "cell_type": "code", "execution_count": 4, "id": "655edfa8", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# setup the feed-forward pass, subscript h = hidden layer\n", @@ -1057,9 +931,7 @@ { "cell_type": "markdown", "id": "6587577e", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Choose cost function and optimizer\n", "\n", @@ -1088,9 +960,7 @@ { "cell_type": "markdown", "id": "4179cedb", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Optimizing the cost function\n", "\n", @@ -1126,9 +996,7 @@ { "cell_type": "markdown", "id": "6eea00ee", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Regularization\n", "\n", @@ -1160,9 +1028,7 @@ { "cell_type": "markdown", "id": "0b5658f7", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Matrix multiplication\n", "\n", @@ -1201,10 +1067,7 @@ "cell_type": "code", "execution_count": 5, "id": "70a11914", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# to categorical turns our integer vector into a onehot representation\n", @@ -1280,9 +1143,7 @@ { "cell_type": "markdown", "id": "b8fe45e7", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Improving performance\n", "\n", @@ -1301,9 +1162,7 @@ { "cell_type": "markdown", "id": "97fbfe63", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Full object-oriented implementation\n", "\n", @@ -1315,10 +1174,7 @@ "cell_type": "code", "execution_count": 6, "id": "9f565f04", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "class NeuralNetwork:\n", @@ -1425,9 +1281,7 @@ { "cell_type": "markdown", "id": "d7c1c598", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Evaluate model performance on test data\n", "\n", @@ -1444,10 +1298,7 @@ "cell_type": "code", "execution_count": 7, "id": "e864f3aa", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "epochs = 100\n", @@ -1471,9 +1322,7 @@ { "cell_type": "markdown", "id": "1f175e5a", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Adjust hyperparameters\n", "\n", @@ -1485,10 +1334,7 @@ "cell_type": "code", "execution_count": 8, "id": "a7eeea76", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "eta_vals = np.logspace(-5, 1, 7)\n", @@ -1516,9 +1362,7 @@ { "cell_type": "markdown", "id": "1ec1196b", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Visualization" ] @@ -1527,10 +1371,7 @@ "cell_type": "code", "execution_count": 9, "id": "b53e874c", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# visual representation of grid search\n", @@ -1571,9 +1412,7 @@ { "cell_type": "markdown", "id": "b11c6a31", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## scikit-learn implementation\n", "\n", @@ -1594,10 +1433,7 @@ "cell_type": "code", "execution_count": 10, "id": "48478b6c", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.neural_network import MLPClassifier\n", @@ -1621,9 +1457,7 @@ { "cell_type": "markdown", "id": "3748070b", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Visualization" ] @@ -1632,10 +1466,7 @@ "cell_type": "code", "execution_count": 11, "id": "0a425870", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# optional\n", @@ -1677,9 +1508,7 @@ { "cell_type": "markdown", "id": "6304a99e", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Testing our code for the XOR, OR and AND gates\n", "\n", @@ -1704,9 +1533,7 @@ { "cell_type": "markdown", "id": "75c6f9a7", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The AND and XOR Gates\n", "\n", @@ -1742,9 +1569,7 @@ { "cell_type": "markdown", "id": "8681d7c3", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Representing the Data Sets\n", "\n", @@ -1754,9 +1579,7 @@ { "cell_type": "markdown", "id": "becf04ff", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\boldsymbol{X}=\\begin{bmatrix} 0 & 0 \\\\\n", @@ -1769,9 +1592,7 @@ { "cell_type": "markdown", "id": "9d8c9533", - "metadata": { - "editable": true - }, + "metadata": {}, "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." ] @@ -1779,9 +1600,7 @@ { "cell_type": "markdown", "id": "c9e5cbe2", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Setting up the Neural Network\n", "\n", @@ -1792,10 +1611,7 @@ "cell_type": "code", "execution_count": 12, "id": "e21b6b05", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "\"\"\"\n", @@ -1868,9 +1684,7 @@ { "cell_type": "markdown", "id": "8e4e97ce", - "metadata": { - "editable": true - }, + "metadata": {}, "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." ] @@ -1878,9 +1692,7 @@ { "cell_type": "markdown", "id": "c7694996", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The Code using Scikit-Learn" ] @@ -1889,10 +1701,7 @@ "cell_type": "code", "execution_count": 13, "id": "019432d0", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# import necessary packages\n", @@ -1957,9 +1766,7 @@ { "cell_type": "markdown", "id": "afb2ee6d", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Building neural networks in Tensorflow and Keras\n", "\n", @@ -1975,9 +1782,7 @@ { "cell_type": "markdown", "id": "68f0ed4c", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Tensorflow\n", "\n", @@ -2010,10 +1815,7 @@ "cell_type": "code", "execution_count": 14, "id": "8c4f086d", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "pip3 install tensorflow" @@ -2022,9 +1824,7 @@ { "cell_type": "markdown", "id": "13c0215c", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "and/or if you use **anaconda**, just write (or install from the graphical user interface)\n", "(current release of CPU-only TensorFlow)" @@ -2034,10 +1834,7 @@ "cell_type": "code", "execution_count": 15, "id": "ad3eabbd", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "conda create -n tf tensorflow\n", @@ -2047,9 +1844,7 @@ { "cell_type": "markdown", "id": "2996f448", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "To install the current release of GPU TensorFlow" ] @@ -2058,10 +1853,7 @@ "cell_type": "code", "execution_count": 16, "id": "be33424d", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "conda create -n tf-gpu tensorflow-gpu\n", @@ -2071,9 +1863,7 @@ { "cell_type": "markdown", "id": "b8e9fe7e", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Using Keras\n", "\n", @@ -2086,10 +1876,7 @@ "cell_type": "code", "execution_count": 17, "id": "16f70fd0", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "conda install keras" @@ -2098,9 +1885,7 @@ { "cell_type": "markdown", "id": "f92ed6ad", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "You can look up the [instructions here](https://keras.io/) for more information.\n", "\n", @@ -2110,9 +1895,7 @@ { "cell_type": "markdown", "id": "c9072373", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Collect and pre-process data\n", "\n", @@ -2121,13 +1904,30 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 1, "id": "62854fe2", - "metadata": { - "collapsed": false, - "editable": true - }, - "outputs": [], + "metadata": {}, + "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": "iVBORw0KGgoAAAANSUhEUgAAA7YAAADICAYAAADcOn20AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAQx0lEQVR4nO3dbWydZRkH8OuMjklYaEHMxla3ji46/KBdiItodB2oEUVXDNMYois6QgLCkKkhEGwHGDCaWIxODY510SUuJmSdZr4AdjUmxPBWEhaNjlBEzSYOOl+SbQwfPxhqN9Y68G7rdc7vl/QDZz3/c5+T6z7P899zOKtVVVUFAAAAJDVrphcAAAAA/wvFFgAAgNQUWwAAAFJTbAEAAEhNsQUAACA1xRYAAIDUFFsAAABSU2wBAABITbEFAAAgtYYptv39/VGr1eLhhx8ukler1eLTn/50kazxmb29va/6/nv37o2Pf/zjsWjRojjttNOivb09brjhhjhw4EC5RZJSvc9/b29v1Gq1CX++//3vF10r+dgD9kAjq/f5f+aZZ+LSSy+Nc889N04//fRobm6O5cuXx9e//vU4evRo0XWSj/lvHE0zvQDKePbZZ+Ntb3tbnHHGGXHbbbfFokWL4rHHHouenp4YHByMRx55JGbNapi/x6DBrFu3Lt73vve97PYrr7wynnzyyRP+GdQTe4BG9o9//CPOOOOMuOWWW2LRokVx5MiR2LVrV1x77bUxPDwc3/nOd2Z6iTBlzP9/KLZ1YmBgIA4cOBDbt2+Piy66KCIiVq1aFYcPH46bbropHn/88Vi+fPkMrxKmRmtra7S2th5z28jISOzZsycuv/zyaGlpmZmFwTSxB2hky5Yti61btx5z28UXXxx//vOfY+vWrfGNb3wj5syZM0Org6ll/v/DJbxxDh06FBs2bIiOjo5obm6Os846Ky644IIYGBiY8D7f/va34w1veEPMmTMn3vSmN53w41779u2Lq666KlpbW+PUU0+NJUuWxMaNG4t+PGD27NkREdHc3HzM7S+dzLzmNa8p9ljUp8zzfyL33HNPVFUV69atm9LHoX7YAzSyepv/iIjXve51MWvWrDjllFOm/LHIzfzXB1dsxzl8+HA899xz8dnPfjYWLlwYR44cifvvvz8+/OEPx5YtW+ITn/jEMb+/c+fOGBwcjFtvvTVOP/302LRpU3zsYx+LpqamuOyyyyLi3wO9YsWKmDVrVnzhC1+I9vb2ePDBB+P222+PkZGR2LJly6Rramtri4h//837ZLq6umLRokWxYcOG2LRpUyxevDgeffTRuPPOO+ODH/xgnHfeea/6daExZJ7/4/3zn/+M/v7+WLp0aaxcufIV3ZfGZQ/QyOph/quqihdffDH+9re/xc9+9rPo7++PDRs2RFOT010mZ/7rRNUgtmzZUkVE9dBDD530fY4ePVq98MIL1ac+9alq+fLlx/xZRFSnnXZatW/fvmN+f9myZdXSpUvHbrvqqququXPnVk8//fQx9//KV75SRUS1Z8+eYzJ7enqO+b329vaqvb39pNb7pz/9qbrggguqiBj7WbNmTXXo0KGTfcrUqUaY//F+/OMfVxFR3XHHHa/4vtQne4BG1ijzf8cdd4yd/9Rqtermm28+6ftSv8x/4/BR5OP84Ac/iHe84x0xd+7caGpqitmzZ8fmzZvj17/+9ct+96KLLop58+aN/fcpp5wSH/3oR2Pv3r3xhz/8ISIifvSjH8WqVatiwYIFcfTo0bGfiy++OCIihoaGJl3P3r17Y+/evf913c8//3ysXr06/vrXv8a2bdviF7/4RWzatCl++ctfxoc+9KGG+1Y0Xp2s83+8zZs3R1NTU3R3d7/i+9LY7AEaWfb57+7ujoceeih++tOfxuc///n48pe/HNdee+1J35/GZv7za6Br0//dvffeGx/5yEdizZo18bnPfS7mz58fTU1N8c1vfjPuueeel/3+/PnzJ7ztwIED0draGvv3748f/vCHY/8P7PH+8pe/FFn7l770pRgeHo6nn346zjnnnIiIeOc73xnLli2LCy+8MLZt2xZr164t8ljUp8zzf3zmzp074wMf+MAJ1wgTsQdoZPUw//Pnzx9bw3vf+94488wz48Ybb4xPfvKTvkCTSZn/+qDYjvO9730vlixZEtu3b49arTZ2++HDh0/4+/v27Zvwtte+9rUREXH22WfHm9/85vjiF794wowFCxb8r8uOiIjh4eFYuHDhWKl9yVvf+taIiHjiiSeKPA71K/P8j/fd7343jhw54gtzeMXsARpZvcz/eCtWrIiIiN/+9rcNc2LPq2P+64NiO06tVotTTz31mIHet2/fhN+I9sADD8T+/fvHPorw4osvxvbt26O9vX3sn1245JJLYteuXdHe3h5nnnnmlK19wYIF8cADD8Qf//jHWLhw4djtDz74YETEy/4ZCDhe5vkfb/PmzbFgwYKxj/rAybIHaGT1Mv/jDQ4ORkTE0qVLp/2xycX814eGK7Y///nPT/jtYu9///vjkksuiXvvvTeuvvrquOyyy+KZZ56J2267Lc4555z43e9+97L7nH322XHhhRfGLbfcMvaNaL/5zW+O+brvW2+9Ne677754+9vfHtddd1288Y1vjEOHDsXIyEjs2rUrvvWtb01aOl8axv/2Gftrrrkmtm3bFu95z3vixhtvjNe//vXxxBNPxO233x7z5s2Lyy+//CRfIepZvc7/S371q1/Fnj174qabbmqor7fn5NkDNLJ6nf+enp7Yv39/vOtd74qFCxfG6Oho/OQnP4m777471qxZE+eff/5JvkLUM/PfAGb626umy0vfiDbRz1NPPVVVVVXdeeedVVtbWzVnzpzqvPPOq+6+++6qp6enOv6liojqmmuuqTZt2lS1t7dXs2fPrpYtW1Zt27btZY/97LPPVtddd121ZMmSavbs2dVZZ51VnX/++dXNN99c/f3vfz8m8/hvRFu8eHG1ePHik3qOjz76aHXppZdWra2t1Zw5c6pzzz23WrduXfX73//+Fb1W1J9GmP+qqqorr7yyqtVq1ZNPPnnS96Ex2AM0snqf/507d1bvfve7q3nz5lVNTU3V3LlzqxUrVlRf+9rXqhdeeOEVv17UF/PfOGpVVVWlyzIAAABMF//cDwAAAKkptgAAAKSm2AIAAJCaYgsAAEBqii0AAACpKbYAAACkptgCAACQWtNML6CU/v7+onm9vb1F81paWormRUT09fUVzevs7Cyax/TZvXt30bzS+2nHjh1F8yIiDh48WDRvcHCwaJ79NL0GBgaK5q1fv75o3lQove/b2tqK5jGxkZGRonmlzwdKHwNKv19HRDQ3NxfNGx4eLppnP01udHS0aN7/+x4o/XwjzOyJuGILAABAaootAAAAqSm2AAAApKbYAgAAkJpiCwAAQGqKLQAAAKkptgAAAKSm2AIAAJCaYgsAAEBqii0AAACpKbYAAACkptgCAACQmmILAABAaootAAAAqSm2AAAApKbYAgAAkJpiCwAAQGqKLQAAAKk1zdQD7969u2jeFVdcUTRv9erVRfNaWlqK5kVEdHV1Fc0bHR0tmsf0uf7664vmlZ6F7u7uonkREXfddVfRvKnYo0xsZGSkaF7p98MMduzYUTSv9PsIE/t/f623bt1aNG9wcLBoXkT5Y4BzoOlV+vUu/X5Y+phSen0REf39/UXzent7i+bNBFdsAQAASE2xBQAAIDXFFgAAgNQUWwAAAFJTbAEAAEhNsQUAACA1xRYAAIDUFFsAAABSU2wBAABITbEFAAAgNcUWAACA1BRbAAAAUlNsAQAASE2xBQAAIDXFFgAAgNQUWwAAAFJTbAEAAEhNsQUAACA1xRYAAIDUalVVVTPxwNdff33RvJGRkaJ5O3bsKJrX2dlZNC8ioqWlpWhe6efM9Ck9/6Vna2hoqGheRMTatWuL5o2OjhbNY3r19fUVzevo6Ciat2rVqqJ5ERErV64smrd79+6iefCS0ud8ERHDw8NF88w/U2kqekDp41Tp4+hMcMUWAACA1BRbAAAAUlNsAQAASE2xBQAAIDXFFgAAgNQUWwAAAFJTbAEAAEhNsQUAACA1xRYAAIDUFFsAAABSU2wBAABITbEFAAAgNcUWAACA1BRbAAAAUlNsAQAASE2xBQAAIDXFFgAAgNQUWwAAAFJTbAEAAEitaaYeuK2trWjeyMhI0bze3t6ieUNDQ0XzIiIee+yx4pnkNDo6WjSv9H7q6ekpmhcR0dLSUjSv9HMu/R7H5Lq7u4vmlT4GTIXSx5XSzznDa8j06OjoKJ7Z399fNK/0cbT0MYrJlT6Gd3V1Fc2bCn19fTO9hP87rtgCAACQmmILAABAaootAAAAqSm2AAAApKbYAgAAkJpiCwAAQGqKLQAAAKkptgAAAKSm2AIAAJCaYgsAAEBqii0AAACpKbYAAACkptgCAACQmmILAABAaootAAAAqSm2AAAApKbYAgAAkJpiCwAAQGqKLQAAAKnVqqqqZnoRJXR0dBTNe/zxx4vmrV27tmheRER/f3/xTKbHwMBA0byurq6ieY2op6enaF5vb2/RvHozPDxcNK+zs7No3sGDB4vmTYXSx5XSM9vW1lY0D8YrPV+lj6N9fX1F85hcIx5TtmzZUjSvu7u7aN5McMUWAACA1BRbAAAAUlNsAQAASE2xBQAAIDXFFgAAgNQUWwAAAFJTbAEAAEhNsQUAACA1xRYAAIDUFFsAAABSU2wBAABITbEFAAAgNcUWAACA1BRbAAAAUlNsAQAASE2xBQAAIDXFFgAAgNQUWwAAAFJTbAEAAEhNsQUAACC1WlVV1UwvooSOjo6ZXsKkWlpaimeWfs59fX1F85jY7t27i+bt2LGjaN7w8HDRvJGRkaJ5EeXXOBV7lImV3gOrVq0qmlfa6tWri2eW3veQSWdn50wvYVKl3+Pqzejo6EwvYVKlzwmmYl5Ln1tNxbnadHPFFgAAgNQUWwAAAFJTbAEAAEhNsQUAACA1xRYAAIDUFFsAAABSU2wBAABITbEFAAAgNcUWAACA1BRbAAAAUlNsAQAASE2xBQAAIDXFFgAAgNQUWwAAAFJTbAEAAEhNsQUAACA1xRYAAIDUFFsAAABSU2wBAABIrWmmF1BKS0tL0bzOzs6ieb29vUXzIso/59JrLL2+elJ6vg4ePFg0r7+/v2heV1dX0bwI85Vd6T2wfv36onl33XVX0bwrrriiaB6MNzAwUDRv8eLFRfOGh4eL5k1F5lScpzGxoaGhonk9PT1F8zZu3Fg0r7u7u2heRPnjyujoaNG8mThPc8UWAACA1BRbAAAAUlNsAQAASE2xBQAAIDXFFgAAgNQUWwAAAFJTbAEAAEhNsQUAACA1xRYAAIDUFFsAAABSU2wBAABITbEFAAAgNcUWAACA1BRbAAAAUlNsAQAASE2xBQAAIDXFFgAAgNQUWwAAAFJTbAEAAEitaaYXUMpnPvOZonldXV1F8zZu3Fg0LyJi9erVRfNaWlqK5jF9nn/++aJ5Bw8eLJrX3d1dNA+m2lve8paieaXfr2G8r371q0XzhoaGiuY1NzcXzYsof1xxnJpeK1euLJrX2dlZNK/0nhodHS2aFxGxfv36onn10ANcsQUAACA1xRYAAIDUFFsAAABSU2wBAABITbEFAAAgNcUWAACA1BRbAAAAUlNsAQAASE2xBQAAIDXFFgAAgNQUWwAAAFJTbAEAAEhNsQUAACA1xRYAAIDUFFsAAABSU2wBAABITbEFAAAgNcUWAACA1BRbAAAAUqtVVVXN9CIAAADg1XLFFgAAgNQUWwAAAFJTbAEAAEhNsQUAACA1xRYAAIDUFFsAAABSU2wBAABITbEFAAAgNcUWAACA1P4F8SqxxH5w1EMAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# import necessary packages\n", "import numpy as np\n", @@ -2176,12 +1976,9 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 2, "id": "bbb70e18", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "from tensorflow.keras.layers import Input\n", @@ -2205,12 +2002,9 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 3, "id": "ee5585fa", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "\n", @@ -2235,13 +2029,278 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 4, "id": "f2f3fa89", - "metadata": { - "collapsed": false, - "editable": true - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Metal device set to: Apple M1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n", + " super(SGD, self).__init__(name, **kwargs)\n", + "2022-10-20 08:47:09.846624: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "12/12 [==============================] - 0s 26ms/step - loss: 2.3578 - accuracy: 0.1278\n", + "Learning rate = 1e-05\n", + "Lambda = 1e-05\n", + "Test accuracy: 0.128\n", + "\n", + "12/12 [==============================] - 0s 7ms/step - loss: 2.3709 - accuracy: 0.1278\n", + "Learning rate = 1e-05\n", + "Lambda = 0.0001\n", + "Test accuracy: 0.128\n", + "\n", + "12/12 [==============================] - 0s 8ms/step - loss: 2.5019 - accuracy: 0.1278\n", + "Learning rate = 1e-05\n", + "Lambda = 0.001\n", + "Test accuracy: 0.128\n", + "\n", + "12/12 [==============================] - 0s 7ms/step - loss: 3.8106 - accuracy: 0.1278\n", + "Learning rate = 1e-05\n", + "Lambda = 0.01\n", + "Test accuracy: 0.128\n", + "\n", + "12/12 [==============================] - 0s 7ms/step - loss: 16.8209 - accuracy: 0.1278\n", + "Learning rate = 1e-05\n", + "Lambda = 0.1\n", + "Test accuracy: 0.128\n", + "\n", + "12/12 [==============================] - 0s 10ms/step - loss: 139.3979 - accuracy: 0.1278\n", + "Learning rate = 1e-05\n", + "Lambda = 1.0\n", + "Test accuracy: 0.128\n", + "\n", + "12/12 [==============================] - 0s 7ms/step - loss: 800.9124 - accuracy: 0.1139\n", + "Learning rate = 1e-05\n", + "Lambda = 10.0\n", + "Test accuracy: 0.114\n", + "\n", + "12/12 [==============================] - 0s 10ms/step - loss: 2.3165 - accuracy: 0.1333\n", + "Learning rate = 0.0001\n", + "Lambda = 1e-05\n", + "Test accuracy: 0.133\n", + "\n", + "12/12 [==============================] - 0s 7ms/step - loss: 2.3297 - accuracy: 0.1333\n", + "Learning rate = 0.0001\n", + "Lambda = 0.0001\n", + "Test accuracy: 0.133\n", + "\n", + "12/12 [==============================] - 0s 7ms/step - loss: 2.4605 - accuracy: 0.1333\n", + "Learning rate = 0.0001\n", + "Lambda = 0.001\n", + "Test accuracy: 0.133\n", + "\n", + "12/12 [==============================] - 0s 7ms/step - loss: 3.7615 - accuracy: 0.1333\n", + "Learning rate = 0.0001\n", + "Lambda = 0.01\n", + "Test accuracy: 0.133\n", + "\n", + "12/12 [==============================] - 0s 8ms/step - loss: 16.0185 - accuracy: 0.1333\n", + "Learning rate = 0.0001\n", + "Lambda = 0.1\n", + "Test accuracy: 0.133\n", + "\n", + "12/12 [==============================] - 0s 8ms/step - loss: 82.1668 - accuracy: 0.1222\n", + "Learning rate = 0.0001\n", + "Lambda = 1.0\n", + "Test accuracy: 0.122\n", + "\n", + "12/12 [==============================] - 0s 9ms/step - loss: 5.9034 - accuracy: 0.1056\n", + "Learning rate = 0.0001\n", + "Lambda = 10.0\n", + "Test accuracy: 0.106\n", + "\n", + "12/12 [==============================] - 0s 8ms/step - loss: 2.1629 - accuracy: 0.5222\n", + "Learning rate = 0.001\n", + "Lambda = 1e-05\n", + "Test accuracy: 0.522\n", + "\n", + "12/12 [==============================] - 0s 8ms/step - loss: 2.1757 - accuracy: 0.5222\n", + "Learning rate = 0.001\n", + "Lambda = 0.0001\n", + "Test accuracy: 0.522\n", + "\n", + "12/12 [==============================] - 0s 10ms/step - loss: 2.3064 - accuracy: 0.5278\n", + "Learning rate = 0.001\n", + "Lambda = 0.001\n", + "Test accuracy: 0.528\n", + "\n", + "12/12 [==============================] - 0s 8ms/step - loss: 3.5376 - accuracy: 0.5167\n", + "Learning rate = 0.001\n", + "Lambda = 0.01\n", + "Test accuracy: 0.517\n", + "\n", + "12/12 [==============================] - 0s 8ms/step - loss: 10.1971 - accuracy: 0.5194\n", + "Learning rate = 0.001\n", + "Lambda = 0.1\n", + "Test accuracy: 0.519\n", + "\n", + "12/12 [==============================] - 0s 7ms/step - loss: 2.6673 - accuracy: 0.2278\n", + "Learning rate = 0.001\n", + "Lambda = 1.0\n", + "Test accuracy: 0.228\n", + "\n", + "12/12 [==============================] - 0s 7ms/step - loss: 2.3055 - accuracy: 0.0889\n", + "Learning rate = 0.001\n", + "Lambda = 10.0\n", + "Test accuracy: 0.089\n", + "\n", + "12/12 [==============================] - 0s 7ms/step - loss: 0.9916 - accuracy: 0.9139\n", + "Learning rate = 0.01\n", + "Lambda = 1e-05\n", + "Test accuracy: 0.914\n", + "\n", + "12/12 [==============================] - 0s 8ms/step - loss: 1.0080 - accuracy: 0.9139\n", + "Learning rate = 0.01\n", + "Lambda = 0.0001\n", + "Test accuracy: 0.914\n", + "\n", + "12/12 [==============================] - 0s 9ms/step - loss: 1.1547 - accuracy: 0.9139\n", + "Learning rate = 0.01\n", + "Lambda = 0.001\n", + "Test accuracy: 0.914\n", + "\n", + "12/12 [==============================] - 0s 9ms/step - loss: 2.0397 - accuracy: 0.9028\n", + "Learning rate = 0.01\n", + "Lambda = 0.01\n", + "Test accuracy: 0.903\n", + "\n", + "12/12 [==============================] - 0s 9ms/step - loss: 2.2562 - accuracy: 0.4806\n", + "Learning rate = 0.01\n", + "Lambda = 0.1\n", + "Test accuracy: 0.481\n", + "\n", + "12/12 [==============================] - 0s 9ms/step - loss: 2.3076 - accuracy: 0.0778\n", + "Learning rate = 0.01\n", + "Lambda = 1.0\n", + "Test accuracy: 0.078\n", + "\n", + "12/12 [==============================] - 0s 9ms/step - loss: 2.3084 - accuracy: 0.0778\n", + "Learning rate = 0.01\n", + "Lambda = 10.0\n", + "Test accuracy: 0.078\n", + "\n", + "12/12 [==============================] - 0s 10ms/step - loss: 0.0887 - accuracy: 0.9806\n", + "Learning rate = 0.1\n", + "Lambda = 1e-05\n", + "Test accuracy: 0.981\n", + "\n", + "12/12 [==============================] - 0s 9ms/step - loss: 0.1090 - accuracy: 0.9806\n", + "Learning rate = 0.1\n", + "Lambda = 0.0001\n", + "Test accuracy: 0.981\n", + "\n", + "12/12 [==============================] - 0s 9ms/step - loss: 0.2472 - accuracy: 0.9806\n", + "Learning rate = 0.1\n", + "Lambda = 0.001\n", + "Test accuracy: 0.981\n", + "\n", + "12/12 [==============================] - 0s 10ms/step - loss: 0.4871 - accuracy: 0.9583\n", + "Learning rate = 0.1\n", + "Lambda = 0.01\n", + "Test accuracy: 0.958\n", + "\n", + "12/12 [==============================] - 0s 10ms/step - loss: 1.4946 - accuracy: 0.7000\n", + "Learning rate = 0.1\n", + "Lambda = 0.1\n", + "Test accuracy: 0.700\n", + "\n", + "12/12 [==============================] - 0s 9ms/step - loss: 2.3065 - accuracy: 0.1250\n", + "Learning rate = 0.1\n", + "Lambda = 1.0\n", + "Test accuracy: 0.125\n", + "\n", + "12/12 [==============================] - 0s 9ms/step - loss: 1594.4144 - accuracy: 0.0722\n", + "Learning rate = 0.1\n", + "Lambda = 10.0\n", + "Test accuracy: 0.072\n", + "\n", + "12/12 [==============================] - 0s 9ms/step - loss: 0.0669 - accuracy: 0.9778\n", + "Learning rate = 1.0\n", + "Lambda = 1e-05\n", + "Test accuracy: 0.978\n", + "\n", + "12/12 [==============================] - 0s 9ms/step - loss: 0.0910 - accuracy: 0.9778\n", + "Learning rate = 1.0\n", + "Lambda = 0.0001\n", + "Test accuracy: 0.978\n", + "\n", + "12/12 [==============================] - 0s 9ms/step - loss: 0.4291 - accuracy: 0.9556\n", + "Learning rate = 1.0\n", + "Lambda = 0.001\n", + "Test accuracy: 0.956\n", + "\n", + "12/12 [==============================] - 0s 9ms/step - loss: 1.7840 - accuracy: 0.5944\n", + "Learning rate = 1.0\n", + "Lambda = 0.01\n", + "Test accuracy: 0.594\n", + "\n", + "12/12 [==============================] - 0s 9ms/step - loss: 2.4207 - accuracy: 0.0778\n", + "Learning rate = 1.0\n", + "Lambda = 0.1\n", + "Test accuracy: 0.078\n", + "\n", + "12/12 [==============================] - 0s 8ms/step - loss: 363.3102 - accuracy: 0.1139\n", + "Learning rate = 1.0\n", + "Lambda = 1.0\n", + "Test accuracy: 0.114\n", + "\n", + "12/12 [==============================] - 0s 9ms/step - loss: nan - accuracy: 0.0778\n", + "Learning rate = 1.0\n", + "Lambda = 10.0\n", + "Test accuracy: 0.078\n", + "\n", + "12/12 [==============================] - 0s 9ms/step - loss: 2.4347 - accuracy: 0.0889\n", + "Learning rate = 10.0\n", + "Lambda = 1e-05\n", + "Test accuracy: 0.089\n", + "\n", + "12/12 [==============================] - 0s 8ms/step - loss: 2.5265 - accuracy: 0.0889\n", + "Learning rate = 10.0\n", + "Lambda = 0.0001\n", + "Test accuracy: 0.089\n", + "\n", + "12/12 [==============================] - 0s 9ms/step - loss: 2.4089 - accuracy: 0.1056\n", + "Learning rate = 10.0\n", + "Lambda = 0.001\n", + "Test accuracy: 0.106\n", + "\n", + "12/12 [==============================] - 0s 9ms/step - loss: 2.5464 - accuracy: 0.0889\n", + "Learning rate = 10.0\n", + "Lambda = 0.01\n", + "Test accuracy: 0.089\n", + "\n", + "12/12 [==============================] - 0s 11ms/step - loss: 3079.4927 - accuracy: 0.1056\n", + "Learning rate = 10.0\n", + "Lambda = 0.1\n", + "Test accuracy: 0.106\n", + "\n", + "12/12 [==============================] - 0s 11ms/step - loss: nan - accuracy: 0.0778\n", + "Learning rate = 10.0\n", + "Lambda = 1.0\n", + "Test accuracy: 0.078\n", + "\n", + "12/12 [==============================] - 0s 12ms/step - loss: nan - accuracy: 0.0778\n", + "Learning rate = 10.0\n", + "Lambda = 10.0\n", + "Test accuracy: 0.078\n", + "\n" + ] + } + ], "source": [ "DNN_keras = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)\n", " \n", @@ -2262,13 +2321,141 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 5, "id": "02c13b5b", - "metadata": { - "collapsed": false, - "editable": true - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "45/45 [==============================] - 0s 10ms/step - loss: 2.3639 - accuracy: 0.1134\n", + "12/12 [==============================] - 0s 8ms/step - loss: 2.3578 - accuracy: 0.1278\n", + "45/45 [==============================] - 0s 9ms/step - loss: 2.3770 - accuracy: 0.1134\n", + "12/12 [==============================] - 0s 9ms/step - loss: 2.3709 - accuracy: 0.1278\n", + "45/45 [==============================] - 0s 9ms/step - loss: 2.5080 - accuracy: 0.1134\n", + "12/12 [==============================] - 0s 9ms/step - loss: 2.5019 - accuracy: 0.1278\n", + "45/45 [==============================] - 0s 9ms/step - loss: 3.8168 - accuracy: 0.1134\n", + "12/12 [==============================] - 0s 9ms/step - loss: 3.8106 - accuracy: 0.1278\n", + "45/45 [==============================] - 0s 8ms/step - loss: 16.8270 - accuracy: 0.1134\n", + "12/12 [==============================] - 0s 8ms/step - loss: 16.8209 - accuracy: 0.1278\n", + "45/45 [==============================] - 0s 8ms/step - loss: 139.4043 - accuracy: 0.1134\n", + "12/12 [==============================] - 0s 9ms/step - loss: 139.3979 - accuracy: 0.1278\n", + "45/45 [==============================] - 1s 11ms/step - loss: 800.9208 - accuracy: 0.1002\n", + "12/12 [==============================] - 0s 11ms/step - loss: 800.9124 - accuracy: 0.1139\n", + "45/45 [==============================] - 1s 11ms/step - loss: 2.3173 - accuracy: 0.1253\n", + "12/12 [==============================] - 0s 10ms/step - loss: 2.3165 - accuracy: 0.1333\n", + "45/45 [==============================] - 0s 9ms/step - loss: 2.3304 - accuracy: 0.1253\n", + "12/12 [==============================] - 0s 9ms/step - loss: 2.3297 - accuracy: 0.1333\n", + "45/45 [==============================] - 0s 9ms/step - loss: 2.4614 - accuracy: 0.1253\n", + "12/12 [==============================] - 0s 9ms/step - loss: 2.4605 - accuracy: 0.1333\n", + "45/45 [==============================] - 0s 9ms/step - loss: 3.7623 - accuracy: 0.1253\n", + "12/12 [==============================] - 0s 9ms/step - loss: 3.7615 - accuracy: 0.1333\n", + "45/45 [==============================] - 0s 10ms/step - loss: 16.0195 - accuracy: 0.1232\n", + "12/12 [==============================] - 0s 11ms/step - loss: 16.0185 - accuracy: 0.1333\n", + "45/45 [==============================] - 0s 11ms/step - loss: 82.1703 - accuracy: 0.1072\n", + "12/12 [==============================] - 0s 9ms/step - loss: 82.1668 - accuracy: 0.1222\n", + "45/45 [==============================] - 1s 11ms/step - loss: 5.9143 - accuracy: 0.0995\n", + "12/12 [==============================] - 0s 11ms/step - loss: 5.9034 - accuracy: 0.1056\n", + "45/45 [==============================] - 0s 11ms/step - loss: 2.1466 - accuracy: 0.5790\n", + "12/12 [==============================] - 0s 11ms/step - loss: 2.1629 - accuracy: 0.5222\n", + "45/45 [==============================] - 1s 11ms/step - loss: 2.1598 - accuracy: 0.5811\n", + "12/12 [==============================] - 0s 10ms/step - loss: 2.1757 - accuracy: 0.5222\n", + "45/45 [==============================] - 1s 11ms/step - loss: 2.2904 - accuracy: 0.5797\n", + "12/12 [==============================] - 0s 11ms/step - loss: 2.3064 - accuracy: 0.5278\n", + "45/45 [==============================] - 0s 11ms/step - loss: 3.5214 - accuracy: 0.5804\n", + "12/12 [==============================] - 0s 8ms/step - loss: 3.5376 - accuracy: 0.5167\n", + "45/45 [==============================] - 0s 8ms/step - loss: 10.1843 - accuracy: 0.5706\n", + "12/12 [==============================] - 0s 8ms/step - loss: 10.1971 - accuracy: 0.5194\n", + "45/45 [==============================] - 0s 9ms/step - loss: 2.6645 - accuracy: 0.2651\n", + "12/12 [==============================] - 0s 9ms/step - loss: 2.6673 - accuracy: 0.2278\n", + "45/45 [==============================] - 0s 9ms/step - loss: 2.3028 - accuracy: 0.1044\n", + "12/12 [==============================] - 0s 10ms/step - loss: 2.3055 - accuracy: 0.0889\n", + "45/45 [==============================] - 1s 11ms/step - loss: 0.9462 - accuracy: 0.9130\n", + "12/12 [==============================] - 0s 11ms/step - loss: 0.9916 - accuracy: 0.9139\n", + "45/45 [==============================] - 1s 11ms/step - loss: 0.9620 - accuracy: 0.9144\n", + "12/12 [==============================] - 0s 10ms/step - loss: 1.0080 - accuracy: 0.9139\n", + "45/45 [==============================] - 0s 9ms/step - loss: 1.1097 - accuracy: 0.9137\n", + "12/12 [==============================] - 0s 9ms/step - loss: 1.1547 - accuracy: 0.9139\n", + "45/45 [==============================] - 0s 9ms/step - loss: 1.9961 - accuracy: 0.9074\n", + "12/12 [==============================] - 0s 9ms/step - loss: 2.0397 - accuracy: 0.9028\n", + "45/45 [==============================] - 0s 9ms/step - loss: 2.2384 - accuracy: 0.5351\n", + "12/12 [==============================] - 0s 9ms/step - loss: 2.2562 - accuracy: 0.4806\n", + "45/45 [==============================] - 0s 9ms/step - loss: 2.3020 - accuracy: 0.1044\n", + "12/12 [==============================] - 0s 10ms/step - loss: 2.3076 - accuracy: 0.0778\n", + "45/45 [==============================] - 1s 11ms/step - loss: 2.3020 - accuracy: 0.1044\n", + "12/12 [==============================] - 0s 10ms/step - loss: 2.3084 - accuracy: 0.0778\n", + "45/45 [==============================] - 0s 10ms/step - loss: 0.0410 - accuracy: 0.9993\n", + "12/12 [==============================] - 0s 11ms/step - loss: 0.0887 - accuracy: 0.9806\n", + "45/45 [==============================] - 0s 10ms/step - loss: 0.0620 - accuracy: 0.9986\n", + "12/12 [==============================] - 0s 9ms/step - loss: 0.1090 - accuracy: 0.9806\n", + "45/45 [==============================] - 0s 9ms/step - loss: 0.2020 - accuracy: 0.9979\n", + "12/12 [==============================] - 0s 11ms/step - loss: 0.2472 - accuracy: 0.9806\n", + "45/45 [==============================] - 1s 11ms/step - loss: 0.4497 - accuracy: 0.9798\n", + "12/12 [==============================] - 0s 11ms/step - loss: 0.4871 - accuracy: 0.9583\n", + "45/45 [==============================] - 1s 11ms/step - loss: 1.4627 - accuracy: 0.7432\n", + "12/12 [==============================] - 0s 19ms/step - loss: 1.4946 - accuracy: 0.7000\n", + "45/45 [==============================] - 1s 11ms/step - loss: 2.3063 - accuracy: 0.0953\n", + "12/12 [==============================] - 0s 10ms/step - loss: 2.3065 - accuracy: 0.1250\n", + "45/45 [==============================] - 1s 11ms/step - loss: 1594.4108 - accuracy: 0.0765\n", + "12/12 [==============================] - 0s 10ms/step - loss: 1594.4144 - accuracy: 0.0722\n", + "45/45 [==============================] - 0s 10ms/step - loss: 0.0053 - accuracy: 1.0000\n", + "12/12 [==============================] - 0s 10ms/step - loss: 0.0669 - accuracy: 0.9778\n", + "45/45 [==============================] - 0s 10ms/step - loss: 0.0258 - accuracy: 1.0000\n", + "12/12 [==============================] - 0s 10ms/step - loss: 0.0910 - accuracy: 0.9778\n", + "45/45 [==============================] - 0s 10ms/step - loss: 0.3921 - accuracy: 0.9659\n", + "12/12 [==============================] - 0s 9ms/step - loss: 0.4291 - accuracy: 0.9556\n", + "45/45 [==============================] - 0s 9ms/step - loss: 1.7498 - accuracy: 0.6291\n", + "12/12 [==============================] - 0s 9ms/step - loss: 1.7840 - accuracy: 0.5944\n", + "45/45 [==============================] - 0s 10ms/step - loss: 2.3903 - accuracy: 0.1072\n", + "12/12 [==============================] - 0s 10ms/step - loss: 2.4207 - accuracy: 0.0778\n", + "45/45 [==============================] - 0s 10ms/step - loss: 362.7305 - accuracy: 0.0967\n", + "12/12 [==============================] - 0s 9ms/step - loss: 363.3102 - accuracy: 0.1139\n", + "45/45 [==============================] - 0s 9ms/step - loss: nan - accuracy: 0.1044\n", + "12/12 [==============================] - 0s 9ms/step - loss: nan - accuracy: 0.0778\n", + "45/45 [==============================] - 0s 9ms/step - loss: 2.4005 - accuracy: 0.1044\n", + "12/12 [==============================] - 0s 10ms/step - loss: 2.4347 - accuracy: 0.0889\n", + "45/45 [==============================] - 0s 10ms/step - loss: 2.4793 - accuracy: 0.1044\n", + "12/12 [==============================] - 0s 11ms/step - loss: 2.5265 - accuracy: 0.0889\n", + "45/45 [==============================] - 1s 11ms/step - loss: 2.4134 - accuracy: 0.1079\n", + "12/12 [==============================] - 0s 10ms/step - loss: 2.4089 - accuracy: 0.1056\n", + "45/45 [==============================] - 1s 11ms/step - loss: 2.5178 - accuracy: 0.1037\n", + "12/12 [==============================] - 0s 11ms/step - loss: 2.5464 - accuracy: 0.0889\n", + "45/45 [==============================] - 1s 12ms/step - loss: 3076.1086 - accuracy: 0.0995\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "12/12 [==============================] - 0s 10ms/step - loss: 3079.4927 - accuracy: 0.1056\n", + "45/45 [==============================] - 1s 11ms/step - loss: nan - accuracy: 0.1044\n", + "12/12 [==============================] - 0s 11ms/step - loss: nan - accuracy: 0.0778\n", + "45/45 [==============================] - 0s 10ms/step - loss: nan - accuracy: 0.1044\n", + "12/12 [==============================] - 0s 10ms/step - loss: nan - accuracy: 0.0778\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# optional\n", "# visual representation of grid search\n", @@ -2306,22 +2493,2645 @@ { "cell_type": "markdown", "id": "2b9be2b5", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The Breast Cancer Data, now with Keras" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 6, "id": "e4543f99", - "metadata": { - "collapsed": false, - "editable": true - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The content of the breast cancer dataset is:\n", + "['mean radius' 'mean texture' 'mean perimeter' 'mean area'\n", + " 'mean smoothness' 'mean compactness' 'mean concavity'\n", + " 'mean concave points' 'mean symmetry' 'mean fractal dimension'\n", + " 'radius error' 'texture error' 'perimeter error' 'area error'\n", + " 'smoothness error' 'compactness error' 'concavity error'\n", + " 'concave points error' 'symmetry error' 'fractal dimension error'\n", + " 'worst radius' 'worst texture' 'worst perimeter' 'worst area'\n", + " 'worst smoothness' 'worst compactness' 'worst concavity'\n", + " 'worst concave points' 'worst symmetry' 'worst fractal dimension']\n", + "-------------------------\n", + "inputs = (569, 30)\n", + "outputs = (569,)\n", + "labels = (30,)\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/100\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n", + " super(SGD, self).__init__(name, **kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6/6 [==============================] - 0s 24ms/step - loss: 0.7171 - accuracy: 0.5820\n", + "Epoch 2/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 0.7169 - accuracy: 0.6289\n", + "Epoch 3/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 0.7167 - accuracy: 0.6289\n", + "Epoch 4/100\n", + "6/6 [==============================] - 0s 14ms/step - loss: 0.7165 - accuracy: 0.6289\n", + "Epoch 5/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7162 - accuracy: 0.6289\n", + "Epoch 6/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.7161 - accuracy: 0.6289\n", + "Epoch 7/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7159 - accuracy: 0.6289\n", + "Epoch 8/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.7157 - accuracy: 0.6289\n", + "Epoch 9/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.7155 - accuracy: 0.6289\n", + "Epoch 10/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.7153 - accuracy: 0.6289\n", + "Epoch 11/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.7151 - accuracy: 0.6289\n", + "Epoch 12/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.7150 - accuracy: 0.6289\n", + "Epoch 13/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7147 - accuracy: 0.6289\n", + "Epoch 14/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.7145 - accuracy: 0.6289\n", + "Epoch 15/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7143 - accuracy: 0.6289\n", + "Epoch 16/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 0.7141 - accuracy: 0.6289\n", + "Epoch 17/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7140 - accuracy: 0.6289\n", + "Epoch 18/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7138 - accuracy: 0.6289\n", + "Epoch 19/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7136 - accuracy: 0.6289\n", + "Epoch 20/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.7134 - accuracy: 0.6289\n", + "Epoch 21/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.7133 - accuracy: 0.6289\n", + "Epoch 22/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7131 - accuracy: 0.6289\n", + "Epoch 23/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7129 - accuracy: 0.6289\n", + "Epoch 24/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.7128 - accuracy: 0.6289\n", + "Epoch 25/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7126 - accuracy: 0.6289\n", + "Epoch 26/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.7124 - accuracy: 0.6289\n", + "Epoch 27/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7122 - accuracy: 0.6289\n", + "Epoch 28/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7120 - accuracy: 0.6289\n", + "Epoch 29/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7118 - accuracy: 0.6289\n", + "Epoch 30/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7117 - accuracy: 0.6289\n", + "Epoch 31/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7115 - accuracy: 0.6289\n", + "Epoch 32/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7113 - accuracy: 0.6289\n", + "Epoch 33/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7111 - accuracy: 0.6289\n", + "Epoch 34/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7110 - accuracy: 0.6289\n", + "Epoch 35/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7108 - accuracy: 0.6289\n", + "Epoch 36/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7107 - accuracy: 0.6289\n", + "Epoch 37/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7105 - accuracy: 0.6289\n", + "Epoch 38/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7103 - accuracy: 0.6289\n", + "Epoch 39/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7101 - accuracy: 0.6289\n", + "Epoch 40/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7099 - accuracy: 0.6289\n", + "Epoch 41/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7098 - accuracy: 0.6289\n", + "Epoch 42/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.7096 - accuracy: 0.6289\n", + "Epoch 43/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7094 - accuracy: 0.6289\n", + "Epoch 44/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7093 - accuracy: 0.6289\n", + "Epoch 45/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7091 - accuracy: 0.6289\n", + "Epoch 46/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7090 - accuracy: 0.6289\n", + "Epoch 47/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7088 - accuracy: 0.6289\n", + "Epoch 48/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7086 - accuracy: 0.6289\n", + "Epoch 49/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7084 - accuracy: 0.6289\n", + "Epoch 50/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7083 - accuracy: 0.6289\n", + "Epoch 51/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7081 - accuracy: 0.6289\n", + "Epoch 52/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7079 - accuracy: 0.6289\n", + "Epoch 53/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7077 - accuracy: 0.6289\n", + "Epoch 54/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7076 - accuracy: 0.6289\n", + "Epoch 55/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7074 - accuracy: 0.6289\n", + "Epoch 56/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.7072 - accuracy: 0.6289\n", + "Epoch 57/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7071 - accuracy: 0.6289\n", + "Epoch 58/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7069 - accuracy: 0.6289\n", + "Epoch 59/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7068 - accuracy: 0.6289\n", + "Epoch 60/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.7066 - accuracy: 0.6289\n", + "Epoch 61/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7065 - accuracy: 0.6289\n", + "Epoch 62/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7064 - accuracy: 0.6289\n", + "Epoch 63/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7062 - accuracy: 0.6289\n", + "Epoch 64/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7061 - accuracy: 0.6289\n", + "Epoch 65/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7060 - accuracy: 0.6289\n", + "Epoch 66/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7058 - accuracy: 0.6289\n", + "Epoch 67/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7057 - accuracy: 0.6289\n", + "Epoch 68/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7056 - accuracy: 0.6289\n", + "Epoch 69/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7054 - accuracy: 0.6289\n", + "Epoch 70/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7053 - accuracy: 0.6289\n", + "Epoch 71/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7051 - accuracy: 0.6289\n", + "Epoch 72/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7050 - accuracy: 0.6289\n", + "Epoch 73/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.7049 - accuracy: 0.6289\n", + "Epoch 74/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.7047 - accuracy: 0.6289\n", + "Epoch 75/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.7046 - accuracy: 0.6289\n", + "Epoch 76/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7044 - accuracy: 0.6289\n", + "Epoch 77/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7043 - accuracy: 0.6289\n", + "Epoch 78/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7042 - accuracy: 0.6289\n", + "Epoch 79/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7041 - accuracy: 0.6289\n", + "Epoch 80/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7040 - accuracy: 0.6289\n", + "Epoch 81/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.7038 - accuracy: 0.6289\n", + "Epoch 82/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.7037 - accuracy: 0.6289\n", + "Epoch 83/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7036 - accuracy: 0.6289\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 84/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.7034 - accuracy: 0.6289\n", + "Epoch 85/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7033 - accuracy: 0.6289\n", + "Epoch 86/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.7032 - accuracy: 0.6289\n", + "Epoch 87/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7030 - accuracy: 0.6289\n", + "Epoch 88/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.7029 - accuracy: 0.6289\n", + "Epoch 89/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.7028 - accuracy: 0.6289\n", + "Epoch 90/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7027 - accuracy: 0.6289\n", + "Epoch 91/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7025 - accuracy: 0.6289\n", + "Epoch 92/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7024 - accuracy: 0.6289\n", + "Epoch 93/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7023 - accuracy: 0.6289\n", + "Epoch 94/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7021 - accuracy: 0.6289\n", + "Epoch 95/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7020 - accuracy: 0.6289\n", + "Epoch 96/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7019 - accuracy: 0.6289\n", + "Epoch 97/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7018 - accuracy: 0.6289\n", + "Epoch 98/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7017 - accuracy: 0.6289\n", + "Epoch 99/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7015 - accuracy: 0.6289\n", + "Epoch 100/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7014 - accuracy: 0.6289\n", + "16/16 [==============================] - 0s 9ms/step - loss: 0.7013 - accuracy: 0.6289\n", + "2/2 [==============================] - 0s 37ms/step - loss: 0.7033 - accuracy: 0.6140\n", + "Epoch 1/100\n", + "6/6 [==============================] - 0s 22ms/step - loss: 0.7165 - accuracy: 0.5859\n", + "Epoch 2/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7139 - accuracy: 0.6289\n", + "Epoch 3/100\n", + "6/6 [==============================] - 0s 14ms/step - loss: 0.7118 - accuracy: 0.6289\n", + "Epoch 4/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 0.7102 - accuracy: 0.6289\n", + "Epoch 5/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7089 - accuracy: 0.6289\n", + "Epoch 6/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7074 - accuracy: 0.6289\n", + "Epoch 7/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7058 - accuracy: 0.6289\n", + "Epoch 8/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7045 - accuracy: 0.6289\n", + "Epoch 9/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7033 - accuracy: 0.6289\n", + "Epoch 10/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7022 - accuracy: 0.6289\n", + "Epoch 11/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.7015 - accuracy: 0.6289\n", + "Epoch 12/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.7004 - accuracy: 0.6289\n", + "Epoch 13/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6994 - accuracy: 0.6289\n", + "Epoch 14/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6984 - accuracy: 0.6289\n", + "Epoch 15/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6976 - accuracy: 0.6289\n", + "Epoch 16/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6966 - accuracy: 0.6289\n", + "Epoch 17/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6957 - accuracy: 0.6289\n", + "Epoch 18/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6950 - accuracy: 0.6289\n", + "Epoch 19/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6941 - accuracy: 0.6289\n", + "Epoch 20/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6936 - accuracy: 0.6289\n", + "Epoch 21/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6930 - accuracy: 0.6289\n", + "Epoch 22/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6922 - accuracy: 0.6289\n", + "Epoch 23/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6915 - accuracy: 0.6289\n", + "Epoch 24/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6911 - accuracy: 0.6289\n", + "Epoch 25/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6905 - accuracy: 0.6289\n", + "Epoch 26/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.6900 - accuracy: 0.6289\n", + "Epoch 27/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6894 - accuracy: 0.6289\n", + "Epoch 28/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6889 - accuracy: 0.6289\n", + "Epoch 29/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6883 - accuracy: 0.6289\n", + "Epoch 30/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6878 - accuracy: 0.6289\n", + "Epoch 31/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6875 - accuracy: 0.6289\n", + "Epoch 32/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6869 - accuracy: 0.6289\n", + "Epoch 33/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6864 - accuracy: 0.6289\n", + "Epoch 34/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6861 - accuracy: 0.6289\n", + "Epoch 35/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6858 - accuracy: 0.6289\n", + "Epoch 36/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6853 - accuracy: 0.6289\n", + "Epoch 37/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6850 - accuracy: 0.6289\n", + "Epoch 38/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6848 - accuracy: 0.6289\n", + "Epoch 39/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6845 - accuracy: 0.6289\n", + "Epoch 40/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6843 - accuracy: 0.6289\n", + "Epoch 41/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6840 - accuracy: 0.6289\n", + "Epoch 42/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6838 - accuracy: 0.6289\n", + "Epoch 43/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6836 - accuracy: 0.6289\n", + "Epoch 44/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6834 - accuracy: 0.6289\n", + "Epoch 45/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6833 - accuracy: 0.6289\n", + "Epoch 46/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6831 - accuracy: 0.6289\n", + "Epoch 47/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6829 - accuracy: 0.6289\n", + "Epoch 48/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6826 - accuracy: 0.6289\n", + "Epoch 49/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6825 - accuracy: 0.6289\n", + "Epoch 50/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6823 - accuracy: 0.6289\n", + "Epoch 51/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6821 - accuracy: 0.6289\n", + "Epoch 52/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6819 - accuracy: 0.6289\n", + "Epoch 53/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6818 - accuracy: 0.6289\n", + "Epoch 54/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6817 - accuracy: 0.6289\n", + "Epoch 55/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6816 - accuracy: 0.6289\n", + "Epoch 56/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6815 - accuracy: 0.6289\n", + "Epoch 57/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6813 - accuracy: 0.6289\n", + "Epoch 58/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6812 - accuracy: 0.6289\n", + "Epoch 59/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6812 - accuracy: 0.6289\n", + "Epoch 60/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6811 - accuracy: 0.6289\n", + "Epoch 61/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6810 - accuracy: 0.6289\n", + "Epoch 62/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6810 - accuracy: 0.6289\n", + "Epoch 63/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6810 - accuracy: 0.6289\n", + "Epoch 64/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6809 - accuracy: 0.6289\n", + "Epoch 65/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6/6 [==============================] - 0s 9ms/step - loss: 0.6808 - accuracy: 0.6289\n", + "Epoch 66/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6807 - accuracy: 0.6289\n", + "Epoch 67/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6807 - accuracy: 0.6289\n", + "Epoch 68/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6806 - accuracy: 0.6289\n", + "Epoch 69/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6804 - accuracy: 0.6289\n", + "Epoch 70/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6804 - accuracy: 0.6289\n", + "Epoch 71/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6803 - accuracy: 0.6289\n", + "Epoch 72/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6802 - accuracy: 0.6289\n", + "Epoch 73/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6802 - accuracy: 0.6289\n", + "Epoch 74/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6801 - accuracy: 0.6289\n", + "Epoch 75/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6800 - accuracy: 0.6289\n", + "Epoch 76/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6799 - accuracy: 0.6289\n", + "Epoch 77/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6798 - accuracy: 0.6289\n", + "Epoch 78/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6797 - accuracy: 0.6289\n", + "Epoch 79/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6797 - accuracy: 0.6289\n", + "Epoch 80/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6797 - accuracy: 0.6289\n", + "Epoch 81/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6796 - accuracy: 0.6289\n", + "Epoch 82/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6796 - accuracy: 0.6289\n", + "Epoch 83/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6795 - accuracy: 0.6289\n", + "Epoch 84/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6795 - accuracy: 0.6289\n", + "Epoch 85/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6794 - accuracy: 0.6289\n", + "Epoch 86/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6793 - accuracy: 0.6289\n", + "Epoch 87/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6793 - accuracy: 0.6289\n", + "Epoch 88/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6792 - accuracy: 0.6289\n", + "Epoch 89/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6792 - accuracy: 0.6289\n", + "Epoch 90/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6791 - accuracy: 0.6289\n", + "Epoch 91/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6791 - accuracy: 0.6289\n", + "Epoch 92/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6791 - accuracy: 0.6289\n", + "Epoch 93/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6790 - accuracy: 0.6289\n", + "Epoch 94/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6789 - accuracy: 0.6289\n", + "Epoch 95/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6789 - accuracy: 0.6289\n", + "Epoch 96/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6788 - accuracy: 0.6289\n", + "Epoch 97/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6787 - accuracy: 0.6289\n", + "Epoch 98/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6787 - accuracy: 0.6289\n", + "Epoch 99/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6786 - accuracy: 0.6289\n", + "Epoch 100/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6786 - accuracy: 0.6289\n", + "16/16 [==============================] - 0s 8ms/step - loss: 0.6785 - accuracy: 0.6289\n", + "2/2 [==============================] - 0s 33ms/step - loss: 0.6859 - accuracy: 0.6140\n", + "Epoch 1/100\n", + "6/6 [==============================] - 0s 18ms/step - loss: 0.7109 - accuracy: 0.5781\n", + "Epoch 2/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 0.6985 - accuracy: 0.6289\n", + "Epoch 3/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.6913 - accuracy: 0.6289\n", + "Epoch 4/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6878 - accuracy: 0.6289\n", + "Epoch 5/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6840 - accuracy: 0.6289\n", + "Epoch 6/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6824 - accuracy: 0.6289\n", + "Epoch 7/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6817 - accuracy: 0.6289\n", + "Epoch 8/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6802 - accuracy: 0.6289\n", + "Epoch 9/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6796 - accuracy: 0.6289\n", + "Epoch 10/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6788 - accuracy: 0.6289\n", + "Epoch 11/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6785 - accuracy: 0.6289\n", + "Epoch 12/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6782 - accuracy: 0.6289\n", + "Epoch 13/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6777 - accuracy: 0.6289\n", + "Epoch 14/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6772 - accuracy: 0.6289\n", + "Epoch 15/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6773 - accuracy: 0.6289\n", + "Epoch 16/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6768 - accuracy: 0.6289\n", + "Epoch 17/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6759 - accuracy: 0.6289\n", + "Epoch 18/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6756 - accuracy: 0.6289\n", + "Epoch 19/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6754 - accuracy: 0.6289\n", + "Epoch 20/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6752 - accuracy: 0.6289\n", + "Epoch 21/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6743 - accuracy: 0.6289\n", + "Epoch 22/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6742 - accuracy: 0.6289\n", + "Epoch 23/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6736 - accuracy: 0.6289\n", + "Epoch 24/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6733 - accuracy: 0.6289\n", + "Epoch 25/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6733 - accuracy: 0.6289\n", + "Epoch 26/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6727 - accuracy: 0.6289\n", + "Epoch 27/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6728 - accuracy: 0.6289\n", + "Epoch 28/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6721 - accuracy: 0.6289\n", + "Epoch 29/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6720 - accuracy: 0.6289\n", + "Epoch 30/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6717 - accuracy: 0.6289\n", + "Epoch 31/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6715 - accuracy: 0.6289\n", + "Epoch 32/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6715 - accuracy: 0.6289\n", + "Epoch 33/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6711 - accuracy: 0.6289\n", + "Epoch 34/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6712 - accuracy: 0.6289\n", + "Epoch 35/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6704 - accuracy: 0.6289\n", + "Epoch 36/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6700 - accuracy: 0.6289\n", + "Epoch 37/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6697 - accuracy: 0.6289\n", + "Epoch 38/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6700 - accuracy: 0.6289\n", + "Epoch 39/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6694 - accuracy: 0.6289\n", + "Epoch 40/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6690 - accuracy: 0.6289\n", + "Epoch 41/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6687 - accuracy: 0.6289\n", + "Epoch 42/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6688 - accuracy: 0.6289\n", + "Epoch 43/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6687 - accuracy: 0.6289\n", + "Epoch 44/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6689 - accuracy: 0.6289\n", + "Epoch 45/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6679 - accuracy: 0.6289\n", + "Epoch 46/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6/6 [==============================] - 0s 9ms/step - loss: 0.6680 - accuracy: 0.6289\n", + "Epoch 47/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6677 - accuracy: 0.6289\n", + "Epoch 48/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6675 - accuracy: 0.6289\n", + "Epoch 49/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6673 - accuracy: 0.6289\n", + "Epoch 50/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6669 - accuracy: 0.6289\n", + "Epoch 51/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6670 - accuracy: 0.6289\n", + "Epoch 52/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6669 - accuracy: 0.6289\n", + "Epoch 53/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6665 - accuracy: 0.6289\n", + "Epoch 54/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6666 - accuracy: 0.6289\n", + "Epoch 55/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6667 - accuracy: 0.6289\n", + "Epoch 56/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6661 - accuracy: 0.6289\n", + "Epoch 57/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6661 - accuracy: 0.6289\n", + "Epoch 58/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6664 - accuracy: 0.6289\n", + "Epoch 59/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6658 - accuracy: 0.6289\n", + "Epoch 60/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6658 - accuracy: 0.6289\n", + "Epoch 61/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6656 - accuracy: 0.6289\n", + "Epoch 62/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6654 - accuracy: 0.6289\n", + "Epoch 63/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6657 - accuracy: 0.6289\n", + "Epoch 64/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6650 - accuracy: 0.6289\n", + "Epoch 65/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6650 - accuracy: 0.6289\n", + "Epoch 66/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6648 - accuracy: 0.6289\n", + "Epoch 67/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6645 - accuracy: 0.6289\n", + "Epoch 68/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.6647 - accuracy: 0.6289\n", + "Epoch 69/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6645 - accuracy: 0.6289\n", + "Epoch 70/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6643 - accuracy: 0.6289\n", + "Epoch 71/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6640 - accuracy: 0.6289\n", + "Epoch 72/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6640 - accuracy: 0.6289\n", + "Epoch 73/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6640 - accuracy: 0.6289\n", + "Epoch 74/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6641 - accuracy: 0.6289\n", + "Epoch 75/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6638 - accuracy: 0.6289\n", + "Epoch 76/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6640 - accuracy: 0.6289\n", + "Epoch 77/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6636 - accuracy: 0.6289\n", + "Epoch 78/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6636 - accuracy: 0.6289\n", + "Epoch 79/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6633 - accuracy: 0.6289\n", + "Epoch 80/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6632 - accuracy: 0.6289\n", + "Epoch 81/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6632 - accuracy: 0.6289\n", + "Epoch 82/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6635 - accuracy: 0.6289\n", + "Epoch 83/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6631 - accuracy: 0.6289\n", + "Epoch 84/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6630 - accuracy: 0.6289\n", + "Epoch 85/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6633 - accuracy: 0.6289\n", + "Epoch 86/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6631 - accuracy: 0.6289\n", + "Epoch 87/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6629 - accuracy: 0.6289\n", + "Epoch 88/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.6628 - accuracy: 0.6289\n", + "Epoch 89/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6629 - accuracy: 0.6289\n", + "Epoch 90/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6631 - accuracy: 0.6289\n", + "Epoch 91/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6626 - accuracy: 0.6289\n", + "Epoch 92/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6626 - accuracy: 0.6289\n", + "Epoch 93/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6626 - accuracy: 0.6289\n", + "Epoch 94/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6623 - accuracy: 0.6289\n", + "Epoch 95/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.6621 - accuracy: 0.6289\n", + "Epoch 96/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6624 - accuracy: 0.6289\n", + "Epoch 97/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6625 - accuracy: 0.6289\n", + "Epoch 98/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6620 - accuracy: 0.6289\n", + "Epoch 99/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6624 - accuracy: 0.6289\n", + "Epoch 100/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6624 - accuracy: 0.6289\n", + "16/16 [==============================] - 0s 10ms/step - loss: 0.6626 - accuracy: 0.6289\n", + "2/2 [==============================] - 0s 36ms/step - loss: 0.6717 - accuracy: 0.6140\n", + "Epoch 1/100\n", + "6/6 [==============================] - 1s 30ms/step - loss: 1.0963 - accuracy: 0.6328\n", + "Epoch 2/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 0.9552 - accuracy: 0.6387\n", + "Epoch 3/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 1.0940 - accuracy: 0.6172\n", + "Epoch 4/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.9469 - accuracy: 0.6406\n", + "Epoch 5/100\n", + "6/6 [==============================] - 0s 16ms/step - loss: 0.8946 - accuracy: 0.6387\n", + "Epoch 6/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.8158 - accuracy: 0.6426\n", + "Epoch 7/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7914 - accuracy: 0.6426\n", + "Epoch 8/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.8557 - accuracy: 0.6230\n", + "Epoch 9/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.7906 - accuracy: 0.6309\n", + "Epoch 10/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.8348 - accuracy: 0.5859\n", + "Epoch 11/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.8183 - accuracy: 0.6152\n", + "Epoch 12/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7809 - accuracy: 0.6367\n", + "Epoch 13/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7879 - accuracy: 0.6309\n", + "Epoch 14/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7831 - accuracy: 0.6113\n", + "Epoch 15/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.8431 - accuracy: 0.6035\n", + "Epoch 16/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.7971 - accuracy: 0.6367\n", + "Epoch 17/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.9388 - accuracy: 0.5547\n", + "Epoch 18/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.8255 - accuracy: 0.5879\n", + "Epoch 19/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.8646 - accuracy: 0.5566\n", + "Epoch 20/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.8169 - accuracy: 0.5996\n", + "Epoch 21/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.8573 - accuracy: 0.5762\n", + "Epoch 22/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7921 - accuracy: 0.6133\n", + "Epoch 23/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.8604 - accuracy: 0.6035\n", + "Epoch 24/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.9144 - accuracy: 0.5625\n", + "Epoch 25/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 1.0482 - accuracy: 0.5449\n", + "Epoch 26/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7858 - accuracy: 0.6035\n", + "Epoch 27/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6/6 [==============================] - 0s 11ms/step - loss: 0.8144 - accuracy: 0.6055\n", + "Epoch 28/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.8771 - accuracy: 0.5801\n", + "Epoch 29/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7965 - accuracy: 0.5879\n", + "Epoch 30/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.7743 - accuracy: 0.6113\n", + "Epoch 31/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7836 - accuracy: 0.6191\n", + "Epoch 32/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.9142 - accuracy: 0.5723\n", + "Epoch 33/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7744 - accuracy: 0.6211\n", + "Epoch 34/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7776 - accuracy: 0.6211\n", + "Epoch 35/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7950 - accuracy: 0.6309\n", + "Epoch 36/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7874 - accuracy: 0.5977\n", + "Epoch 37/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.8718 - accuracy: 0.5430\n", + "Epoch 38/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.8264 - accuracy: 0.5762\n", + "Epoch 39/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7859 - accuracy: 0.6152\n", + "Epoch 40/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.8115 - accuracy: 0.6230\n", + "Epoch 41/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7712 - accuracy: 0.6035\n", + "Epoch 42/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7836 - accuracy: 0.6094\n", + "Epoch 43/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7742 - accuracy: 0.6152\n", + "Epoch 44/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7820 - accuracy: 0.6426\n", + "Epoch 45/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7676 - accuracy: 0.6113\n", + "Epoch 46/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7722 - accuracy: 0.5957\n", + "Epoch 47/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7666 - accuracy: 0.5859\n", + "Epoch 48/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7696 - accuracy: 0.6172\n", + "Epoch 49/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7847 - accuracy: 0.5664\n", + "Epoch 50/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7823 - accuracy: 0.6074\n", + "Epoch 51/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7859 - accuracy: 0.6387\n", + "Epoch 52/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7768 - accuracy: 0.5859\n", + "Epoch 53/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7879 - accuracy: 0.5996\n", + "Epoch 54/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.8243 - accuracy: 0.5918\n", + "Epoch 55/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7688 - accuracy: 0.6406\n", + "Epoch 56/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.8635 - accuracy: 0.5508\n", + "Epoch 57/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7732 - accuracy: 0.5996\n", + "Epoch 58/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7671 - accuracy: 0.6309\n", + "Epoch 59/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.8018 - accuracy: 0.6035\n", + "Epoch 60/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7949 - accuracy: 0.5859\n", + "Epoch 61/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7926 - accuracy: 0.6250\n", + "Epoch 62/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7591 - accuracy: 0.6543\n", + "Epoch 63/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.8462 - accuracy: 0.5781\n", + "Epoch 64/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7648 - accuracy: 0.6445\n", + "Epoch 65/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7686 - accuracy: 0.6152\n", + "Epoch 66/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.8602 - accuracy: 0.5684\n", + "Epoch 67/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7619 - accuracy: 0.6445\n", + "Epoch 68/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.8019 - accuracy: 0.5977\n", + "Epoch 69/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7771 - accuracy: 0.6445\n", + "Epoch 70/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7549 - accuracy: 0.6348\n", + "Epoch 71/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7579 - accuracy: 0.6289\n", + "Epoch 72/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7574 - accuracy: 0.6094\n", + "Epoch 73/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7623 - accuracy: 0.6250\n", + "Epoch 74/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7850 - accuracy: 0.6016\n", + "Epoch 75/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7636 - accuracy: 0.6426\n", + "Epoch 76/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7895 - accuracy: 0.6172\n", + "Epoch 77/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7724 - accuracy: 0.6172\n", + "Epoch 78/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.8117 - accuracy: 0.6230\n", + "Epoch 79/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7583 - accuracy: 0.6621\n", + "Epoch 80/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7612 - accuracy: 0.6133\n", + "Epoch 81/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7736 - accuracy: 0.6250\n", + "Epoch 82/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7550 - accuracy: 0.6309\n", + "Epoch 83/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.8076 - accuracy: 0.5781\n", + "Epoch 84/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.8435 - accuracy: 0.5879\n", + "Epoch 85/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7891 - accuracy: 0.5801\n", + "Epoch 86/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7645 - accuracy: 0.6152\n", + "Epoch 87/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7533 - accuracy: 0.6562\n", + "Epoch 88/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7772 - accuracy: 0.5938\n", + "Epoch 89/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7591 - accuracy: 0.6289\n", + "Epoch 90/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7493 - accuracy: 0.6484\n", + "Epoch 91/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 0.7639 - accuracy: 0.6680\n", + "Epoch 92/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7734 - accuracy: 0.6230\n", + "Epoch 93/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7732 - accuracy: 0.6309\n", + "Epoch 94/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7607 - accuracy: 0.6660\n", + "Epoch 95/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7495 - accuracy: 0.6641\n", + "Epoch 96/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7629 - accuracy: 0.5996\n", + "Epoch 97/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7681 - accuracy: 0.6367\n", + "Epoch 98/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.7579 - accuracy: 0.6621\n", + "Epoch 99/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7476 - accuracy: 0.6504\n", + "Epoch 100/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7535 - accuracy: 0.6523\n", + "16/16 [==============================] - 0s 11ms/step - loss: 0.7505 - accuracy: 0.6094\n", + "2/2 [==============================] - 0s 48ms/step - loss: 0.8149 - accuracy: 0.5965\n", + "Epoch 1/100\n", + "6/6 [==============================] - 0s 24ms/step - loss: 5.7989 - accuracy: 0.4863\n", + "Epoch 2/100\n", + "6/6 [==============================] - 0s 15ms/step - loss: 0.9667 - accuracy: 0.5039\n", + "Epoch 3/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 0.8038 - accuracy: 0.5957\n", + "Epoch 4/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 0.7711 - accuracy: 0.6328\n", + "Epoch 5/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7591 - accuracy: 0.6562\n", + "Epoch 6/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7550 - accuracy: 0.6328\n", + "Epoch 7/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.8062 - accuracy: 0.6133\n", + "Epoch 8/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6/6 [==============================] - 0s 11ms/step - loss: 0.7670 - accuracy: 0.6191\n", + "Epoch 9/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7727 - accuracy: 0.6523\n", + "Epoch 10/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.8023 - accuracy: 0.6387\n", + "Epoch 11/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7880 - accuracy: 0.6484\n", + "Epoch 12/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7521 - accuracy: 0.6680\n", + "Epoch 13/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7489 - accuracy: 0.6348\n", + "Epoch 14/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7764 - accuracy: 0.6230\n", + "Epoch 15/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7813 - accuracy: 0.6191\n", + "Epoch 16/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7474 - accuracy: 0.6582\n", + "Epoch 17/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7619 - accuracy: 0.6562\n", + "Epoch 18/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7572 - accuracy: 0.6133\n", + "Epoch 19/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7640 - accuracy: 0.6230\n", + "Epoch 20/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7429 - accuracy: 0.6719\n", + "Epoch 21/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7350 - accuracy: 0.6680\n", + "Epoch 22/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7275 - accuracy: 0.6953\n", + "Epoch 23/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7360 - accuracy: 0.6719\n", + "Epoch 24/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7326 - accuracy: 0.6680\n", + "Epoch 25/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7310 - accuracy: 0.6680\n", + "Epoch 26/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7451 - accuracy: 0.6387\n", + "Epoch 27/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7613 - accuracy: 0.6406\n", + "Epoch 28/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7331 - accuracy: 0.6582\n", + "Epoch 29/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7345 - accuracy: 0.6504\n", + "Epoch 30/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7600 - accuracy: 0.6445\n", + "Epoch 31/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7513 - accuracy: 0.6328\n", + "Epoch 32/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7274 - accuracy: 0.6484\n", + "Epoch 33/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7442 - accuracy: 0.6562\n", + "Epoch 34/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7295 - accuracy: 0.6562\n", + "Epoch 35/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.7718 - accuracy: 0.6211\n", + "Epoch 36/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7344 - accuracy: 0.6250\n", + "Epoch 37/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.7230 - accuracy: 0.6914\n", + "Epoch 38/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.7226 - accuracy: 0.6758\n", + "Epoch 39/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.7329 - accuracy: 0.6777\n", + "Epoch 40/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.7456 - accuracy: 0.6680\n", + "Epoch 41/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7274 - accuracy: 0.6562\n", + "Epoch 42/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 0.7257 - accuracy: 0.6855\n", + "Epoch 43/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7179 - accuracy: 0.6562\n", + "Epoch 44/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7298 - accuracy: 0.6719\n", + "Epoch 45/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7199 - accuracy: 0.6738\n", + "Epoch 46/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7533 - accuracy: 0.6270\n", + "Epoch 47/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7264 - accuracy: 0.7109\n", + "Epoch 48/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7245 - accuracy: 0.7090\n", + "Epoch 49/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7227 - accuracy: 0.6406\n", + "Epoch 50/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7229 - accuracy: 0.6621\n", + "Epoch 51/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7143 - accuracy: 0.6699\n", + "Epoch 52/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7201 - accuracy: 0.6758\n", + "Epoch 53/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7397 - accuracy: 0.6934\n", + "Epoch 54/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7179 - accuracy: 0.7266\n", + "Epoch 55/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7160 - accuracy: 0.7227\n", + "Epoch 56/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7120 - accuracy: 0.6543\n", + "Epoch 57/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7093 - accuracy: 0.7441\n", + "Epoch 58/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7190 - accuracy: 0.6582\n", + "Epoch 59/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7051 - accuracy: 0.6797\n", + "Epoch 60/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7216 - accuracy: 0.6797\n", + "Epoch 61/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7149 - accuracy: 0.7129\n", + "Epoch 62/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7057 - accuracy: 0.7363\n", + "Epoch 63/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7043 - accuracy: 0.7363\n", + "Epoch 64/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.6987 - accuracy: 0.7090\n", + "Epoch 65/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7021 - accuracy: 0.6738\n", + "Epoch 66/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7086 - accuracy: 0.6973\n", + "Epoch 67/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7003 - accuracy: 0.6895\n", + "Epoch 68/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7008 - accuracy: 0.6934\n", + "Epoch 69/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6998 - accuracy: 0.6699\n", + "Epoch 70/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6995 - accuracy: 0.6875\n", + "Epoch 71/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7034 - accuracy: 0.6914\n", + "Epoch 72/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7015 - accuracy: 0.7227\n", + "Epoch 73/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6997 - accuracy: 0.6660\n", + "Epoch 74/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6917 - accuracy: 0.6855\n", + "Epoch 75/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6914 - accuracy: 0.6973\n", + "Epoch 76/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.7060 - accuracy: 0.6738\n", + "Epoch 77/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.7029 - accuracy: 0.6758\n", + "Epoch 78/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6903 - accuracy: 0.7383\n", + "Epoch 79/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.6884 - accuracy: 0.7051\n", + "Epoch 80/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6979 - accuracy: 0.7109\n", + "Epoch 81/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6926 - accuracy: 0.7344\n", + "Epoch 82/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6914 - accuracy: 0.6992\n", + "Epoch 83/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6951 - accuracy: 0.6875\n", + "Epoch 84/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6920 - accuracy: 0.7344\n", + "Epoch 85/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6880 - accuracy: 0.7148\n", + "Epoch 86/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6837 - accuracy: 0.7109\n", + "Epoch 87/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6925 - accuracy: 0.7188\n", + "Epoch 88/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6844 - accuracy: 0.7266\n", + "Epoch 89/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.6892 - accuracy: 0.7461\n", + "Epoch 90/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6/6 [==============================] - 0s 12ms/step - loss: 0.6856 - accuracy: 0.7422\n", + "Epoch 91/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6840 - accuracy: 0.7305\n", + "Epoch 92/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6905 - accuracy: 0.7441\n", + "Epoch 93/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 0.6835 - accuracy: 0.7461\n", + "Epoch 94/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6795 - accuracy: 0.7090\n", + "Epoch 95/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6886 - accuracy: 0.7324\n", + "Epoch 96/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6900 - accuracy: 0.6934\n", + "Epoch 97/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6846 - accuracy: 0.7402\n", + "Epoch 98/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 0.6766 - accuracy: 0.7480\n", + "Epoch 99/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 0.6894 - accuracy: 0.7207\n", + "Epoch 100/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 0.6769 - accuracy: 0.7363\n", + "16/16 [==============================] - 0s 11ms/step - loss: 0.6738 - accuracy: 0.7695\n", + "2/2 [==============================] - 0s 41ms/step - loss: 0.7160 - accuracy: 0.7719\n", + "Epoch 1/100\n", + "6/6 [==============================] - 0s 21ms/step - loss: 1253.1278 - accuracy: 0.4941\n", + "Epoch 2/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 346.2668 - accuracy: 0.6289\n", + "Epoch 3/100\n", + "6/6 [==============================] - 0s 14ms/step - loss: 338.0537 - accuracy: 0.6289\n", + "Epoch 4/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 330.0428 - accuracy: 0.6289\n", + "Epoch 5/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 322.2213 - accuracy: 0.6289\n", + "Epoch 6/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 314.5881 - accuracy: 0.6289\n", + "Epoch 7/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 307.1357 - accuracy: 0.6289\n", + "Epoch 8/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 299.8600 - accuracy: 0.6289\n", + "Epoch 9/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 292.7588 - accuracy: 0.6289\n", + "Epoch 10/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 285.8242 - accuracy: 0.6289\n", + "Epoch 11/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 279.0542 - accuracy: 0.6289\n", + "Epoch 12/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 272.4458 - accuracy: 0.6289\n", + "Epoch 13/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 265.9942 - accuracy: 0.6289\n", + "Epoch 14/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 259.6956 - accuracy: 0.6289\n", + "Epoch 15/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 253.5466 - accuracy: 0.6289\n", + "Epoch 16/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 247.5437 - accuracy: 0.6289\n", + "Epoch 17/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 241.6834 - accuracy: 0.6289\n", + "Epoch 18/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 235.9619 - accuracy: 0.6289\n", + "Epoch 19/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 230.3763 - accuracy: 0.6289\n", + "Epoch 20/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 224.9233 - accuracy: 0.6289\n", + "Epoch 21/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 219.5999 - accuracy: 0.6289\n", + "Epoch 22/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 214.4028 - accuracy: 0.6289\n", + "Epoch 23/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 209.3291 - accuracy: 0.6289\n", + "Epoch 24/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 204.3756 - accuracy: 0.6289\n", + "Epoch 25/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 199.5397 - accuracy: 0.6289\n", + "Epoch 26/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 194.8188 - accuracy: 0.6289\n", + "Epoch 27/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 190.2102 - accuracy: 0.6289\n", + "Epoch 28/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 185.7104 - accuracy: 0.6289\n", + "Epoch 29/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 181.3179 - accuracy: 0.6289\n", + "Epoch 30/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 177.0292 - accuracy: 0.6289\n", + "Epoch 31/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 172.8425 - accuracy: 0.6289\n", + "Epoch 32/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 168.7555 - accuracy: 0.6289\n", + "Epoch 33/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 164.7656 - accuracy: 0.6289\n", + "Epoch 34/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 160.8699 - accuracy: 0.6289\n", + "Epoch 35/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 157.0670 - accuracy: 0.6289\n", + "Epoch 36/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 153.3540 - accuracy: 0.6289\n", + "Epoch 37/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 149.7297 - accuracy: 0.6289\n", + "Epoch 38/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 146.1913 - accuracy: 0.6289\n", + "Epoch 39/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 142.7363 - accuracy: 0.6289\n", + "Epoch 40/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 139.3637 - accuracy: 0.6289\n", + "Epoch 41/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 136.0710 - accuracy: 0.6289\n", + "Epoch 42/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 132.8567 - accuracy: 0.6289\n", + "Epoch 43/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 129.7186 - accuracy: 0.6289\n", + "Epoch 44/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 126.6553 - accuracy: 0.6289\n", + "Epoch 45/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 123.6648 - accuracy: 0.6289\n", + "Epoch 46/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 120.7449 - accuracy: 0.6289\n", + "Epoch 47/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 117.8944 - accuracy: 0.6289\n", + "Epoch 48/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 115.1112 - accuracy: 0.6289\n", + "Epoch 49/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 112.3945 - accuracy: 0.6289\n", + "Epoch 50/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 109.7418 - accuracy: 0.6289\n", + "Epoch 51/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 107.1529 - accuracy: 0.6289\n", + "Epoch 52/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 104.6247 - accuracy: 0.6289\n", + "Epoch 53/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 102.1567 - accuracy: 0.6289\n", + "Epoch 54/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 99.7478 - accuracy: 0.6289\n", + "Epoch 55/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 97.3957 - accuracy: 0.6289\n", + "Epoch 56/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 95.0993 - accuracy: 0.6289\n", + "Epoch 57/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 92.8573 - accuracy: 0.6289\n", + "Epoch 58/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 90.6689 - accuracy: 0.6289\n", + "Epoch 59/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 88.5326 - accuracy: 0.6289\n", + "Epoch 60/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 86.4466 - accuracy: 0.6289\n", + "Epoch 61/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 84.4098 - accuracy: 0.6289\n", + "Epoch 62/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 82.4217 - accuracy: 0.6289\n", + "Epoch 63/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 80.4808 - accuracy: 0.6289\n", + "Epoch 64/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 78.5862 - accuracy: 0.6289\n", + "Epoch 65/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 76.7365 - accuracy: 0.6289\n", + "Epoch 66/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 74.9307 - accuracy: 0.6289\n", + "Epoch 67/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 73.1675 - accuracy: 0.6289\n", + "Epoch 68/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 71.4463 - accuracy: 0.6289\n", + "Epoch 69/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 69.7660 - accuracy: 0.6289\n", + "Epoch 70/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6/6 [==============================] - 0s 9ms/step - loss: 68.1258 - accuracy: 0.6289\n", + "Epoch 71/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 66.5240 - accuracy: 0.6289\n", + "Epoch 72/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 64.9607 - accuracy: 0.6289\n", + "Epoch 73/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 63.4343 - accuracy: 0.6289\n", + "Epoch 74/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 61.9441 - accuracy: 0.6289\n", + "Epoch 75/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 60.4895 - accuracy: 0.6289\n", + "Epoch 76/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 59.0696 - accuracy: 0.6289\n", + "Epoch 77/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 57.6830 - accuracy: 0.6289\n", + "Epoch 78/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 56.3292 - accuracy: 0.6289\n", + "Epoch 79/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 55.0078 - accuracy: 0.6289\n", + "Epoch 80/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 53.7176 - accuracy: 0.6289\n", + "Epoch 81/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 52.4579 - accuracy: 0.6289\n", + "Epoch 82/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 51.2283 - accuracy: 0.6289\n", + "Epoch 83/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 50.0282 - accuracy: 0.6289\n", + "Epoch 84/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 48.8562 - accuracy: 0.6289\n", + "Epoch 85/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 47.7127 - accuracy: 0.6289\n", + "Epoch 86/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 46.5952 - accuracy: 0.6289\n", + "Epoch 87/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 45.5050 - accuracy: 0.6289\n", + "Epoch 88/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 44.4403 - accuracy: 0.6289\n", + "Epoch 89/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 43.4010 - accuracy: 0.6289\n", + "Epoch 90/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 42.3869 - accuracy: 0.6289\n", + "Epoch 91/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 41.3965 - accuracy: 0.6289\n", + "Epoch 92/100\n", + "6/6 [==============================] - 0s 14ms/step - loss: 40.4293 - accuracy: 0.6289\n", + "Epoch 93/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 39.4849 - accuracy: 0.6289\n", + "Epoch 94/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 38.5633 - accuracy: 0.6289\n", + "Epoch 95/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 37.6637 - accuracy: 0.6289\n", + "Epoch 96/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 36.7850 - accuracy: 0.6289\n", + "Epoch 97/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 35.9278 - accuracy: 0.6289\n", + "Epoch 98/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 35.0906 - accuracy: 0.6289\n", + "Epoch 99/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 34.2732 - accuracy: 0.6289\n", + "Epoch 100/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 33.4757 - accuracy: 0.6289\n", + "16/16 [==============================] - 0s 8ms/step - loss: 32.9622 - accuracy: 0.6289\n", + "2/2 [==============================] - 0s 34ms/step - loss: 32.9701 - accuracy: 0.6140\n", + "Epoch 1/100\n", + "6/6 [==============================] - 0s 26ms/step - loss: 3.6565 - accuracy: 0.5195\n", + "Epoch 2/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 3.1900 - accuracy: 0.5176\n", + "Epoch 3/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 2.9806 - accuracy: 0.5547\n", + "Epoch 4/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 2.8656 - accuracy: 0.5391\n", + "Epoch 5/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 2.9776 - accuracy: 0.5312\n", + "Epoch 6/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 2.8417 - accuracy: 0.5293\n", + "Epoch 7/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 2.8734 - accuracy: 0.5371\n", + "Epoch 8/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 2.8355 - accuracy: 0.5020\n", + "Epoch 9/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 2.7189 - accuracy: 0.5098\n", + "Epoch 10/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 2.5583 - accuracy: 0.5527\n", + "Epoch 11/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 2.1461 - accuracy: 0.5762\n", + "Epoch 12/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 2.1961 - accuracy: 0.5840\n", + "Epoch 13/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 2.3601 - accuracy: 0.5410\n", + "Epoch 14/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 2.2982 - accuracy: 0.5469\n", + "Epoch 15/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.9622 - accuracy: 0.5371\n", + "Epoch 16/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 2.2012 - accuracy: 0.5117\n", + "Epoch 17/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 2.1302 - accuracy: 0.5430\n", + "Epoch 18/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 2.2197 - accuracy: 0.5312\n", + "Epoch 19/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 1.6827 - accuracy: 0.6777\n", + "Epoch 20/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.7034 - accuracy: 0.6426\n", + "Epoch 21/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 1.9680 - accuracy: 0.5430\n", + "Epoch 22/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6988 - accuracy: 0.6309\n", + "Epoch 23/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.7547 - accuracy: 0.6328\n", + "Epoch 24/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 1.8833 - accuracy: 0.6113\n", + "Epoch 25/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 1.9347 - accuracy: 0.5684\n", + "Epoch 26/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.7131 - accuracy: 0.6035\n", + "Epoch 27/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 2.0259 - accuracy: 0.5469\n", + "Epoch 28/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6808 - accuracy: 0.6445\n", + "Epoch 29/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6747 - accuracy: 0.6602\n", + "Epoch 30/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 1.8893 - accuracy: 0.5410\n", + "Epoch 31/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 1.7332 - accuracy: 0.6230\n", + "Epoch 32/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 1.7638 - accuracy: 0.6113\n", + "Epoch 33/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.8527 - accuracy: 0.5977\n", + "Epoch 34/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.8895 - accuracy: 0.5820\n", + "Epoch 35/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.7927 - accuracy: 0.6328\n", + "Epoch 36/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.7594 - accuracy: 0.6602\n", + "Epoch 37/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.9532 - accuracy: 0.5352\n", + "Epoch 38/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.7756 - accuracy: 0.6328\n", + "Epoch 39/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.8510 - accuracy: 0.5586\n", + "Epoch 40/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.8933 - accuracy: 0.5703\n", + "Epoch 41/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.7105 - accuracy: 0.6191\n", + "Epoch 42/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.7506 - accuracy: 0.6016\n", + "Epoch 43/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.8728 - accuracy: 0.6094\n", + "Epoch 44/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.7400 - accuracy: 0.6445\n", + "Epoch 45/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6863 - accuracy: 0.6602\n", + "Epoch 46/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.8019 - accuracy: 0.5703\n", + "Epoch 47/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.7251 - accuracy: 0.6270\n", + "Epoch 48/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.7758 - accuracy: 0.5723\n", + "Epoch 49/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.7378 - accuracy: 0.6309\n", + "Epoch 50/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.9021 - accuracy: 0.5840\n", + "Epoch 51/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6/6 [==============================] - 0s 9ms/step - loss: 1.7031 - accuracy: 0.6387\n", + "Epoch 52/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.7034 - accuracy: 0.6348\n", + "Epoch 53/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.8735 - accuracy: 0.5488\n", + "Epoch 54/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.7462 - accuracy: 0.5938\n", + "Epoch 55/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6724 - accuracy: 0.6484\n", + "Epoch 56/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.7151 - accuracy: 0.6152\n", + "Epoch 57/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6794 - accuracy: 0.6504\n", + "Epoch 58/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6763 - accuracy: 0.6426\n", + "Epoch 59/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.7084 - accuracy: 0.6074\n", + "Epoch 60/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.7094 - accuracy: 0.6621\n", + "Epoch 61/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6752 - accuracy: 0.6777\n", + "Epoch 62/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6763 - accuracy: 0.6406\n", + "Epoch 63/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6986 - accuracy: 0.6426\n", + "Epoch 64/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6832 - accuracy: 0.6523\n", + "Epoch 65/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.7444 - accuracy: 0.5957\n", + "Epoch 66/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6611 - accuracy: 0.6797\n", + "Epoch 67/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.7071 - accuracy: 0.6289\n", + "Epoch 68/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 1.6853 - accuracy: 0.6387\n", + "Epoch 69/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.8469 - accuracy: 0.5547\n", + "Epoch 70/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6731 - accuracy: 0.6562\n", + "Epoch 71/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6750 - accuracy: 0.6504\n", + "Epoch 72/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6740 - accuracy: 0.6523\n", + "Epoch 73/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.7329 - accuracy: 0.5781\n", + "Epoch 74/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6835 - accuracy: 0.6406\n", + "Epoch 75/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 1.6662 - accuracy: 0.6504\n", + "Epoch 76/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6817 - accuracy: 0.6543\n", + "Epoch 77/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.7084 - accuracy: 0.6289\n", + "Epoch 78/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.7059 - accuracy: 0.6523\n", + "Epoch 79/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.8092 - accuracy: 0.5605\n", + "Epoch 80/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6710 - accuracy: 0.6465\n", + "Epoch 81/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6609 - accuracy: 0.6836\n", + "Epoch 82/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6667 - accuracy: 0.6230\n", + "Epoch 83/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.7515 - accuracy: 0.5996\n", + "Epoch 84/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6702 - accuracy: 0.6465\n", + "Epoch 85/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6914 - accuracy: 0.6152\n", + "Epoch 86/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 1.6890 - accuracy: 0.6621\n", + "Epoch 87/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 1.7383 - accuracy: 0.5918\n", + "Epoch 88/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6989 - accuracy: 0.6289\n", + "Epoch 89/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.7135 - accuracy: 0.6172\n", + "Epoch 90/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6486 - accuracy: 0.6660\n", + "Epoch 91/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.7465 - accuracy: 0.6094\n", + "Epoch 92/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6923 - accuracy: 0.6270\n", + "Epoch 93/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.7185 - accuracy: 0.6328\n", + "Epoch 94/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6715 - accuracy: 0.6348\n", + "Epoch 95/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6498 - accuracy: 0.6836\n", + "Epoch 96/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6935 - accuracy: 0.6406\n", + "Epoch 97/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6918 - accuracy: 0.6094\n", + "Epoch 98/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6608 - accuracy: 0.6602\n", + "Epoch 99/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6815 - accuracy: 0.6406\n", + "Epoch 100/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 1.8035 - accuracy: 0.5801\n", + "16/16 [==============================] - 0s 8ms/step - loss: 1.7570 - accuracy: 0.6367\n", + "2/2 [==============================] - 0s 37ms/step - loss: 1.8262 - accuracy: 0.6491\n", + "Epoch 1/100\n", + "6/6 [==============================] - 0s 19ms/step - loss: 13.0807 - accuracy: 0.5000\n", + "Epoch 2/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 1.9594 - accuracy: 0.5117\n", + "Epoch 3/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 1.7759 - accuracy: 0.5469\n", + "Epoch 4/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 1.7062 - accuracy: 0.6289\n", + "Epoch 5/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 1.7395 - accuracy: 0.5430\n", + "Epoch 6/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 1.6964 - accuracy: 0.6230\n", + "Epoch 7/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 1.6863 - accuracy: 0.6309\n", + "Epoch 8/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 1.7085 - accuracy: 0.5977\n", + "Epoch 9/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 1.6827 - accuracy: 0.6387\n", + "Epoch 10/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 1.7609 - accuracy: 0.6348\n", + "Epoch 11/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 1.7228 - accuracy: 0.6465\n", + "Epoch 12/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 1.6650 - accuracy: 0.6543\n", + "Epoch 13/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 1.6948 - accuracy: 0.5918\n", + "Epoch 14/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 1.6861 - accuracy: 0.6211\n", + "Epoch 15/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6571 - accuracy: 0.6367\n", + "Epoch 16/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 1.6341 - accuracy: 0.6719\n", + "Epoch 17/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6947 - accuracy: 0.5781\n", + "Epoch 18/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6382 - accuracy: 0.6562\n", + "Epoch 19/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6234 - accuracy: 0.6855\n", + "Epoch 20/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6451 - accuracy: 0.6113\n", + "Epoch 21/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.7047 - accuracy: 0.6641\n", + "Epoch 22/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6207 - accuracy: 0.6504\n", + "Epoch 23/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6141 - accuracy: 0.6758\n", + "Epoch 24/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6126 - accuracy: 0.6699\n", + "Epoch 25/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6507 - accuracy: 0.6211\n", + "Epoch 26/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6032 - accuracy: 0.6699\n", + "Epoch 27/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 1.5980 - accuracy: 0.6875\n", + "Epoch 28/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6514 - accuracy: 0.6387\n", + "Epoch 29/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6102 - accuracy: 0.6367\n", + "Epoch 30/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6227 - accuracy: 0.6562\n", + "Epoch 31/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6017 - accuracy: 0.6523\n", + "Epoch 32/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6/6 [==============================] - 0s 9ms/step - loss: 1.5853 - accuracy: 0.6914\n", + "Epoch 33/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.5842 - accuracy: 0.6914\n", + "Epoch 34/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.5803 - accuracy: 0.6738\n", + "Epoch 35/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.6309 - accuracy: 0.6934\n", + "Epoch 36/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.5750 - accuracy: 0.7051\n", + "Epoch 37/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.5810 - accuracy: 0.6738\n", + "Epoch 38/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.5951 - accuracy: 0.6777\n", + "Epoch 39/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.5803 - accuracy: 0.6836\n", + "Epoch 40/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.5626 - accuracy: 0.6680\n", + "Epoch 41/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.5663 - accuracy: 0.6719\n", + "Epoch 42/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.5642 - accuracy: 0.6875\n", + "Epoch 43/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.5549 - accuracy: 0.6953\n", + "Epoch 44/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.5928 - accuracy: 0.6602\n", + "Epoch 45/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.5487 - accuracy: 0.6777\n", + "Epoch 46/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.5472 - accuracy: 0.6855\n", + "Epoch 47/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.5437 - accuracy: 0.6992\n", + "Epoch 48/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 1.5568 - accuracy: 0.6875\n", + "Epoch 49/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.5361 - accuracy: 0.6973\n", + "Epoch 50/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.5453 - accuracy: 0.6777\n", + "Epoch 51/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.5382 - accuracy: 0.6953\n", + "Epoch 52/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.5323 - accuracy: 0.6914\n", + "Epoch 53/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.5257 - accuracy: 0.7109\n", + "Epoch 54/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.5518 - accuracy: 0.6543\n", + "Epoch 55/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.5612 - accuracy: 0.6562\n", + "Epoch 56/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.5293 - accuracy: 0.7207\n", + "Epoch 57/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.5243 - accuracy: 0.6895\n", + "Epoch 58/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.5287 - accuracy: 0.7129\n", + "Epoch 59/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.5226 - accuracy: 0.6758\n", + "Epoch 60/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.5084 - accuracy: 0.7129\n", + "Epoch 61/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.5049 - accuracy: 0.7207\n", + "Epoch 62/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.5069 - accuracy: 0.7051\n", + "Epoch 63/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.4954 - accuracy: 0.6855\n", + "Epoch 64/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.4957 - accuracy: 0.6875\n", + "Epoch 65/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 1.5069 - accuracy: 0.7148\n", + "Epoch 66/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.4857 - accuracy: 0.7109\n", + "Epoch 67/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.4881 - accuracy: 0.6953\n", + "Epoch 68/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.4995 - accuracy: 0.6816\n", + "Epoch 69/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.5281 - accuracy: 0.6621\n", + "Epoch 70/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.4780 - accuracy: 0.7109\n", + "Epoch 71/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.4852 - accuracy: 0.6875\n", + "Epoch 72/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.4755 - accuracy: 0.7129\n", + "Epoch 73/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 1.4706 - accuracy: 0.6875\n", + "Epoch 74/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.4639 - accuracy: 0.7227\n", + "Epoch 75/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 1.4633 - accuracy: 0.7246\n", + "Epoch 76/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.4651 - accuracy: 0.7148\n", + "Epoch 77/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.4746 - accuracy: 0.6973\n", + "Epoch 78/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.4549 - accuracy: 0.6934\n", + "Epoch 79/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.4523 - accuracy: 0.7070\n", + "Epoch 80/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.4625 - accuracy: 0.6895\n", + "Epoch 81/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.4586 - accuracy: 0.6797\n", + "Epoch 82/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.4502 - accuracy: 0.7188\n", + "Epoch 83/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.4408 - accuracy: 0.7188\n", + "Epoch 84/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.4532 - accuracy: 0.6914\n", + "Epoch 85/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.4399 - accuracy: 0.6992\n", + "Epoch 86/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.4352 - accuracy: 0.7109\n", + "Epoch 87/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.4359 - accuracy: 0.6992\n", + "Epoch 88/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 1.4377 - accuracy: 0.7070\n", + "Epoch 89/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.4310 - accuracy: 0.7168\n", + "Epoch 90/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.4244 - accuracy: 0.7148\n", + "Epoch 91/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 1.4364 - accuracy: 0.7266\n", + "Epoch 92/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.4233 - accuracy: 0.7344\n", + "Epoch 93/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.4464 - accuracy: 0.7227\n", + "Epoch 94/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.4190 - accuracy: 0.6953\n", + "Epoch 95/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.4233 - accuracy: 0.7305\n", + "Epoch 96/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.4144 - accuracy: 0.7402\n", + "Epoch 97/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.4172 - accuracy: 0.6973\n", + "Epoch 98/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.4086 - accuracy: 0.7227\n", + "Epoch 99/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 1.4059 - accuracy: 0.7246\n", + "Epoch 100/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 1.4227 - accuracy: 0.7188\n", + "16/16 [==============================] - 0s 9ms/step - loss: 1.4548 - accuracy: 0.5566\n", + "2/2 [==============================] - 0s 32ms/step - loss: 1.4688 - accuracy: 0.5263\n", + "Epoch 1/100\n", + "6/6 [==============================] - 0s 19ms/step - loss: 5010.8311 - accuracy: 0.4883\n", + "Epoch 2/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 20585.4336 - accuracy: 0.6289\n", + "Epoch 3/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 20096.7871 - accuracy: 0.6289\n", + "Epoch 4/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 19619.7441 - accuracy: 0.6289\n", + "Epoch 5/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 19154.0254 - accuracy: 0.6289\n", + "Epoch 6/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 18699.3672 - accuracy: 0.6289\n", + "Epoch 7/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 18255.5020 - accuracy: 0.6289\n", + "Epoch 8/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 17822.1699 - accuracy: 0.6289\n", + "Epoch 9/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 17399.1250 - accuracy: 0.6289\n", + "Epoch 10/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 16986.1211 - accuracy: 0.6289\n", + "Epoch 11/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 16582.9258 - accuracy: 0.6289\n", + "Epoch 12/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 16189.2998 - accuracy: 0.6289\n", + "Epoch 13/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6/6 [==============================] - 0s 9ms/step - loss: 15805.0195 - accuracy: 0.6289\n", + "Epoch 14/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 15429.8604 - accuracy: 0.6289\n", + "Epoch 15/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 15063.6074 - accuracy: 0.6289\n", + "Epoch 16/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 14706.0488 - accuracy: 0.6289\n", + "Epoch 17/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 14356.9775 - accuracy: 0.6289\n", + "Epoch 18/100\n", + "6/6 [==============================] - 0s 10ms/step - loss: 14016.1904 - accuracy: 0.6289\n", + "Epoch 19/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 13683.4922 - accuracy: 0.6289\n", + "Epoch 20/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 13358.6924 - accuracy: 0.6289\n", + "Epoch 21/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 13041.6025 - accuracy: 0.6289\n", + "Epoch 22/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 12732.0410 - accuracy: 0.6289\n", + "Epoch 23/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 12429.8281 - accuracy: 0.6289\n", + "Epoch 24/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 12134.7881 - accuracy: 0.6289\n", + "Epoch 25/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 11846.7510 - accuracy: 0.6289\n", + "Epoch 26/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 11565.5508 - accuracy: 0.6289\n", + "Epoch 27/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 11291.0264 - accuracy: 0.6289\n", + "Epoch 28/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 11023.0186 - accuracy: 0.6289\n", + "Epoch 29/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 10761.3730 - accuracy: 0.6289\n", + "Epoch 30/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 10505.9385 - accuracy: 0.6289\n", + "Epoch 31/100\n", + "6/6 [==============================] - 0s 9ms/step - loss: 10256.5684 - accuracy: 0.6289\n", + "Epoch 32/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10013.1162 - accuracy: 0.6289\n", + "Epoch 33/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 9775.4434 - accuracy: 0.6289\n", + "Epoch 34/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 9543.4141 - accuracy: 0.6289\n", + "Epoch 35/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 9316.8906 - accuracy: 0.6289\n", + "Epoch 36/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 9095.7441 - accuracy: 0.6289\n", + "Epoch 37/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 8879.8477 - accuracy: 0.6289\n", + "Epoch 38/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 8669.0762 - accuracy: 0.6289\n", + "Epoch 39/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 8463.3076 - accuracy: 0.6289\n", + "Epoch 40/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 8262.4238 - accuracy: 0.6289\n", + "Epoch 41/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 8066.3081 - accuracy: 0.6289\n", + "Epoch 42/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 7874.8486 - accuracy: 0.6289\n", + "Epoch 43/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 7687.9331 - accuracy: 0.6289\n", + "Epoch 44/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 7505.4546 - accuracy: 0.6289\n", + "Epoch 45/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 7327.3071 - accuracy: 0.6289\n", + "Epoch 46/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 7153.3896 - accuracy: 0.6289\n", + "Epoch 47/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 6983.5996 - accuracy: 0.6289\n", + "Epoch 48/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 6817.8398 - accuracy: 0.6289\n", + "Epoch 49/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 6656.0161 - accuracy: 0.6289\n", + "Epoch 50/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 6498.0322 - accuracy: 0.6289\n", + "Epoch 51/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 6343.7998 - accuracy: 0.6289\n", + "Epoch 52/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 6193.2271 - accuracy: 0.6289\n", + "Epoch 53/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 6046.2305 - accuracy: 0.6289\n", + "Epoch 54/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 5902.7222 - accuracy: 0.6289\n", + "Epoch 55/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 5762.6206 - accuracy: 0.6289\n", + "Epoch 56/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 5625.8447 - accuracy: 0.6289\n", + "Epoch 57/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 5492.3154 - accuracy: 0.6289\n", + "Epoch 58/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 5361.9561 - accuracy: 0.6289\n", + "Epoch 59/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 5234.6914 - accuracy: 0.6289\n", + "Epoch 60/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 5110.4478 - accuracy: 0.6289\n", + "Epoch 61/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 4989.1523 - accuracy: 0.6289\n", + "Epoch 62/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 4870.7358 - accuracy: 0.6289\n", + "Epoch 63/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 4755.1313 - accuracy: 0.6289\n", + "Epoch 64/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 4642.2710 - accuracy: 0.6289\n", + "Epoch 65/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 4532.0898 - accuracy: 0.6289\n", + "Epoch 66/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 4424.5239 - accuracy: 0.6289\n", + "Epoch 67/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 4319.5112 - accuracy: 0.6289\n", + "Epoch 68/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 4216.9917 - accuracy: 0.6289\n", + "Epoch 69/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 4116.9048 - accuracy: 0.6289\n", + "Epoch 70/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 4019.1936 - accuracy: 0.6289\n", + "Epoch 71/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 3923.8030 - accuracy: 0.6289\n", + "Epoch 72/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 3830.6770 - accuracy: 0.6289\n", + "Epoch 73/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 3739.7615 - accuracy: 0.6289\n", + "Epoch 74/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 3651.0037 - accuracy: 0.6289\n", + "Epoch 75/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 3564.3521 - accuracy: 0.6289\n", + "Epoch 76/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 3479.7573 - accuracy: 0.6289\n", + "Epoch 77/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 3397.1719 - accuracy: 0.6289\n", + "Epoch 78/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 3316.5464 - accuracy: 0.6289\n", + "Epoch 79/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 3237.8350 - accuracy: 0.6289\n", + "Epoch 80/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 3160.9915 - accuracy: 0.6289\n", + "Epoch 81/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 3085.9719 - accuracy: 0.6289\n", + "Epoch 82/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 3012.7339 - accuracy: 0.6289\n", + "Epoch 83/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 2941.2341 - accuracy: 0.6289\n", + "Epoch 84/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 2871.4314 - accuracy: 0.6289\n", + "Epoch 85/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 2803.2856 - accuracy: 0.6289\n", + "Epoch 86/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 2736.7576 - accuracy: 0.6289\n", + "Epoch 87/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 2671.8091 - accuracy: 0.6289\n", + "Epoch 88/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 2608.4023 - accuracy: 0.6289\n", + "Epoch 89/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 2546.5000 - accuracy: 0.6289\n", + "Epoch 90/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 2486.0676 - accuracy: 0.6289\n", + "Epoch 91/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 2427.0693 - accuracy: 0.6289\n", + "Epoch 92/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 2369.4709 - accuracy: 0.6289\n", + "Epoch 93/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6/6 [==============================] - 0s 11ms/step - loss: 2313.2407 - accuracy: 0.6289\n", + "Epoch 94/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 2258.3457 - accuracy: 0.6289\n", + "Epoch 95/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 2204.7532 - accuracy: 0.6289\n", + "Epoch 96/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 2152.4329 - accuracy: 0.6289\n", + "Epoch 97/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 2101.3547 - accuracy: 0.6289\n", + "Epoch 98/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 2051.4895 - accuracy: 0.6289\n", + "Epoch 99/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 2002.8074 - accuracy: 0.6289\n", + "Epoch 100/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 1955.2803 - accuracy: 0.6289\n", + "16/16 [==============================] - 0s 11ms/step - loss: 1924.7323 - accuracy: 0.6289\n", + "2/2 [==============================] - 0s 36ms/step - loss: 1924.7401 - accuracy: 0.6140\n", + "Epoch 1/100\n", + "6/6 [==============================] - 1s 27ms/step - loss: 13.4402 - accuracy: 0.5508\n", + "Epoch 2/100\n", + "6/6 [==============================] - 0s 23ms/step - loss: 13.2908 - accuracy: 0.5195\n", + "Epoch 3/100\n", + "6/6 [==============================] - 0s 14ms/step - loss: 13.3748 - accuracy: 0.5117\n", + "Epoch 4/100\n", + "6/6 [==============================] - 0s 14ms/step - loss: 12.6703 - accuracy: 0.5410\n", + "Epoch 5/100\n", + "6/6 [==============================] - 0s 15ms/step - loss: 12.5163 - accuracy: 0.5586\n", + "Epoch 6/100\n", + "6/6 [==============================] - 0s 14ms/step - loss: 12.7866 - accuracy: 0.4531\n", + "Epoch 7/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 12.1621 - accuracy: 0.5020\n", + "Epoch 8/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 12.0670 - accuracy: 0.4727\n", + "Epoch 9/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 12.2272 - accuracy: 0.4766\n", + "Epoch 10/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 11.8846 - accuracy: 0.5234\n", + "Epoch 11/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 11.6332 - accuracy: 0.5430\n", + "Epoch 12/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 11.5610 - accuracy: 0.5469\n", + "Epoch 13/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 11.3528 - accuracy: 0.5586\n", + "Epoch 14/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 11.2470 - accuracy: 0.6016\n", + "Epoch 15/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 11.1688 - accuracy: 0.5547\n", + "Epoch 16/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 11.2074 - accuracy: 0.5723\n", + "Epoch 17/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.8804 - accuracy: 0.5859\n", + "Epoch 18/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 11.4099 - accuracy: 0.5332\n", + "Epoch 19/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 11.2171 - accuracy: 0.5957\n", + "Epoch 20/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.6701 - accuracy: 0.6367\n", + "Epoch 21/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 11.3147 - accuracy: 0.5273\n", + "Epoch 22/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 11.4584 - accuracy: 0.5059\n", + "Epoch 23/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.7740 - accuracy: 0.6191\n", + "Epoch 24/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.8824 - accuracy: 0.5781\n", + "Epoch 25/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.7481 - accuracy: 0.6543\n", + "Epoch 26/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.7032 - accuracy: 0.6230\n", + "Epoch 27/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.8342 - accuracy: 0.5879\n", + "Epoch 28/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.6271 - accuracy: 0.6641\n", + "Epoch 29/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 10.9034 - accuracy: 0.5488\n", + "Epoch 30/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 11.1930 - accuracy: 0.4941\n", + "Epoch 31/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 11.1156 - accuracy: 0.5371\n", + "Epoch 32/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.9151 - accuracy: 0.6016\n", + "Epoch 33/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.8529 - accuracy: 0.5859\n", + "Epoch 34/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.8781 - accuracy: 0.5625\n", + "Epoch 35/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.8427 - accuracy: 0.6113\n", + "Epoch 36/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.6861 - accuracy: 0.6094\n", + "Epoch 37/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.6782 - accuracy: 0.6504\n", + "Epoch 38/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.6600 - accuracy: 0.6270\n", + "Epoch 39/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.7761 - accuracy: 0.5723\n", + "Epoch 40/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.7380 - accuracy: 0.5684\n", + "Epoch 41/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.6890 - accuracy: 0.6035\n", + "Epoch 42/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.5833 - accuracy: 0.6680\n", + "Epoch 43/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.9157 - accuracy: 0.5078\n", + "Epoch 44/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.6079 - accuracy: 0.6523\n", + "Epoch 45/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.6290 - accuracy: 0.6230\n", + "Epoch 46/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.6520 - accuracy: 0.5938\n", + "Epoch 47/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.6034 - accuracy: 0.6504\n", + "Epoch 48/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.6567 - accuracy: 0.6055\n", + "Epoch 49/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.7755 - accuracy: 0.5645\n", + "Epoch 50/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.7243 - accuracy: 0.5781\n", + "Epoch 51/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.6358 - accuracy: 0.6387\n", + "Epoch 52/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.5733 - accuracy: 0.6582\n", + "Epoch 53/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.5997 - accuracy: 0.6191\n", + "Epoch 54/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.5774 - accuracy: 0.6523\n", + "Epoch 55/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.6827 - accuracy: 0.5742\n", + "Epoch 56/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.9124 - accuracy: 0.5195\n", + "Epoch 57/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.5725 - accuracy: 0.6504\n", + "Epoch 58/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.7590 - accuracy: 0.5332\n", + "Epoch 59/100\n", + "6/6 [==============================] - 0s 14ms/step - loss: 10.6612 - accuracy: 0.6211\n", + "Epoch 60/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.6715 - accuracy: 0.6074\n", + "Epoch 61/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.5505 - accuracy: 0.6660\n", + "Epoch 62/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 10.5563 - accuracy: 0.6348\n", + "Epoch 63/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.6242 - accuracy: 0.5820\n", + "Epoch 64/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.5308 - accuracy: 0.6797\n", + "Epoch 65/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.5511 - accuracy: 0.6387\n", + "Epoch 66/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 10.5718 - accuracy: 0.6504\n", + "Epoch 67/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.5806 - accuracy: 0.6074\n", + "Epoch 68/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.5914 - accuracy: 0.6289\n", + "Epoch 69/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.5176 - accuracy: 0.6582\n", + "Epoch 70/100\n", + "6/6 [==============================] - 0s 20ms/step - loss: 10.5397 - accuracy: 0.6660\n", + "Epoch 71/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.5369 - accuracy: 0.6348\n", + "Epoch 72/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 10.5350 - accuracy: 0.6328\n", + "Epoch 73/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6/6 [==============================] - 0s 12ms/step - loss: 10.5265 - accuracy: 0.6348\n", + "Epoch 74/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.5268 - accuracy: 0.6621\n", + "Epoch 75/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 10.5496 - accuracy: 0.6035\n", + "Epoch 76/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 10.5750 - accuracy: 0.6484\n", + "Epoch 77/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 10.5031 - accuracy: 0.6484\n", + "Epoch 78/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 10.5421 - accuracy: 0.6367\n", + "Epoch 79/100\n", + "6/6 [==============================] - 0s 15ms/step - loss: 10.5092 - accuracy: 0.6523\n", + "Epoch 80/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.5365 - accuracy: 0.6172\n", + "Epoch 81/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.5219 - accuracy: 0.6426\n", + "Epoch 82/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 10.5196 - accuracy: 0.6621\n", + "Epoch 83/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.5009 - accuracy: 0.6367\n", + "Epoch 84/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.4797 - accuracy: 0.6719\n", + "Epoch 85/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 10.5241 - accuracy: 0.6250\n", + "Epoch 86/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.4941 - accuracy: 0.6562\n", + "Epoch 87/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 10.4996 - accuracy: 0.6504\n", + "Epoch 88/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.5067 - accuracy: 0.6328\n", + "Epoch 89/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.4744 - accuracy: 0.6816\n", + "Epoch 90/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 10.5490 - accuracy: 0.5996\n", + "Epoch 91/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.4588 - accuracy: 0.6699\n", + "Epoch 92/100\n", + "6/6 [==============================] - 0s 16ms/step - loss: 10.4597 - accuracy: 0.6660\n", + "Epoch 93/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.4697 - accuracy: 0.6660\n", + "Epoch 94/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 10.4641 - accuracy: 0.6602\n", + "Epoch 95/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.4543 - accuracy: 0.6660\n", + "Epoch 96/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.5177 - accuracy: 0.6270\n", + "Epoch 97/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.4491 - accuracy: 0.6797\n", + "Epoch 98/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.4654 - accuracy: 0.6367\n", + "Epoch 99/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.4482 - accuracy: 0.6738\n", + "Epoch 100/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.5714 - accuracy: 0.6191\n", + "16/16 [==============================] - 1s 11ms/step - loss: 10.7151 - accuracy: 0.4805\n", + "2/2 [==============================] - 0s 49ms/step - loss: 10.7193 - accuracy: 0.4561\n", + "Epoch 1/100\n", + "6/6 [==============================] - 0s 24ms/step - loss: 24.6499 - accuracy: 0.4961\n", + "Epoch 2/100\n", + "6/6 [==============================] - 0s 14ms/step - loss: 10.7608 - accuracy: 0.5488\n", + "Epoch 3/100\n", + "6/6 [==============================] - 0s 16ms/step - loss: 10.6703 - accuracy: 0.6465\n", + "Epoch 4/100\n", + "6/6 [==============================] - 0s 14ms/step - loss: 10.6597 - accuracy: 0.6191\n", + "Epoch 5/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.5923 - accuracy: 0.6543\n", + "Epoch 6/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.5901 - accuracy: 0.6230\n", + "Epoch 7/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.5357 - accuracy: 0.6738\n", + "Epoch 8/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 10.5083 - accuracy: 0.6504\n", + "Epoch 9/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.6133 - accuracy: 0.5996\n", + "Epoch 10/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 10.4644 - accuracy: 0.6699\n", + "Epoch 11/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.4385 - accuracy: 0.6641\n", + "Epoch 12/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.4346 - accuracy: 0.6113\n", + "Epoch 13/100\n", + "6/6 [==============================] - 0s 15ms/step - loss: 10.4361 - accuracy: 0.6230\n", + "Epoch 14/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 10.3586 - accuracy: 0.6758\n", + "Epoch 15/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.3491 - accuracy: 0.6387\n", + "Epoch 16/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 10.3341 - accuracy: 0.6309\n", + "Epoch 17/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 10.3329 - accuracy: 0.6855\n", + "Epoch 18/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.2908 - accuracy: 0.6328\n", + "Epoch 19/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 10.2893 - accuracy: 0.5879\n", + "Epoch 20/100\n", + "6/6 [==============================] - 0s 14ms/step - loss: 10.2245 - accuracy: 0.6836\n", + "Epoch 21/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 10.2062 - accuracy: 0.6777\n", + "Epoch 22/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 10.1919 - accuracy: 0.6426\n", + "Epoch 23/100\n", + "6/6 [==============================] - 0s 14ms/step - loss: 10.1629 - accuracy: 0.6680\n", + "Epoch 24/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 10.1501 - accuracy: 0.6445\n", + "Epoch 25/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 10.1015 - accuracy: 0.6426\n", + "Epoch 26/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 10.0737 - accuracy: 0.6602\n", + "Epoch 27/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 10.0624 - accuracy: 0.6426\n", + "Epoch 28/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 10.0252 - accuracy: 0.6680\n", + "Epoch 29/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 10.0046 - accuracy: 0.6719\n", + "Epoch 30/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 9.9919 - accuracy: 0.6621\n", + "Epoch 31/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 9.9913 - accuracy: 0.6367\n", + "Epoch 32/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 9.9446 - accuracy: 0.6680\n", + "Epoch 33/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 9.9728 - accuracy: 0.6309\n", + "Epoch 34/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 9.9044 - accuracy: 0.6387\n", + "Epoch 35/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 9.8717 - accuracy: 0.6680\n", + "Epoch 36/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 9.8502 - accuracy: 0.6621\n", + "Epoch 37/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 9.8268 - accuracy: 0.6699\n", + "Epoch 38/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 9.8169 - accuracy: 0.6484\n", + "Epoch 39/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 9.8171 - accuracy: 0.6250\n", + "Epoch 40/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 9.7534 - accuracy: 0.6582\n", + "Epoch 41/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 9.7499 - accuracy: 0.6758\n", + "Epoch 42/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 9.7435 - accuracy: 0.6445\n", + "Epoch 43/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 9.6912 - accuracy: 0.6914\n", + "Epoch 44/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 9.6917 - accuracy: 0.6738\n", + "Epoch 45/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 9.6655 - accuracy: 0.6445\n", + "Epoch 46/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 9.6215 - accuracy: 0.6992\n", + "Epoch 47/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 9.6076 - accuracy: 0.6738\n", + "Epoch 48/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 9.5743 - accuracy: 0.6855\n", + "Epoch 49/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 9.5855 - accuracy: 0.6484\n", + "Epoch 50/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 9.5339 - accuracy: 0.7227\n", + "Epoch 51/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 9.5146 - accuracy: 0.7070\n", + "Epoch 52/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 9.4899 - accuracy: 0.6816\n", + "Epoch 53/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6/6 [==============================] - 0s 12ms/step - loss: 9.4874 - accuracy: 0.6719\n", + "Epoch 54/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 9.4528 - accuracy: 0.6875\n", + "Epoch 55/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 9.4601 - accuracy: 0.6133\n", + "Epoch 56/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 9.4243 - accuracy: 0.6797\n", + "Epoch 57/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 9.3898 - accuracy: 0.6602\n", + "Epoch 58/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 9.3620 - accuracy: 0.6621\n", + "Epoch 59/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 9.3536 - accuracy: 0.7051\n", + "Epoch 60/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 9.3230 - accuracy: 0.6934\n", + "Epoch 61/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 9.2986 - accuracy: 0.6914\n", + "Epoch 62/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 9.2714 - accuracy: 0.6855\n", + "Epoch 63/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 9.2592 - accuracy: 0.6914\n", + "Epoch 64/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 9.2284 - accuracy: 0.7129\n", + "Epoch 65/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 9.2129 - accuracy: 0.6816\n", + "Epoch 66/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 9.1845 - accuracy: 0.7012\n", + "Epoch 67/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 9.1704 - accuracy: 0.7168\n", + "Epoch 68/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 9.1450 - accuracy: 0.7148\n", + "Epoch 69/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 9.1377 - accuracy: 0.6738\n", + "Epoch 70/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 9.1031 - accuracy: 0.7051\n", + "Epoch 71/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 9.0857 - accuracy: 0.7012\n", + "Epoch 72/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 9.0689 - accuracy: 0.7031\n", + "Epoch 73/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 9.0378 - accuracy: 0.7148\n", + "Epoch 74/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 9.0195 - accuracy: 0.7090\n", + "Epoch 75/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 9.0011 - accuracy: 0.7109\n", + "Epoch 76/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 8.9820 - accuracy: 0.7168\n", + "Epoch 77/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 8.9592 - accuracy: 0.7090\n", + "Epoch 78/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 8.9467 - accuracy: 0.7129\n", + "Epoch 79/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 8.9202 - accuracy: 0.7070\n", + "Epoch 80/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 8.8976 - accuracy: 0.7461\n", + "Epoch 81/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 8.8774 - accuracy: 0.6973\n", + "Epoch 82/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 8.8607 - accuracy: 0.7012\n", + "Epoch 83/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 8.8382 - accuracy: 0.7051\n", + "Epoch 84/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 8.8149 - accuracy: 0.6953\n", + "Epoch 85/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 8.8007 - accuracy: 0.7090\n", + "Epoch 86/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 8.7774 - accuracy: 0.7266\n", + "Epoch 87/100\n", + "6/6 [==============================] - 0s 14ms/step - loss: 8.7650 - accuracy: 0.7012\n", + "Epoch 88/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 8.7353 - accuracy: 0.7051\n", + "Epoch 89/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 8.7171 - accuracy: 0.7324\n", + "Epoch 90/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 8.6977 - accuracy: 0.7188\n", + "Epoch 91/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 8.6871 - accuracy: 0.6621\n", + "Epoch 92/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 8.6672 - accuracy: 0.7402\n", + "Epoch 93/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 8.6385 - accuracy: 0.6895\n", + "Epoch 94/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 8.6129 - accuracy: 0.7207\n", + "Epoch 95/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 8.6130 - accuracy: 0.6934\n", + "Epoch 96/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 8.5767 - accuracy: 0.7090\n", + "Epoch 97/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 8.5579 - accuracy: 0.7227\n", + "Epoch 98/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 8.5398 - accuracy: 0.7031\n", + "Epoch 99/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 8.5137 - accuracy: 0.7285\n", + "Epoch 100/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 8.4966 - accuracy: 0.7266\n", + "16/16 [==============================] - 0s 10ms/step - loss: 8.5038 - accuracy: 0.6348\n", + "2/2 [==============================] - 0s 39ms/step - loss: 8.5332 - accuracy: 0.6316\n", + "Epoch 1/100\n", + "6/6 [==============================] - 0s 23ms/step - loss: 3498733.0000 - accuracy: 0.5039\n", + "Epoch 2/100\n", + "6/6 [==============================] - 0s 15ms/step - loss: 1342693347164160.0000 - accuracy: 0.5547\n", + "Epoch 3/100\n", + "6/6 [==============================] - 0s 15ms/step - loss: 1632294938869760.0000 - accuracy: 0.5469\n", + "Epoch 4/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 1593548159844352.0000 - accuracy: 0.3711\n", + "Epoch 5/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 1555720906473472.0000 - accuracy: 0.5859\n", + "Epoch 6/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 1518791435485184.0000 - accuracy: 0.6289\n", + "Epoch 7/100\n", + "6/6 [==============================] - 0s 14ms/step - loss: 1482738808913920.0000 - accuracy: 0.6289\n", + "Epoch 8/100\n", + "6/6 [==============================] - 0s 16ms/step - loss: 1447541954576384.0000 - accuracy: 0.6289\n", + "Epoch 9/100\n", + "6/6 [==============================] - 0s 17ms/step - loss: 1413180605595648.0000 - accuracy: 0.6289\n", + "Epoch 10/100\n", + "6/6 [==============================] - 0s 17ms/step - loss: 1379634763530240.0000 - accuracy: 0.6289\n", + "Epoch 11/100\n", + "6/6 [==============================] - 0s 15ms/step - loss: 1346885369462784.0000 - accuracy: 0.6289\n", + "Epoch 12/100\n", + "6/6 [==============================] - 0s 14ms/step - loss: 1314913096040448.0000 - accuracy: 0.6289\n", + "Epoch 13/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 1283700092305408.0000 - accuracy: 0.6289\n", + "Epoch 14/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 1253227970428928.0000 - accuracy: 0.6289\n", + "Epoch 15/100\n", + "6/6 [==============================] - 0s 14ms/step - loss: 1223479147888640.0000 - accuracy: 0.6289\n", + "Epoch 16/100\n", + "6/6 [==============================] - 0s 17ms/step - loss: 1194436444815360.0000 - accuracy: 0.6289\n", + "Epoch 17/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 1166083352428544.0000 - accuracy: 0.6289\n", + "Epoch 18/100\n", + "6/6 [==============================] - 0s 14ms/step - loss: 1138403093512192.0000 - accuracy: 0.6289\n", + "Epoch 19/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 1111379964592128.0000 - accuracy: 0.6289\n", + "Epoch 20/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 1084998262194176.0000 - accuracy: 0.6289\n", + "Epoch 21/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 1059242886823936.0000 - accuracy: 0.6289\n", + "Epoch 22/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 1034098873204736.0000 - accuracy: 0.6289\n", + "Epoch 23/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 1009551725821952.0000 - accuracy: 0.6289\n", + "Epoch 24/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 985587217596416.0000 - accuracy: 0.6289\n", + "Epoch 25/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 962191658319872.0000 - accuracy: 0.6289\n", + "Epoch 26/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 939351290675200.0000 - accuracy: 0.6289\n", + "Epoch 27/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 917053363978240.0000 - accuracy: 0.6289\n", + "Epoch 28/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 895284523565056.0000 - accuracy: 0.6289\n", + "Epoch 29/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 874032421404672.0000 - accuracy: 0.6289\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 30/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 853284910792704.0000 - accuracy: 0.6289\n", + "Epoch 31/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 833029845024768.0000 - accuracy: 0.6289\n", + "Epoch 32/100\n", + "6/6 [==============================] - 0s 14ms/step - loss: 813255614267392.0000 - accuracy: 0.6289\n", + "Epoch 33/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 793950742904832.0000 - accuracy: 0.6289\n", + "Epoch 34/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 775104090865664.0000 - accuracy: 0.6289\n", + "Epoch 35/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 756704987840512.0000 - accuracy: 0.6289\n", + "Epoch 36/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 738742427975680.0000 - accuracy: 0.6289\n", + "Epoch 37/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 721206344941568.0000 - accuracy: 0.6289\n", + "Epoch 38/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 704086605299712.0000 - accuracy: 0.6289\n", + "Epoch 39/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 687373142720512.0000 - accuracy: 0.6289\n", + "Epoch 40/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 671056494854144.0000 - accuracy: 0.6289\n", + "Epoch 41/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 655127065133056.0000 - accuracy: 0.6289\n", + "Epoch 42/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 639575860969472.0000 - accuracy: 0.6289\n", + "Epoch 43/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 624393688449024.0000 - accuracy: 0.6289\n", + "Epoch 44/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 609572024745984.0000 - accuracy: 0.6289\n", + "Epoch 45/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 595102145708032.0000 - accuracy: 0.6289\n", + "Epoch 46/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 580975796944896.0000 - accuracy: 0.6289\n", + "Epoch 47/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 567184724066304.0000 - accuracy: 0.6289\n", + "Epoch 48/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 553720974671872.0000 - accuracy: 0.6289\n", + "Epoch 49/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 540576898351104.0000 - accuracy: 0.6289\n", + "Epoch 50/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 527744844693504.0000 - accuracy: 0.6289\n", + "Epoch 51/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 515217331060736.0000 - accuracy: 0.6289\n", + "Epoch 52/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 502987243913216.0000 - accuracy: 0.6289\n", + "Epoch 53/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 491047469711360.0000 - accuracy: 0.6289\n", + "Epoch 54/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 479391096242176.0000 - accuracy: 0.6289\n", + "Epoch 55/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 468011412619264.0000 - accuracy: 0.6289\n", + "Epoch 56/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 456901875728384.0000 - accuracy: 0.6289\n", + "Epoch 57/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 446056076673024.0000 - accuracy: 0.6289\n", + "Epoch 58/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 435467707219968.0000 - accuracy: 0.6289\n", + "Epoch 59/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 425130626908160.0000 - accuracy: 0.6289\n", + "Epoch 60/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 415038997266432.0000 - accuracy: 0.6289\n", + "Epoch 61/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 405186879160320.0000 - accuracy: 0.6289\n", + "Epoch 62/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 395568668999680.0000 - accuracy: 0.6289\n", + "Epoch 63/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 386178696085504.0000 - accuracy: 0.6289\n", + "Epoch 64/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 377011725926400.0000 - accuracy: 0.6289\n", + "Epoch 65/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 368062322704384.0000 - accuracy: 0.6289\n", + "Epoch 66/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 359325352591360.0000 - accuracy: 0.6289\n", + "Epoch 67/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 350795748868096.0000 - accuracy: 0.6289\n", + "Epoch 68/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 342468646141952.0000 - accuracy: 0.6289\n", + "Epoch 69/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 334339212574720.0000 - accuracy: 0.6289\n", + "Epoch 70/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 326402750545920.0000 - accuracy: 0.6289\n", + "Epoch 71/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 318654663098368.0000 - accuracy: 0.6289\n", + "Epoch 72/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 311090521047040.0000 - accuracy: 0.6289\n", + "Epoch 73/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 303705962315776.0000 - accuracy: 0.6289\n", + "Epoch 74/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 296496658382848.0000 - accuracy: 0.6289\n", + "Epoch 75/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 289458482053120.0000 - accuracy: 0.6289\n", + "Epoch 76/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 282587373240320.0000 - accuracy: 0.6289\n", + "Epoch 77/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 275879372521472.0000 - accuracy: 0.6289\n", + "Epoch 78/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 269330654691328.0000 - accuracy: 0.6289\n", + "Epoch 79/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 262937344212992.0000 - accuracy: 0.6289\n", + "Epoch 80/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 256695817207808.0000 - accuracy: 0.6289\n", + "Epoch 81/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 250602399465472.0000 - accuracy: 0.6289\n", + "Epoch 82/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 244653668433920.0000 - accuracy: 0.6289\n", + "Epoch 83/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 238846134452224.0000 - accuracy: 0.6289\n", + "Epoch 84/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 233176475631616.0000 - accuracy: 0.6289\n", + "Epoch 85/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 227641386860544.0000 - accuracy: 0.6289\n", + "Epoch 86/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 222237714022400.0000 - accuracy: 0.6289\n", + "Epoch 87/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 216962286223360.0000 - accuracy: 0.6289\n", + "Epoch 88/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 211812083564544.0000 - accuracy: 0.6289\n", + "Epoch 89/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 206784136478720.0000 - accuracy: 0.6289\n", + "Epoch 90/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 201875559284736.0000 - accuracy: 0.6289\n", + "Epoch 91/100\n", + "6/6 [==============================] - 0s 11ms/step - loss: 197083516633088.0000 - accuracy: 0.6289\n", + "Epoch 92/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 192405189951488.0000 - accuracy: 0.6289\n", + "Epoch 93/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 187837911662592.0000 - accuracy: 0.6289\n", + "Epoch 94/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 183379064520704.0000 - accuracy: 0.6289\n", + "Epoch 95/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 179026064834560.0000 - accuracy: 0.6289\n", + "Epoch 96/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 174776379244544.0000 - accuracy: 0.6289\n", + "Epoch 97/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 170627558277120.0000 - accuracy: 0.6289\n", + "Epoch 98/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 166577253122048.0000 - accuracy: 0.6289\n", + "Epoch 99/100\n", + "6/6 [==============================] - 0s 13ms/step - loss: 162623081414656.0000 - accuracy: 0.6289\n", + "Epoch 100/100\n", + "6/6 [==============================] - 0s 12ms/step - loss: 158762778230784.0000 - accuracy: 0.6289\n", + "16/16 [==============================] - 0s 10ms/step - loss: 156281545424896.0000 - accuracy: 0.6289\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/2 [==============>...............] - ETA: 0s - loss: 156281578979328.0000 - accuracy: 0.6562\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "2/2 [==============================] - 0s 38ms/step - loss: 156281578979328.0000 - accuracy: 0.6140\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/1608181771.py:136: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n", + " cbar=fig.colorbar(cax)\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/1608181771.py:152: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " ax.set_xticklabels(['']+x)\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_73993/1608181771.py:153: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " ax.set_yticklabels(['']+y)\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABEgAAASfCAYAAAAeQMhBAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAA9hAAAPYQGoP6dpAACyhElEQVR4nOzdd3gVVf7H8c9NuUluEggJIYWWANKkF6W5SBNEURFxFWGVbgEUBNFFxIaroCgYUFZAEZUmiKgs6sL+FDSAIIqYIL2TQhIS0sud3x+BSKSFm4Qb7rxfz5NHcubMnDOUcfK5Z75jMQzDEAAAAAAAgIm5OXsCAAAAAAAAzkZAAgAAAAAATI+ABAAAAAAAmB4BCQAAAAAAMD0CEgAAAAAAYHoEJAAAAAAAwPQISAAAAAAAgOkRkAAAAAAAANMjIAEAoBQMw3D2FAAAAFAGCEgAAHDQunXrNHHixFIfZ+XKlWrQoIGOHj1arvsAAADg4iwGH30BAOCQQYMGSZIWLVpUquMkJyfr8OHDaty4saxWa7ntAwAAgIvzcPYEAAAwu8DAQAUGBpb7PgAAALg4HrEBAMABgwYN0pYtW7RlyxY1aNBAmzdvVoMGDbRkyRJ16dJFHTp00MaNGyVJy5cv1913360WLVqoWbNmuvPOO7VmzZqiY/31cZmnn35aDz30kFasWKGePXuqSZMmuuOOO/Tdd9+Vah9J2r59ux544AG1aNFCN998sxYuXKiHHnpITz/9dHn/lgEAAFRoBCQAADhgypQpaty4sRo3bqylS5cqPT1dkvTmm29q4sSJmjhxolq0aKGPP/5Yzz33nLp166a5c+dq+vTp8vT01IQJE3T8+PGLHn/nzp2aP3++xowZo9mzZ8vDw0NjxoxRamqqw/vs27dPDz30kCRpxowZGj16tP79739r27ZtZfcbAwAAcI3iERsAABxQr149+fn5SZJatGihzZs3S5Luu+8+9erVq6jfkSNHNGTIED322GNFbTVq1NDdd9+tn3/+WeHh4Rc8/unTp7Vy5UrVqlVLkmSz2TRw4EBt2rRJPXv2dGifuXPnys/PT/PmzZOPj48kqU6dOrrvvvtK+bsBAABw7SMgAQCgDDVo0KDY92cfXTl9+rQOHjyogwcPKjo6WpKUl5d30eMEBgYWBR2SFBoaKknKyspyeJ9Nmzapc+fOReGIJLVs2VLVq1cv0bkBAAC4MgISAADKUFBQULHvDx8+rOeee06bNm2Sh4eH6tSpUxSiXOpFcueGGJJksVgkSXa73eF9kpOTz5ufJAUHB1/0mAAAAGZBQAIAQDmx2+0aMWKEPD09tWzZMjVu3FgeHh7au3evVq9efdXnExoaqqSkpPPak5KSFBkZedXnAwAAUJFQpBUAAAe5uV36f6MpKSk6cOCA7rnnHjVr1kweHoWfS3z//feSLr0apDy0bdtW33//vXJycoraYmNji96EAwAAYGasIAEAwEGVKlXS9u3bFR0dXfQWm3MFBQWpevXq+vjjjxUaGqpKlSpp48aNWrhwoaRL1xMpDw8//LDWrFmjYcOGaciQIUpLS9PMmTNlsViKHscBAAAwK1aQAADgoAceeECenp4aPny4srOzL9hnzpw5CgkJ0dNPP60nnnhCv/zyi9555x3VqVNHW7duvarzrV27tubPn6+cnByNGTNGb775poYPH67g4GD5+vpe1bkAAABUNBbjUhXiAACAy4iOjpanp6fatGlT1JaamqqOHTvqqaee0j/+8Q8nzg4AAMC5eMQGAACT+P333zVr1iyNGzdO119/vVJSUrRgwQL5+/vr9ttvd/b0AAAAnIqABAAAkxgyZIhyc3O1ePFinThxQjabTTfccINee+01BQYGOnt6AAAATsUjNgAAAAAAwPQo0goAAAAAAEyPgAQAAAAAAJgeAQkAAAAAADA9AhIAAAAAAGB6BCRACdntds2aNUs33XSTmjdvriFDhujQoUMX7Z+SkqInn3xSbdu2Vdu2bTV58mRlZmYW6/Of//xHvXv3VtOmTdWnTx99//33Fx176NChevvtt8v0nABcfeVxLTnrp59+UqNGjcpr6gAqkCu9lpy7H/cUAHBhBCRACc2ZM0dLlizRyy+/rKVLl8pisWj48OHKzc29YP8xY8boyJEj+uCDDzRr1iz98MMPeuGFF4q2b9q0SRMmTNCAAQO0atUqderUSY899pj27dtX7DjZ2dmaMGGCNm7cWK7nB+DqKOtryVmbN2/Wo48+KrvdXt6nAKACuNJricQ9BQBcDgEJUAK5ublasGCBRo8erc6dO6thw4Z68803FR8fr2+//fa8/tu3b9eWLVv0r3/9S9dff73at2+vF198UZ9//rni4+MlSe+995569OihgQMHqm7dupo4caKuv/56LVy4sOg4P//8s/r27atff/1VlSpVumrnC6B8lMe1JD8/Xy+//LKGDBmimjVrXu1TAuAEV3otkbinAICSICABSmDXrl3KyMhQu3btitoqVaqkxo0b66effjqv/9atWxUcHKy6desWtd1www2yWCzatm2b7Ha7fv7552LHk6Qbb7xRW7duLfp+w4YN6tGjh1atWiV/f/9yODMAV1NZX0skKTMzUzt37tSCBQs0cODA8j8JAE53pdcSiXsKACgJD2dPALgWxMXFSZLCwsKKtVerVk0nTpw4r398fPx5fa1WqwICAnTixAmlpaUpMzNToaGhlzze448/XlanAKACKOtriVT4Q9GSJUskSStXriyPaQOoYK70WiJxTwEAJcEKEqAEsrKyJBX+YHIuLy8v5eTkXLD/X/ue2z87O/uKjgfANZT1tQSAOV3ptQQAUDIEJEAJeHt7S9J5hc9ycnLk4+Nzwf4XKpKWk5Mjm80mLy+vKzoeANdQ1tcSAOZ0pdcSAEDJEJAAJXB2CWtCQkKx9oSEhPMek5Gk0NDQ8/rm5ubq1KlTCgkJUUBAgGw2W4mPB8A1lPW1BIA5Xem1BABQMgQkQAk0bNhQfn5+2rx5c1FbWlqaYmJi1KZNm/P6t23bVnFxcTp06FBR29l9W7VqJYvFolatWmnLli3F9tu8ebNat25dTmcBwNnK+loCwJyu9FoCACgZirQCJWC1WjVw4EC9/vrrCgwMVPXq1TV9+nSFhoaqR48eKigoUHJysvz9/eXt7a3mzZurVatWGjt2rJ5//nllZmZqypQpuuuuu4o+9R08eLBGjBihxo0b629/+5tWrFih2NhYTZ061clnC6C8lMe1BID5XOm1BABQMqwgAUpozJgxuueee/Tss8/q/vvvl7u7u+bPny+r1aoTJ06oU6dOWrNmjSTJYrEoKipKNWrU0IMPPqgnnnhCf/vb3/T8888XHa9Tp0565ZVXtHjxYvXt21ebNm3Su+++W+x1ngBcT1lfSwCY05VcSwAAJWMxDMNw9iQAAAAAAACciRUkAAAAAADA9AhIAAAAAACA6RGQAAAAAAAA0yMgAQAAAAAApkdAAgAAAAAATI+ABAAAAAAAmB4BCQAAAAAAMD0CEuAq6datm7p16+bsaQC4xnDtAOAIrh0AcOUISAAAAAAAgOkRkAAAAAAAANMjIAEAAAAAwGTmzJmjQYMGFWuLjY3VwIED1aJFC918882aP39+se12u12zZs3STTfdpObNm2vIkCE6dOhQ0facnByNGzdOrVq1Ut++fbVr165i+7/00kuaPn16+Z1UKRGQAAAAAABgIh988IFmzZpVrC0lJUWDBw9WRESEVqxYodGjR2vmzJlasWJFUZ85c+ZoyZIlevnll7V06VJZLBYNHz5cubm5kqRPP/1UBw4c0KeffqrOnTtr8uTJRfsePnxYa9eu1ciRI6/OSTqAgAQAAAAAABOIj4/XsGHDNHPmTEVGRhbbtmzZMlmtVj3//POqW7eu+vXrp4ceekjvvfeeJCk3N1cLFizQ6NGj1blzZzVs2FBvvvmm4uPj9e2330qS9uzZo06dOqlOnTrq06eP9uzZU3T8119/XUOHDlWlSpWu3glfIQISAAAAAABM4Pfff1flypW1evVqNW/evNi2rVu3qm3btvLw8Chqa9eunQ4cOKCkpCTt2rVLGRkZateuXdH2SpUqqXHjxvrpp58kSTVr1tSOHTuUm5urn3/+WTVr1pQk/frrr9q5c6cGDhx4Fc7ScR6X7wKgLCxatMjZUwBwDeLaAcARXDsA1/Xrr79q3LhxF92+bt26i27r2rWrunbtesFtcXFxql+/frG2atWqSZKOHz+uuLg4SVJYWNh5fU6cOCFJ+vvf/66vv/5aLVq0kL+/v9566y1J0rRp0/T444/LarVe+uScjICkHBj5+bKfTHD2NFDBhHi6y61qNRUkxksFBc6eDoBrBNcOAI7g2oFLcataTRYP1/9R0DAMyZ7v7GmULTcPBQcHl8uhs7OzzwswvLy8JBUWX83KypKkC/ZJTU2VJPn5+WnZsmU6efKkAgIC5OHhof/+97/KyMjQHXfcofnz5+vjjz9WcHCwXnrppfMCGWdz/X8VTmA/maBTI+939jRQwbjXuU4Bb87T6VcmqWD/nsvvAADi2gHAMVw7cCkBcxfLPTTc2dMof/Z85acccfYsypRHlZoKDw+/5CoRR3l7excVWz0rJydHkmSz2eTt7S2psBbJ2V+f7ePj41Nsv6pVq0qSCgoKNGPGDP3zn/9UbGysFi5cqFWrVmnjxo166qmntGrVqjI/j9KgBgkAAAAAACYXGhqqhITiT0Kc/T4kJKTo0ZoL9QkNDb3gMZcvX66QkBB16tRJW7ZsUatWrRQYGKhbbrlFsbGxSk9PL4czcRwBCQAAAAAAJte2bVtt27ZNBec8lhcdHa3IyEgFBQWpYcOG8vPz0+bNm4u2p6WlKSYmRm3atDnveJmZmZo9e7YmTJggSbJYLEXHzsvLkyTZ7fbyPKUrRkACAAAAAHBN9gLX+ipH/fr1U3p6uiZNmqS9e/dq5cqVWrhwoUaOHCmpsPbIwIED9frrr2vdunXatWuXxo4dq9DQUPXo0eO84y1YsEA33nijGjduLElq3ry5Nm3apB07duijjz7SddddV+Fe+UsNEgAAAAAATC4oKEjz5s3T1KlT1bdvXwUHB+upp55S3759i/qMGTNG+fn5evbZZ5Wdna22bdtq/vz55xVuTUpK0qJFi7RixYqithYtWmjgwIEaNmyYQkJC9Oqrr161cyspAhIAAAAAAEzmQgFFs2bNtHTp0ovu4+7urgkTJhQ9NnMxQUFBxR7FOevxxx/X448/fuWTvUp4xAYAAAAAAJgeK0gAAAAAAK7HMCSjYhUBLTXDcPYMXBorSAAAAAAAgOkRkAAAAAAAANMjIAEAAAAAAKZHDRIAAAAAgGuyu1gNEpQrVpAAAAAAAADTIyABAAAAAACmR0ACAAAAAABMj4AEAAAAAACYHkVaAQAAAAAuyTAo0oqSYwUJAAAAAAAwPQISAAAAAABgegQkAAAAAADA9KhBAgAAAABwTXZqkKDkWEECAAAAAABMj4AEAAAAAACYHgEJAAAAAAAwPWqQAAAAAABckCEZrlaDxHD2BFwaK0gAAAAAAIDpEZAAAAAAAADTIyABAAAAAACmRw0SAAAAAIDrMSTZC5w9i7JFCZJyxQoSAAAAAABgegQkAAAAAADA9AhIAAAAAACA6VGDBAAAAADgmgy7s2eAawgrSAAAAAAAgOkRkAAAAAAAANMjIAEAAAAAAKZHQAIAAAAAAEyPIq0AAAAAABdkSHZXK9JqOHsCLo0VJAAAAAAAwPQISAAAAAAAgOkRkAAAAAAAANOjBgkAAAAAwCUZhqvVIEF5YgUJAAAAAAAwPQISAAAAAABgegQkAAAAAADA9KhBAgAAAABwTXZqkKDkWEECAAAAAABMj4AEAAAAAACYHgEJAAAAAAAwPWqQAAAAAABcjyHJcLEaJIazJ+DaWEECAAAAAABMj4AEAAAAAACYHgEJAAAAAAAwPQISAAAAAABgehRpBQAAAAC4IEOyFzh7EmWMKq3liRUkAAAAAADA9AhIAAAAAACA6RGQAAAAAAAA06MGCQAAAADANRl2Z88A1xBWkAAAAAAAANMjIAEAAAAAAKZHQAIAAAAAAEyPGiQAAAAAANdkpwYJSo4VJAAAAAAAwPQISAAAAAAAgOkRkAAAAAAAANOjBgkAAAAAwAUZkuFqNUgMZ0/ApbGCBAAAAAAAmB4BCQAAAAAAMD0CEgAAAAAAYHoEJAAAAAAAwPQo0goAAAAAcD2GJLuLFWmlRmu5YgUJAAAAAAAwPQISAAAAAABgegQkAAAAAADA9KhBAgAAAABwOYYkwyhw9jTKlCHJ4uxJuDBWkAAAAAAAANMjIAEAAAAAAKZHQAIAAAAAAEyPGiQAAAAAABdkSIbd2ZMoY4azJ+DSWEECAAAAAABMj4AEAAAAAACYHgEJAAAAAAAwPWqQAAAAAABck93VapCgPLGCBAAAAAAAmB4BCQAAAAAAMD0CEgAAAAAAYHoEJAAAAAAAwPQo0goAAAAAcE0GRVpRcqwgAQAAAAAApkdAAgAAAAAATI+ABAAAAAAAmB41SAAAAAAArseQZC9w9izKluHsCbg2VpAAAAAAAADTIyABAAAAAACmR0ACAAAAAABMjxokAAAAAAAXZEiG3dmTKGMUISlPrCABAAAAAACmR0ACAAAAAABMj4AEAAAAAACYHjVIAAAAAACuye5qNUhQnlhBAgAAAAAATI+ABAAAAAAAmB4BCQAAAAAAMD1qkAAAAAAAXJNBDRKUHCtIAAAAAACA6RGQAAAAAAAA0yMgAQAAAAAApkdAAgAAAAAATI8irQAAAAAAF2RIdlcr0mo4ewIujRUkAAAAAADA9AhIAAAAAACA6RGQAAAAAAAA06MGCQAAAADA9RhyvRoklCApV6wgAQAAAAAApkdAAgAAAAAATI+ABAAAAAAAmB41SAAAAAAALskwCpw9BVxDWEECAAAAAABMj4AEAAAAAACYHgEJAAAAAAAwPWqQAAAAAABck93u7BngGsIKEgAAAAAAYHoEJAAAAAAAwPQISAAAAAAAgOkRkAAAAAAAANOjSCsAAAAAwAUZkuFqRVoNZ0/ApbGCBAAAAAAAmB4rSIC/+DUzVx8mpWtnVp5yDEPVPNx1a2UfDa7qJ0nampGjr9Oy9VtWrhLy7PJzt6iht6eGVPVTQ2/PEo0Rk5Wrf58sHMMwpEY+nhpR1U/NbNZi/eLzCjQtLlW/ZuUp2MNNjwT762/+3sX6rE/L1rS4VC2uE6wqHmSegLNw7QDgCK4dAFBxcFUDzvFNapYeO5wsXzc3TQ6rrDdqVNHAIN9iC9k+O5WpE3kFureKr96oWUVPVKuklHy7hh9M0taMnMuOEXPqtB49nKwcu6HJYZX1XHhl5dgNjTmSrN+ycov1felEqtLthqZWD1Bnf29NPn5KR3Pzi7anF9j1VkKaRlXz5yYFcCKuHQAcwbUDACoWVpD8xZw5cxQdHa1FixY5eyq4yhLzCvRaXJruDPDRhNDKRe2t/9LvyZBKCvRwL9bWzs+qe/ed1IdJGWrj63XJcebuPiQ/Nze9WTNQ3m4WSVIbm1X99ycqKuG05tYOkiRl2w1tz8zVu7UD1dTHqht9vfS/tGz9lJGrGtbCf7pzEk+rltVDtwfYSnn2ABzFtQOAI7h2AFeJ3dVqkKA8Ef2e44MPPtCsWbOcPQ04yerULGUZhgYG+V2y319vUiTJ5uamCC8PJeQXXHacHSlpammzFt2kSJKvu5ta+Fj1W1aeTp45Rp5hyJDkY/mzn4+bRblG4edKv2Xm6j+pWXoqtFJJTg9AOeHaAcARXDsAoOIhIJEUHx+vYcOGaebMmYqMjHT2dOAkv2TmqpKbRYdy8vXggZO6aVeceu9J0LS4VGUUXDp5Ti+wa3d2niKtl1+UlWfYZbWc3+555sZlX07hUlZ/dzfVtrprcXKG0grs+u50tvbm5Kupj6fyDUOvxqXpH0F+qlWCMQGUH64dABzBtQMAKh6ucJJ+//13Va5cWatXr9bs2bN17Nix0h3Q3V3uda4rm8nhqjl5ZKtylK9n49L0YJ2aalrFXzGp6Xpv9yHtt3jq3+2ayWK5wB2GpBm/7FKWIQ1u3kjulf0v2Me9Ri1JUmSVAP2enS1LZD25nTlevt1QzOGtkqTTQdXkHl5NkvRslRA9/XOMeu1JkJukwfVqqWn92lqw97BkterBNs3l7kbOCTgT1w4AjuDaAafyLFmBX8BsCEgkde3aVV27di2z47lVraaAN+eV2fFwdVh69lTOwYN6cuyTGjFihCSpm6RKCxfqlVdeUey9Q9WhQ4fz9nvrrbe0ds0GTZ48We0HDrzsOA+OG69JkyZpVkBNPfLII7Lb7YqKilJcTp4kyf/BhxXQu7ck6W+SvsvL05EjRxQcHCx/f38dPHhQC++8U/Pnz1dQq1aKiorSypUrlZeXp549e2rixIny8rr088gAyg7XDgCO4NoBXAWGJMPFapAYl+8Cx1kMw+C3+BxPP/20jh07VqoirQWJ8Tr9yqQynBWuhqE//qKdp07rw44t1aDyn88DH07PVP/vt2lUgwgNqluz2D7z9hzSe3sO65H6tfVQvVqXPL57jVryf/I5nX7jRb3/3Q96f+8RZRYUPvfbNMBfLQMr68P9RzW3XTO1CKx80eM8tnmHath89EzT67T6SJzm7TmsqBubyubursd/2qnOIUEaUb92KX4nAFwJrh0AHMG1A87k/+y/5B4U7OxplDt7ZppyfvzE2dMoU14dBsjNRi2g8sIKkvJQUKCC/XucPQtcobrK105JBccOqyDpz2WH+WeezVVykgos2UXt80+ma/7JdA2t6qdBbjkl/jMvOHpYD1iy1b9uVR3Jy5fNzU1hnu56LS5JPhaL6ifHq+BUwgX3/epUpg6cStfUQC8V7N+jH46lqIuPu6rHH5Uk3e7jprVHj2uoR+4F9wdQ9rh2AHAE1w44VV6es2cAVEg8RAiccbO/tyRpU3pOsfbojMLvr/f58+bl/TM3KQ8F+Wpo1UtXn78Yq5tFdb08Febprri8Aq1Ly9YdAT7ycrvw88an8u2KSjytJ0L85e9+5p+uIWWfswgsy86CMOBq49oBwBFcOwCg4mEFCXDGjb5e6uTnpfeT0mWX1MTHU7HZeVpwMl0dfb3U3GaVJH2SlKH3Tqarna9VHfy8tDOr+KcmTXysRb/enpmrMYeTNbiqn4bXKWzbdzpD606mq6G3h6wWi/Zk5+uj5HTVsLprePDFb3pmJaSpsbdV3Sr5FLXd4OelqITTauJjlY/FouUpmeoT4HPRYwAoe1w7ADiCawdwNRiS3cVqkFCEpFwRkADneCk8QPNPpmv1qUwtOGlXVQ833RfoqyFBf95A/JBeuNx1U0auNmUkn3eMHxuGFv3aMAwVSLKf82mLp8WibRk5Wp6coSzDUIiHu+4KsGlQkK98LlIZfmtGjr47naOP6lQt1t6nso+O5xbonYTTyjMM3ezvrYeCHPtkCYDjuHYAcATXDgCoWCjS+hdlUqQ17rhOjby/DGcFV+Be5zoFvDlPp8YOo0YNgBLj2gHAEVw7cCkBcxfLPTTc2dMod/bMVOVs/NjZ0yhTXp0ekJvt4oWVUTqsIPmLV1991dlTAAAAAAAAVxlFWgEAAAAAgOmxggQAAAAA4JoMVyvSivLEChIAAAAAAGB6BCQAAAAAAMD0CEgAAAAAAIDpUYMEAAAAAOCa7NQgQcmxggQAAAAAAJgeAQkAAAAAADA9AhIAAAAAAGB61CABAAAAALgmapDgCrCCBAAAAAAAmB4BCQAAAAAAMD0CEgAAAAAAYHrUIAEAAAAAuB7DkAwXq0FiGM6egUtjBQkAAAAAADA9AhIAAAAAAGB6BCQAAAAAAMD0qEECAAAAAHBNdherQYJyxQoSAAAAAABgegQkAAAAAADA9AhIAAAAAACA6RGQAAAAAAAA06NIKwAAAADANRkUaUXJsYIEAAAAAACYHgEJAAAAAAAwPQISAAAAAABgetQgAQAAAAC4Jjs1SFByrCABAAAAAACmR0ACAAAAAABMj4AEAAAAAACYHjVIAAAAAAAuyJAMV6tBYjh7Ai6NFSQAAAAAAMD0CEgAAAAAAIDpEZAAAAAAAADTowYJAAAAAMD1GJLsLlaDhBIk5YoVJAAAAAAAwPQISAAAAAAAgOkRkAAAAAAAANMjIAEAAAAAAKZHkVYAAAAAgGtytSKtKFesIAEAAAAAAKZHQAIAAAAAAEyPgAQAAAAAAJgeNUgAAAAAAK7JMJw9A1xDWEECAAAAAABMj4AEAAAAAACYHgEJAAAAAAAwPWqQAAAAAABckCHZ7c6eRBmjpkp5YgUJAAAAAAAwPQISAAAAAABgegQkAAAAAADA9KhBAgAAAABwPYZcrwYJJUjKFStIAAAAAACA6RGQAAAAAAAA0yMgAQAAAAAApkdAAgAAAAAATI8irQAAAAAA12S4WJFWlCtWkAAAAAAAANMjIAEAAAAAAKZHQAIAAAAAAEyPGiQAAAAAANdkpwYJSo4VJAAAAAAAwPQISAAAAAAAgOkRkAAAAAAAANOjBgkAAAAAwAUZkmE4exJlzNXOp2JhBQkAAAAAADA9AhIAAAAAAGB6BCQAAAAAAMD0qEECAAAAAHBNdruzZ4BrCCtIAAAAAAAwgby8PL355pu6+eab1bJlSw0YMEA///xz0fbY2FgNHDhQLVq00M0336z58+cX23/Dhg3q1q2bbrjhBr322mvFtsXHx6tDhw5KSkq6KudSHghIAAAAAAAwgXfeeUcrVqzQyy+/rFWrVqlOnToaPny44uPjlZKSosGDBysiIkIrVqzQ6NGjNXPmTK1YsUKSZLfbNXHiRI0YMUIffvih1qxZo++++67o2G+99ZYGDBigoKAgZ51eqfGIDQAAAAAAJrBu3Trdfvvt6tSpkyTp6aef1vLly/XLL7/o4MGDslqtev755+Xh4aG6devq0KFDeu+999SvXz+lpKQoKSlJffv2ldVqVatWrbRnzx517txZf/zxhzZu3Kivv/7ayWdYOgQkAAAAAABcI44fP65BgwZddPu6desuui0gIED/+9//NHDgQIWFhWnp0qWyWq1q1KiRPv30U7Vt21YeHn/GBO3atdPcuXOVlJSkKlWqyNfXV9u2bVOzZs20a9cu9erVS5I0ffp0Pfroo7LZbGV3ok5AQAIAAAAAcD2GXK9Iq1G63SdNmqSxY8eqW7ducnd3l5ubm2bOnKlatWopLi5O9evXL9a/WrVqkgpDmaCgIE2ePFkjR45Ufn6+unfvrh49eig6OlpHjx5V//79Sze5CoCABAAAAACAa0R4ePglV4lcyr59+1SpUiXNnj1bISEhWr58uSZOnKiPPvpI2dnZslqtxfp7eXlJknJyciRJffv2Ve/evZWVlaWAgAAZhqHp06frySefVEJCgiZOnKijR4/qjjvu0NixY0t3ok5AQAIAAAAAgIs7duyYJkyYoA8++EBt2rSRJDVt2lR79+7V22+/LW9vb+Xm5hbb52wwcu6jM15eXkXByRdffCGr1aoePXrokUceUadOnTRo0CANGDBATZs2Vffu3a/S2ZUN3mIDAAAAAICL27Fjh/Ly8tS0adNi7c2bN9fBgwcVGhqqhISEYtvOfh8SEnLe8XJzczVz5kxNmDBBkrRlyxZ1795dNptNHTt21NatW8vpTMoPAQkAAAAAwDUZdtf6KoWwsDBJ0h9//FGsfffu3apdu7batm2rbdu2qaCgoGhbdHS0IiMjL/jq3o8++kgNGzZU69atJUlubm7Kz8+XJOXl5cl+DdZ/ISABAAAAAMDFNWvWTG3atNHEiRO1adMmHTx4UG+99Zaio6M1YsQI9evXT+np6Zo0aZL27t2rlStXauHChRo5cuR5x0pLS9O8efM0bty4YsdfvHixdu/erXXr1qlVq1ZX8/TKBAEJAAAAAAAuzs3NTXPmzFG7du30zDPP6O6779amTZv0wQcfqEWLFgoKCtK8efN04MAB9e3bV1FRUXrqqafUt2/f84717rvvqnv37qpbt25R26RJk7Rjxw4NGDBAXbp0Uc+ePa/m6ZUJirQCAAAAAGAClStX1pQpUzRlypQLbm/WrJmWLl162eM89dRT57XVqVNHK1euLPUcnYmABAAAAADgkgy74ewp4BrCIzYAAAAAAMD0CEgAAAAAAIDpEZAAAAAAAADTowYJAAAAAMAFGZLd7uxJlDFqqpQnVpAAAAAAAADTIyABAAAAAACmR0ACAAAAAABMjxokAAAAAADXZLhaDRKUJ1aQAAAAAAAA0yMgAQAAAAAApkdAAgAAAAAATI+ABAAAAAAAmB5FWgEAAAAArseQZDecPYuy5WKnU9GwggQAAAAAAJgeAQkAAAAAADA9AhIAAAAAAGB61CABAAAAALgmu93ZM8A1hBUkAAAAAADA9AhIAAAAAACA6RGQAAAAAAAA06MGCQAAAADABRkuWIPEcPYEXBorSAAAAAAAgOkRkAAAAAAAANMjIAEAAAAAAKZHDRIAAAAAgGsyqNmBkmMFCQAAAAAAMD0CEgAAAAAAYHoEJAAAAAAAwPQISAAAAAAAgOlRpBUAAAAA4HoMSXa7s2dRtqg5W65YQQIAAAAAAEyPgAQAAAAAAJgeAQkAAAAAADA9apAAAAAAAFyTnaIdKDlWkAAAAAAAANMjIAEAAAAAAKZHQAIAAAAAAEyPGiQAAAAAANdk2J09A1xDWEECAAAAAABMj4AEAAAAAACYHgEJAAAAAAAwPWqQAAAAAABckCHZDWdPooy52vlULKwgAQAAAAAApkdAAgAAAAAATI9HbMqDIRVksvQJxVmyC/9rz+bvBy7M7/bGzp4CKiBLcE1JklfbSBkR/G8b58taH+vsKQAA4BJYQQIAAAAAAEyPj6IAAAAAAK7HkAy73dmzKFssRC9XrCABAAAAAACmR0ACAAAAAABMj4AEAAAAAACYHjVIAAAAAACuyU7RDpQcK0gAAAAAAIDpEZAAAAAAAADTIyABAAAAAACmRw0SAAAAAIBrMuzOngGuIawgAQAAAAAApkdAAgAAAAAATI+ABAAAAAAAmB41SAAAAAAArsluOHsGuIawggQAAAAAAJgeAQkAAAAAADA9AhIAAAAAAGB6BCQAAAAAAMD0KNIKAAAAAHBBhmS3O3sSZYyis+WJFSQAAAAAAMD0CEgAAAAAAIDpEZAAAAAAAADTowYJAAAAAMD1GJLsLlazw8VOp6JhBQkAAAAAADA9AhIAAAAAAGB6BCQAAAAAAMD0qEECAAAAAHBNht3ZM8A1hBUkAAAAAADA9AhIAAAAAACA6RGQAAAAAAAA06MGCQAAAADANdkNZ88A1xBWkAAAAAAAANMjIAEAAAAAAKZHQAIAAAAAAEyPGiQAAAAAAJdk2O3OngKuIawgAQAAAAAApkdAAgAAAAAATI+ABAAAAAAAmB4BCQAAAAAAMD2KtAIAAAAAXI9hSHbD2bMoW4aLnU8FwwoSAAAAAABgegQkAAAAAADA9AhIAAAAAACA6VGDBAAAAADgmlytBgnKFStIAAAAAACA6RGQAAAAAAAA0yMgAQAAAAAApkcNEgAAAACAazLszp4BriGsIAEAAAAAAKZHQAIAAAAAAEyPgAQAAAAAAJgeNUgAAAAAAK7Jbjh7BriGsIIEAAAAAACYHgEJAAAAAAAwPQISAAAAAABgegQkAAAAAADA9CjSCgAAAABwPYZkuFqRVhc7nYqGFSQAAAAAAMD0CEgAAAAAAIDpEZAAAAAAAADTowYJAAAAAMA1uVoNEpQrVpAAAAAAAADTIyABAAAAAACmR0ACAAAAAABMjxokAAAAAAAXZEh2u7MnUcaoqVKeWEECAAAAAABMj4AEAAAAAACYHgEJAAAAAAAwPWqQAAAAAABck52aHSg5VpAAAAAAAADTIyABAAAAAACmR0ACAAAAAABMj4AEAAAAAACYHkVaAQAAAACux5DrFWl1sdOpaFhBAgAAAAAATI+ABAAAAAAAmB4BCQAAAAAAMD1qkAAAAAAAXJJhULQDJccKEgAAAAAAYHoEJAAAAAAAwPQISAAAAAAAgOlRgwQAAAAA4Jrs1CBBybGCBAAAAAAAmB4BCQAAAAAAMD0CEgAAAAAAYHrUIAEAAAAAuCZqkOAKsIIEAAAAAACYHgEJAAAAAAAwPQISAAAAAABgetQgAQAAAAC4HkMyXK0GiYudTkXDChIAAAAAAGB6BCQAAAAAAMD0CEgAAAAAAIDpEZAAAAAAAADTo0grAAAAAMAFGZKrFWmlSmu5YgUJAAAAAAAwPQISAAAAAABgegQkAAAAAADA9KhBAgAAAABwTXZnTwDXElaQAAAAAAAA0yMgAQAAAAAApkdAAgAAAAAATI8aJAAAAAAAl2NIMuyGs6dRplzrbCoeVpAAAAAAAADTIyABAAAAAACmR0ACAAAAAABMjxokAAAAAADXY0hysRokFCEpX6wgAQAAAAAApkdAAgAAAAAATI+ABAAAAAAAmB4BCQAAAAAAMD2KtAIAAAAAXJPd2RPAtYQVJAAAAAAAwPQISAAAAAAAgOkRkAAAAAAAANOjBgkAAAAAwCUZdsPZU8A1hBUkAAAAAADA9AhIAAAAAACA6RGQAAAAAAAA06MGCQAAAADANdmdPQFcS1hBAgAAAAAATI+ABAAAAAAAmB4BCQAAAAAAMD1qkAAAAAAAXI8hGXbD2bMoWy52OhUNK0gAAAAAAIDpEZAAAAAAAADTIyABAAAAAACmR0ACAAAAAABMjyKtAAAAAADXZHf2BHAtYQUJAAAAAAAwPVaQAH+xIztXi9IytDMnT7kyVM3dXT19vfVQZb+iPpl2u+alZmh9ZrZOF9hVy9NDD1TyVXdf7xKNEZOTp3mp6dqZkydJamj10LAAPzXzshbrF59foNeT07QjJ0/B7m4aGeCnm2zFx/hfZrZeT07TR2FVVcWdzBO42p5bF6sv/oi76PaFd7dSs9DKJe53KZl5+Zq9+YC+2ZugtJx8RQTYNLhVLfW6LqSoT9zpbL383R/65USqqvl6aXT7OuoSGVzsON/uTdDU7/7QygE3KtDH+tdhAFxFv2bm6sOkdO3MylOOYaiah7tureyjwVUL7zu2ZuTo67Rs/ZaVq4Q8u/zcLWro7akhVf3U0NuzRGPEZOXq3ycLxzAMqZGPp0ZU9VMz21/uO/IKNC0uVb9m5SnYw02PBPvrb/7F7zvWp2VrWlyqFtcJVhUP7jsAuBYCEuAc32Zk6eWkNHWxeenZoMrycbPoWH6+ThYUX5v37MlUxebk6eEAP9X09NC3Gdl6ISlVhgz18PW55Bgxaac1Oj5Zjbw89WxQJUnSJ2mZGhufopkhVdTknJDklaRU5RrSS1Ura3tOrqacTNWiMA9V9yz8p5tut2tmymk9GuBPOAI4yfA2Ebrn+vDz2h9f85us7m66vlqlK+p3KU/+Z6d+TzytMe3qqHaATf/ZHa9nvo2RYUi31i8MSZ5bH6vcArum92yircdTNPHr37Xi/htVs3Lhtel0Tr6mbdyjsR3qEY4ATvZNapZePJGqrv7emhx25r4jr0An8/+87/jsVKZSCwzdW8VXkV4eSsm3a3FyhoYfTNKbNauoja/XJceIOXVajx5OViNvT00OKwxhP0rK0JgjyXq7VqCannMdeOlEqvIMQ1OrB2h7Zq4mHz+ljyOrqob1zH1HgV1vJaRpVDV/whEALslUAcmcOXMUHR2tRYsWFbXFxsZq6tSp2rlzpwICAjRo0CANHTrUibOEsyTmF2h68mnd4eejJwP//EGllYr/ABGdlaOfsnM1JaiSup8JQ1p5WxVfUKA5p9LV1eYtd4vlouP8e/8h+bm56fXgKvJ2K+zXxtuqe4+f1OyUdL0TGihJyrYb2p6TpzlnQpMbfLz0f5mFY58NSN49la5aHu66ze/SoQyA8lOzsk9R+HDW1mMpOpWdp2Gta8v9zL/zkva7mA17jmrT0RS90qOxbj2zYqRt9So6cTpbb0bv1S31qim3wK6tx07p/btbqXloZbWvFaj/7kvUpiPJqlm5uiRpZvQ+RQTYdGejsLL6LQDggMS8Ar0Wl6Y7A3w04ZzVY63/0u/JkEoK9HAv1tbOz6p7953Uh0kZlw1I5u4uvO94s2bgn/cdNqv6709UVMJpza0dJOnMfUdmrt6tXRia3Ojrpf+lZeunjNyigGRO4mnVsnro9gBbKc8euHoMapDgCpgm+v3ggw80a9asYm0pKSkaPHiwIiIitGLFCo0ePVozZ87UihUrnDRLONOXGVnKMgw9UMn3kv2+z8yRj8Wim//yqEtvX2+dLLArJjfvkvv/lpqmFt6eRTcpkmRzc1MLL6t25ubpZEGBJClPhgxJ3ueELT4Wi3INo/A4Oblam5Gl8YGX/9QZwNW1KvaELJLuukwIUdJ+krQ+9rBsnu7qUbf44zJ3NApTYkaufotPU77dLkOSzzk/TNk83ZV7ZhXcLydS9dXuOD17c4MrPSUAZWx1auF9x8Agv0v2+2s4IhXeN0R4eSghv+Cy4+xISVNLm7XYfYevu5ta+Fj1W1aeTp45Rp5ReN/hc+59h9s59x2ZufpPapaeCuW+A4DrcvmAJD4+XsOGDdPMmTMVGRlZbNuyZctktVr1/PPPq27duurXr58eeughvffee06aLZzpl+w8VXKz6FBevgafSNLNh+PV52iCXk9OU4b9z+j5QF6+Ijzd5fGXVSJ1PT2Ltl9Knt0u6wVWmHieadufW7i/v5ubanu4a+npTJ222/V9Zrb25eWriZdV+Yah6clpGljJV7U8TbUQDKjwTufka93+RN1Qo4qqV7r46q6S9jtrX+IpRVaxycOt+P+665/54Wpfcob8vTwVGWDTol+PKC07T//bn6jdSelqHlpZeQV2vfzdHxrcqrZq8+kv4HS/ZOYW3nfk5OvBAyd106449d6ToGlxqcoouPRH3ukFdu3OzlOk9fL3AHmGXdYLLFDzPBOY7Ms5c9/h7qbaVnctTs5QWoFd353O1t6cfDX18VS+YejVuDT9I8hPtUowJgBcq1z+Cvf777+rcuXKWr16tWbPnq1jx44Vbdu6davatm0rD48/fxvatWunuXPnKikpSUFBQY4N6u4uj3r1Szt1XGUnT25Vdl6+nks+rX/Urqmmlf0Vk5aueQcO6YC7p95t1UwWi0VpiVsV7uN93p9xlZxcKS5JpwOC5BFR87zju9esJUmKrBKgmOxsudW9Tm5nQpF8u6HYhK2SpPTgEHmEVpMk/TM4VP/8LUa9jybKTdJDEbXUrE5tvX/gsGT10oMtW5z3wxKuXZbg8//e4Nrz9dY/lJ1vV98bm1zyz7Sk/SxVQiVJp3IKVKOS/3l9K3tnStqiVHebLME1NeVuL41f9p06L9goN4tFw25qpqZNrtd73++Q4e6hIbd0kMX9/E+kce1yr3PpYB4V08kjW5WjfD0bl6YH69RU0yr+iklN13u7D2m/xVP/bld433EhM37ZpSxDGty8kdwr+1+wj3uNP+87fs/OliWyXrH7jpjDhfcdp4OqyT288L7j2SohevrnGPXakyA3SYPr1VLT+rW1YO9hyWrVg22ay537DtfgWbICv4DZuHxA0rVrV3Xt2vWC2+Li4lS/fvEfcqtVK/wfxPHjxx0OSNyCqynwnXkO7QvncevZU7kHD2r0k09qxIgRkqTukiovXKhXXnlFf/xjqDp06CD3nj1lrVnzvD/j/IQE6aab5Nu3nwLP7H8hDz45XpMmTVJUaE098sgjstvtioqKUlxe4aM5lYY/rMDevSVJN0v6Li9PR44cUXBwsPz9/XXw4EF9eOedmj9/voJbtVJUVJRWrlypvLw89ezZUxMnTpSX16WfRwZQfj5f0U8BAQG67fk5slovXgS1pP3OcvOtLPfwmvIZMKlYu3dCgjTjU1lbdpXPgBFqL+n/xp1/3Zj/2jLNnz9fflw3XI7PAGfPAI6w9OypnIMH9eTYP+87ukmqdOa+I/bewvuOv3rrrbe0ds0GTZ48We0HDrzsOA+OK7zvmBXwl/uOM2/S83/wYQWcue/4my5837HwzH1HENcPXIuoQYIr4PIByaVkZ2efd1N69gKfk5Pj8HHtiQlKnTLp8h1RofglJ0mSmn6/XsnbtxS1N8/MlCRtffkFNaxdU37JSTqZdFLJjwwrtv/h9AxJkseXnxfb/yz3mrVU+Z/PqcfuGB2rG6EPli3T4sWLJUlNKvlrQPUwLTp8VD6L3lfyFyuL7RsgKU9SsqR/bt+hnoEBqjP/XX34Upw+PXBYb7dsKh93d439fJXe+mGDhtepXTa/KbiqfG6KvHwnVGi741O0c+dODbixkQo+na6sUvaTCleQeN86VJWMXCXvi1HWJ1OLbY9POCVJ8vnjB2V9kljUfraqSZakyR9+o9sa19T1u7/WkiVRWvHdr5r7j1tks3rosY/XKOrIb3q0SwsHzxrOlvPTAWdPAQ7wO1V439F803qdiv3zvqFleuF9x7ZXX1DjusVXjM3bc0jv7TmsR+rX1u3b/k+ntv3fRY/vXqOW/J98Tj0Pxeh4gwi9f859R9MAfz1QO0wf7j8q29L3derb4vcdgZIKJJ2SNGnzDvUKDlC9j9/VR6/G6dM9hxV1Y1PZ3N31+OpVmrl5g0bU577jWuP/7L/kHhR8+Y6AyZg6IPH29lZubm6xtrPBiM1WiuezCwqUv3d3aaYGJ6hjz9dOSQVHDinf+ueyw/wzNUWMpJPKz8tSZEG+/puZrew9fxSrQ7I7I1uSVPtUkvKzTl90nIIjh3V/Xpb6hQfpaF6BbG4WhXq4a3pyknwsFtVLiFP+yfgL7rsmPUsHUtP1kq9V+Xt368fEU7rZ6q6w40ckSbd5uunr48c12O54wAfnMRqa+pLsEj77YY8kqW+En4zEI6Xud656Qb5a+1u88uIPFXu0bs/ewutFXc/cCx5r9a4T2h+frNe71pOReEQbf9+r7pFBqmVPlbKlO6+rqq9iD+iRJg4+VgqnK9i/x9lTgAPq6sx9x7HDKkg6577jTE0QJSepwJJd1D7/ZLrmn0zX0Kp+GuSWU+I/94Kjh/WAJVv961bVkbx82dzcFObprtfiCu876ifHq+BUwgX3/epUpg6cStfUQC8V7N+jH46lqIuPu6rHH5Uk3e7jprVHj2uoR+4F90cFlnfplwoAZmXqhwhDQ0OVkFD8fwhnvw8JCXHGlOBEnX0KVw9tyir+P/norMKw4fozocnfbF7KMgx9l1k8hFibkaWq7m5qbC3ZM51Wi0V1rB4K9XBXfH6B1mdk63Y/H3ld5FWfpwrsmnPqtB6v4i//c344yj5TXV6SMu2FFegBXH25BXat2R2nJtX8Ve8Sb6Uoab+/6tqwljLzCrRuX2Kx9i/+iFOwr1VNQ85/s0RKVq7e/HGfJnS6Tv5ehdcmQ1JW3p9vvsjKy+e6ATjBzf6Fb8PblF78fiI648x9h8+f9xPvnwlHHgry1dCqJb9unMvqZlFdL0+FeborLq9A69KydUfAJe478u2KSjytJ0L85e9+5r7DKH7fkWXn6gHAtZj648q2bdtqyZIlKigokPuZgnXR0dGKjIx0vEArrlk3+Hipo49VC1PTZcjQ9V6e2pWTr/fT0tXB26pm3oWPY7Xz8VJbb6veSElThmFXDQ8P/TcjW5uzczU5qJLcz1lVsj07V2MTUvRgZV8NP9O2Lz1D61PT1cDqKatF2pubr4/SMlTD013DKl/8FcNRKafVyOqprr5/vl74Bh+rolLSdb3VUz5uFq04nanb/S7/NgwAZe9/+xOVmpOvMY3DS9Vv67EUPbz6Vw1vU1sj2/752FWn66qrXY0qeuX73crIK1DNyj5auydePx5O1tTujeR+gR9y3vhhr5pU89ct9aoVtXWoGagZP+5Vs9BKsnm6a/GOY+rb+PKvGQZQtm709VInPy+9n5Quu6QmPp6Kzc7TgpPp6ujrpea2wvuOT5Iy9N7JdLXztaqDn5d2/uWDnCY+fz4uvj0zV2MOJ2twVT8Nr1PYtu90htadTFdDbw9ZLRbtyc7XR8npqmF11/Dgi4ctsxLS1Njbqm7nvGXrBj8vRSWcVhMfq3wsFi1PyVSfAO47UIEZkuFqNUjIJcuVqQOSfv36ad68eZo0aZKGDRumHTt2aOHChXrhhRecPTU4yQtBAXo/LV2r07P0fmqGqrq76V5/mwZXLn4D8XLVynrvVLrmn8rQabtdtTw9NCWosrqfE16cVSDpnA9b5Olm0bbsXH16OlNZdkMhHu66y8+mByr5yucin+Jszc7R91k5+jCseHB3m6+PjucX6N1T6cqXoc4+3vpHpYuHLADKz6rYE/LxcFfPc8IIR/sVGEax68ZZb9zaRFGbDuidLQeUmp2niCo2/atHY/W67vxVj5uPJmv9/pP69P4birXf1ShMR9Oy9Pam/corsKtb3WANbU39AMAZXgoP0PyT6Vp9KlMLTtpV1cNN9wX6asg5q8t+SC98zGZTRq42ZSSfd4wfG4YW/dowDBVIsp9zAfG0WLQtI0fLkzOUZZy57wiwaVCQr3wu8kaarRk5+u50jj6qU7VYe5/KPjqeW6B3Ek4rzzB0s7+3HrqClXAAUNFZDONCt2Cu6emnn9axY8e0aNGiorYdO3Zo6tSpiomJUXBwsIYMGaKBJagIfikFJ44r6R/3lXa6cDEe9eor8J15Sn5kGDVqcEF+tzd29hRQAVmCC99ck/XJ1BLXK4G5ZK2PdfYUUAG517lOAW/O06mxw6hTg/MEzF0s99BLr3h0BQUnjitlyP3OnkaZqrJgsdzDXP/PzllMtYLk1VdfPa+tWbNmWrp0qRNmAwAAAAAAKgpTF2kFAAAAAACQTLaCBAAAAABgIq5WpBXlihUkAAAAAADA9AhIAAAAAACA6RGQAAAAAAAA06MGCQAAAADAJRnUIMEVYAUJAAAAAAAwPQISAAAAAABgegQkAAAAAADA9KhBAgAAAABwPYYL1iAxnD0B18YKEgAAAAAAYHoEJAAAAAAAwPQISAAAAAAAgOlRgwQAAAAA4HIMuV4NEkqQlC9WkAAAAAAAANMjIAEAAAAAwCRWrVql3r17q2nTprrtttv0n//8p2hbbGysBg4cqBYtWujmm2/W/Pnzi+27YcMGdevWTTfccINee+21Ytvi4+PVoUMHJSUlXZXzKA8EJAAAAAAAmMDnn3+uf/7zn/r73/+uL7/8Ur1799a4ceO0fft2paSkaPDgwYqIiNCKFSs0evRozZw5UytWrJAk2e12TZw4USNGjNCHH36oNWvW6Lvvvis69ltvvaUBAwYoKCjIWadXatQgAQAAAAC4JsPi7BlUGIZhaObMmXrwwQf14IMPSpIee+wx/fzzz9qyZYu2bNkiq9Wq559/Xh4eHqpbt64OHTqk9957T/369VNKSoqSkpLUt29fWa1WtWrVSnv27FHnzp31xx9/aOPGjfr666+dfJalQ0ACAAAAAMA14vjx4xo0aNBFt69bt+6C7fv379exY8fUp0+fYu1nH6MZPny42rZtKw+PP2OCdu3aae7cuUpKSlKVKlXk6+urbdu2qVmzZtq1a5d69eolSZo+fboeffRR2Wy20p6eU/GIDQAAAAAALu7gwYOSpMzMTA0dOlTt27dX//79tX79eklSXFycQkNDi+1TrVo1SYWhjJubmyZPnqyRI0eqbdu2uu6669SjRw9FR0fr6NGj6t+//1U9n/LAChIAAAAAAK4R4eHhF10lcinp6emSpIkTJ2rUqFEaP368vv76az366KN6//33lZ2dLavVWmwfLy8vSVJOTo4kqW/fvurdu7eysrIUEBAgwzA0ffp0Pfnkk0pISNDEiRN19OhR3XHHHRo7dmwpz/TqIyABAAAAAMDFeXp6SpKGDh2qvn37SpIaNWqkmJgYvf/++/L29lZubm6xfc4GI+c+OuPl5VUUnHzxxReyWq3q0aOHHnnkEXXq1EmDBg3SgAED1LRpU3Xv3v1qnFqZ4REbAAAAAIBLMuyu9VUaZx+fqV+/frH2evXq6ejRowoNDVVCQkKxbWe/DwkJOe94ubm5mjlzpiZMmCBJ2rJli7p37y6bzaaOHTtq69atpZuwExCQAAAAAADg4ho3bixfX1/9+uuvxdp3796tWrVqqW3bttq2bZsKCgqKtkVHRysyMvKCr+796KOP1LBhQ7Vu3VqS5Obmpvz8fElSXl6e7PZSJjpOQEACAAAAAICL8/b21rBhwzR79mx9+eWXOnz4sN555x398MMPGjx4sPr166f09HRNmjRJe/fu1cqVK7Vw4UKNHDnyvGOlpaVp3rx5GjduXFFbs2bNtHjxYu3evVvr1q1Tq1atrubplQlqkAAAAAAAYAKPPvqofHx89Oabbyo+Pl5169bV22+/rRtvvFGSNG/ePE2dOlV9+/ZVcHCwnnrqqaJ6Jed699131b17d9WtW7eobdKkSRo/fry+/PJL3XXXXerZs+dVO6+yQkACAAAAAHBJht3i7ClUOIMHD9bgwYMvuK1Zs2ZaunTpZY/x1FNPnddWp04drVy5stTzcyYesQEAAAAAAKZHQAIAAAAAAEyPgAQAAAAAAJgeNUgAAAAAAK7HkIxr702zl2Y4ewKujRUkAAAAAADA9AhIAAAAAACA6RGQAAAAAAAA06MGCQAAAADAJRmGxdlTwDWEFSQAAAAAAMD0CEgAAAAAAIDpEZAAAAAAAADTIyABAAAAAACmR5FWAAAAAIBLMuzOngGuJawgAQAAAAAApkdAAgAAAAAATI+ABAAAAAAAmF651CBJT0/X999/r4SEBDVu3Fg33HBDeQwDAAAAAMBFGXaLs6eAa4jDAcmaNWv03nvvacCAAerfv39R+759+zR06FDFx8cXtd1555169dVXSzdTAAAAAACAcuLwIzZr1qzRrl271KZNm2Ltr7zyiuLi4lSzZk1169ZNNptNn3/+ub777rtSTxYAAAAAAKA8OByQ7Nq1S5UrV1ZkZGRRW0JCgn788UeFh4friy++UFRUlN59910ZhqGPP/64TCYMAAAAAABQ1hx+xCY5OVkRERHF2jZv3izDMHT77bfLy8tLktS2bVuFh4dr3759pZooAAAAAAAlZkiG4exJlDFXO58KxuEVJHl5eSooKCjWtnXrVlksFt14443F2qtWrarExERHhwIAAAAAAChXDgckISEhOnz4sLKysoraNmzYIHd3d7Vq1apY34yMDPn7+zs+SwAAAAAAgHLkcEDSoUMHZWdn66WXXtLu3bv19ttv6/jx42rXrp18fHyK+mVnZ+vQoUMKDQ0tkwkDAAAAAACUNYdrkDz88MP65ptv9Nlnn+mzzz6TYRjy8PDQmDFjivVbv3698vPzz3vbDQAAAAAA5cWQRYbd4uxplClDrnU+FY3DAUl4eLhWrFihBQsW6NChQwoLC9MDDzyghg0bFuu3ZcsWNWzYUN26dSv1ZAEAAAAAAMqDwwGJJFWvXl2TJ0++ZJ/nn3++NEMAAAAAAACUO4drkAAAAAAAALiKUq0gOVdqaqoyMzNlXOJF0+Hh4WU1HAAAAAAAQJkpVUBy4MABRUVFacOGDTp9+vQl+1osFsXExJRmOAAAAAAASszVirSifDkckMTGxmrgwIFFq0a8vLwUGBgoi4W/gAAAAAAA4NricEAyY8YMZWRkqH379nrmmWdUv379spwXAAAAAADAVeNwQLJ9+3bZbDbNnj1bNputLOcEAAAAAABwVTkckNjtdkVGRhKOAAAAAAAqpEu8QwQ4j8Ov+W3UqJESExPLci4AAAAAAABO4XBAMmLECCUmJmrVqlVlOB0AAAAAAICrz+FHbDp37qwpU6bohRdeUExMjO655x7VqlVL3t7eZTk/AAAAAACAcudwQNKoUaOiXy9atEiLFi26ZH+LxaKYmBhHhwMAAAAA4IoYdouzp4BriMMBiXGF1W6utD8AAAAAAIAkbdu2TRs3btTmzZsVFxenpKQkFRQUqHLlyoqIiFCrVq3UtWtXtWzZ0uExHA5Idu3a5fCgAAAAAAAAl2K327Vy5UrNmzdPhw4duuDCi6SkJCUlJWnbtm2aN2+e6tWrp8cee0y9evW64vEcDkgAAAAAAADKw/bt2/Xcc89pz549CggI0G233aYbbrhBDRo0UI0aNeTv7y+73a7k5GQlJCRo+/bt2rJlizZt2qSxY8fqnXfe0UsvvaRmzZqVeEwCEgAAAACA6zEkw3CxGiQmqVwxc+ZMzZ07Vw0bNtQbb7yhW265RZ6enhfsGx4ervDwcLVo0UKDBw9Wenq6PvvsMy1evFgDBgzQww8/rFGjRpVo3DILSA4cOKCDBw8qIyNDvr6+ioiIUGRkZFkdHgAAAAAAmMCnn36qV199VXfccccV7+vn56dBgwZp4MCBWrZsmWbNmnX1ApIlS5bovffe0/Hjx8/bVr16dY0YMUL33ntvaYcBAAAAAAAmsHbtWvn6+pbqGBaLRX//+9912223lXifUgUkzzzzjFatWiXDMGS1WhUWFqagoCAlJSXpxIkTOnr0qKZMmaLt27frX//6V2mGAgAAAAAAJlDacORcfn5+Je7rcEDyxRdf6LPPPpOPj49GjRql++67r9hJZGRkaMmSJZo9e7ZWrVqljh076vbbb3d0OAAAAAAArohhd/YMcC1xc3THZcuWyWKx6O2339bQoUPPS3h8fX01dOhQzZo1S4ZhaNmyZaWeLAAAAAAAwNdff6377rtPN954ozp06KAHH3xQmzZtKtUxHQ5I/vjjD9WsWVOdOnW6ZL9OnTqpdu3aio2NdXQoAAAAAAAASdKCBQv0+OOP6+DBg4qIiFDVqlW1bds2DR48WGvWrHH4uA4HJDk5OfL39y9RX19fX+Xm5jo6FAAAAAAAgAzD0Ny5c9W7d29t2LBBS5cu1erVq/Wf//xHVapU0ezZsx0+tsMBSVhYmPbs2aPk5ORL9ktOTtbevXsVFhbm6FAAAAAAAMAkvv/++4tuO336tFJTU9WzZ095enoWtdesWVMtWrTQ4cOHHR7X4YCka9euys3N1dixYy8akiQlJemJJ55QXl6eunXr5vAkAQAAAAC4EoYku2FxqS/D2b+pV8mIESP04IMP6rfffjtvm7+/vypXrqzly5fr1KlTRe3bt2/X5s2bFRER4fC4Dr/FZsSIEVqzZo22bNmiLl26qFevXqpbt27Ra3737duntWvXKicnR2FhYRo+fLjDkwQAAAAAAOYwf/58TZ8+Xffee6969eqlsWPHqlatWpIki8WiMWPG6KWXXlLHjh1VpUoV5eXlKS0tTRaLRWPHjnV4XIcDkoCAAC1cuFDjxo3T77//rs8//1wWi6Vou2EUZltNmzbVG2+8oYCAAIcnCQAAAAAAzKFjx47q2LGjPv/8c82aNUu9e/fWvffeq8cee0xBQUF64IEH1KBBAy1atEj79++XYRjq2LGjHnjgAbVu3drhcR0OSCSpdu3aWrFihaKjo7Vx40YdPHhQmZmZstlsioiIUKdOndS+ffvSDAEAAAAAAEzozjvv1K233qqPP/5Y7777rlatWqUhQ4ZoyJAhatOmjdq0aVOm4zkckBw/flySFBoaqvbt2xOEAAAAAAAqFMOwXL4TKjSr1arBgwerf//+evfddzVv3jwtXrxYjz76qP7+97/Lw6NU6z6KKVWR1nvvvbfMJgIAAAAAAHAhfn5+Gj9+vNauXau//e1vmjp1qm699VatWbOmzMZwOCDx9/dXeHi43NwcPgQAAAAAAMB5Tpw4oenTp+vRRx/VhAkTtGTJEtntdoWGhupf//qXPv/8c9WpU0fjxo3TPffco+jo6FKP6fBalPr16+vQoUOlngAAAAAAAMBZO3fu1KBBg5SVlVXU9uWXX+qrr77Shx9+KIvFouuuu05z587Vli1b9Prrr2vIkCHq0KGDJkyYoIYNGzo0rsPLP/7xj3/o5MmT+vTTTx09BAAAAAAA5cawW1zqyyymTZsmSZo7d65++eUX/fjjjxo4cKC2bt2qzz//vFjfG264QcuWLdOMGTN09OhR3X333Q6P63BA0rNnTz355JN68cUX9corr+j3339Xdna2wxMBAAAAAAD4/fff1aFDB3Xu3Fne3t4KDAzU2LFjZRiGduzYccF9br31Vn311VeaNGmSw+M6/IhNo0aNin69aNEiLVq06JL9LRaLYmJiHB0OAAAAAACYgI+Pj/bs2aPc3FxZrVZJhY/dSFJgYOBF9/Pw8NADDzzg8LgOBySGYZRrfwAAAAAAYD533HGHFixYoJ49e6p58+bKyclRdHS0fHx8dMcdd5TbuA4HJLt27SrLeQAAAAAAUKb4nP7a9OSTT8pms2nRokVau3at3Nzc1LBhQz3zzDOqVatWuY3rcEACAAAAAABQ1tzd3TVq1CiNGjVKycnJ8vf3l6enZ7mPS0ACAAAAAAAqpEvVHClrDr/FBgAAAAAAoKxNnTpV6enppT5OSkqKXnjhhRL3L5O32JQEb7EBAAAAAACXs3HjRn355Zd69NFHde+998rLy+uK9j916pSWLVumefPmKSQkpMT78RYbAAAAAIDrMSwy7BZnz6JsGS52Phfx2Wef6dVXX9Urr7yiqKgo9enTR927d1erVq2KXvv7VykpKdq6dau+/vprffPNN8rLy9OAAQP01FNPlXjccnmLTVZWlg4dOqTly5dr5cqVevrpp/X3v//d0aEAAAAAAIBJeHt76/nnn9ddd92l119/XR999JE+/vhjubu7q1atWgoPD5efn5/sdrtSUlKUkJCgw4cPSypcnNGpUyeNGTNGzZo1u6Jxy6VIq4+Pjxo2bKjJkyerSZMm+uc//6nQ0FB17ty5PIYDAAAAAAAupkWLFvroo4+0fft2ffbZZ9q4caP279+v/fv3n9e3atWq6tq1q+65554rDkbOKve32PTt21czZszQv//9bwISAAAAAABwRVq2bKmWLVtKko4ePaoTJ04oKSlJ+fn5qly5siIiIlSzZs1Sj3NVXvMbHBys2NjYqzEUAAAAAACSJLtJanaYSY0aNVSjRo1yOXa5v+Y3MzNTBw4ckJsbbxQGAAAAAAAVU7mmFvv27dOYMWOUnZ2tVq1aledQAAAAAAAADnP4EZtu3bpddJthGEpJSVF2drYMw5DNZtO4ceMcHQoAAAAAAKBcORyQHDt27LJ9/P391alTJ40ePVp16tRxdCgAAAAAAK6YQQ0SXAGHA5J169ZddJvFYpGPj4+qVKni6OEBAAAAAACuGocDkurVq5flPAAAAAAAAJyGV8sAAAAAAIAKa+jQoVqzZo1yc3PLdRyHV5Cc68SJE9q6davi4+OVnZ2tUaNGFW3Ly8uTYRiyWq1lMRQAAAAAAJdlSDIMZ8+ibLnY6ZTYDz/8oB9//FH+/v66/fbb1bdvXzVt2rTMxylVQJKcnKwXX3xR33zzjYxz/uadG5A888wz+uqrr7R8+XI1adKkNMMBAAAAAACTWbp0qVasWKG1a9fqk08+0eLFi1WvXj3169dPffr0UVBQUJmM4/AjNunp6Ro0aJDWrl2rkJAQ9e3bVyEhIef169+/vwzD0LfffluqiQIAAAAAAPNp3ry5XnzxRW3cuFEzZsxQx44dtX//fr366qvq3LmzHnvsMf33v/9VQUFBqcZxeAXJvHnztG/fPt1yyy2aNm2avL29NWDAAMXHxxfr17ZtW3l7e2vz5s2lmigAAAAAADAvq9Wq3r17q3fv3kpMTNTq1av1zTffaP369Vq/fr2qVKmiO++8U/369VO9evWu+PgOryD5+uuvZbVaNXXqVHl7e198ADc31apVS8ePH3d0KAAAAAAAgCLBwcEaOnSoXnnlFT3wwAOyWCxKTk7W+++/rz59+uihhx7Sb7/9dkXHdHgFybFjxxQRESF/f//L9vXx8dGBAwccHQoAAAAAgCtmNyzOngLKQXx8vFavXq3Vq1dr7969kiRfX1/17t1brVu31n/+8x99//33uv/++/XWW2+pe/fuJTquwwGJl5eXMjIyStQ3MTGxREEKAAAAAADAX2VmZuqbb77RqlWrtGXLFhmGIcMw1LZtW/Xr10+9evUqerrlzjvv1Jo1azRu3Di98cYb5R+Q1KtXTzt27NCxY8dUvXr1i/aLjY3V8ePHddNNNzk6FAAAAAAAMKnx48dr3bp1ys7OlmEYqlatmvr27at+/fqpVq1aF9ynd+/eeuaZZ5SQkFDicRwOSO644w5t375dzz33nKKiouTj43Nen9TUVE2aNEkWi0V33XWXo0MBAAAAAACT+vLLL+Xh4aHu3bvrnnvu0U033SQ3t0uXVM3JyVHjxo3VqlWrEo/jcEBy77336quvvtIPP/ygPn36qFevXkpKSpIkffrpp9qzZ49Wr16tlJQUdezYUbfddpujQwEAAAAAcMUMapC4hIkTJ+rOO+9UYGBgiffx8vLS4sWLr2gch99i4+7urnfffVe9e/fW0aNHNX/+fB06dEiGYWjy5MlauHChUlJSdOutt+rtt992dBgAAAAAAGBigwcPVmBgoHbt2qU1a9YU27Zjxw5NmzZNsbGxpR7H4RUkkuTn56cZM2Zo5MiR+vbbb7V7926lp6fLZrOpbt266tGjh5o0aVLqSQIAAAAAAPOaMWOG3nvvPdWvX1+9e/cuat+1a5cWLFighQsX6oknntDw4cMdHqNUAclZDRo0UIMGDcriUAAAAAAAAEXWrFmjf//73woKClK/fv2KbevWrZvy8/M1Z84czZgxQ7Vq1VLPnj0dGsfhR2wAAAAAAKiwDMlwsS8Zzv5NdY5FixbJx8dHS5cu1T/+8Y9i24KCgjRgwAAtXrxYVqtVCxcudHicMllBIhW+sSYzM1OGcfE/sfDw8LIaDgAAAAAAmMD+/ft14403qkaNGhftU7NmTbVp00Y///yzw+OUKiA5cOCAoqKitGHDBp0+ffqSfS0Wi2JiYkozHAAAAAAAMJlLLcQ4l5eXV6nGcTggiY2N1cCBA4tWjXh5eSkwMFAWC69RAgAAAAAAZaNu3brasmWLTp48qapVq16wT2pqqn766SfVrVvX4XEcDkhmzJihjIwMtW/fXs8884zq16/v8CQAAAAAAChrdoMP8F1Bv3799Oyzz2rYsGF6+eWXz3tb7h9//KHnnntO6enpuvvuux0ex+GAZPv27bLZbJo9e7ZsNpvDEwAAAAAAALiYe+65R+vWrdP//vc/9e/fX0FBQUU1TuPi4pSYmCjDMNSlSxfdf//9Do/jcEBit9sVGRlJOAIAAAAAAMrV7Nmz9eGHH2rx4sU6dOiQTp48WbQtNDRUAwcO1JAhQ0pV9sPhgKRRo0Y6cuSIwwO7NIvk4c9SLhTn7nvOf/n7gQs4MW+/s6eACsjayE01B0gnVx1Tbix/R3C+gDrOngEAAOXPzc1NDz30kB566CHFx8crMTFRBQUFCg4OLrM35ro5uuOIESOUmJioVatWlclEAAAAAAAALickJERNmjRR8+bNyywckUqxgqRz586aMmWKXnjhBcXExOiee+5RrVq15O3tXWaTAwAAAADAEYYkw8WKtJbsZbeuKz4+XseOHVNubm6xV/8ahqGcnBydPHlS69ev1zvvvOPQ8Uv1iM1ZixYt0qJFiy7Z32KxKCYmxtHhAAAAAACACeXm5mr8+PH69ttvy3Uchx+xMQzjir7sdntZzhsAAAAAAJjAggUL9M0338jd3V0NGzZUWFiYJKlNmzaqX7++3N3dZRiGIiMjNWvWLIfHcXgFya5duxweFAAAAAAAoCTWrl0rNzc3LVy4UK1bt9aXX36pCRMmaMqUKapXr57i4+M1atQoxcbGFoUnjnB4BQkAAAAAABWZ3bC41JdZHT58WM2aNVPr1q0lSc2aNZNhGPr5558lFRZtnTlzpiTp/fffd3gcAhIAAAAAAFBh5eXlKTQ0tOj7mjVrytPTU3/88UdRW3h4uFq2bFmq2qcEJAAAAAAAoMIKDAzUqVOnir63WCyqXr269uzZc16/uLg4h8chIAEAAAAAABVW8+bN9fPPP+vIkSNFbfXq1dPOnTuVmZlZ1LZnzx75+vo6PA4BCQAAAADAJRku9mVW999/v3Jzc9W/f3999NFHkqTevXsrMzNTo0eP1vr16zV58mQdOHBA119/vcPjEJAAAAAAAIAKq3379po4caIyMjK0fft2SVKvXr3UtGlT/fDDD3rssce0fPlyubu7a/To0Q6P4/BrfgEAAAAAAK6GwYMH6/bbb9fJkyclSW5ubvrwww81f/58/fLLLwoMDNTAgQPVpEkTh8cgIAEAAAAAABXWjBkzVLduXd15550KDg4uavfx8dGoUaPKbBwCEgAAAACAC7LIblicPYky5mrnUzJLlixReHi47rzzznIdp8wCktTUVGVmZsowLl46Jjw8vKyGAwAAAAAAJpCXl6fq1auX+zilCkgOHDigqKgobdiwQadPn75kX4vFopiYmNIMBwAAAAAATOb222/X6tWrtWPHDjVr1qzcxnE4IImNjdXAgQOLVo14eXkpMDBQFos5l/wAAAAAAICy16FDB/3000+6//771aJFC9WvX1+VKlWSm9v5L+a1WCwaM2aMQ+M4HJDMmDFDGRkZat++vZ555hnVr1/f0UMBAAAAAFDmDJerQWJOY8eOlcVikWEY2rZtm7Zt23Zen7PbnRKQbN++XTabTbNnz5bNZnP0MAAAAAAAABf12GOPXZWnVRwOSOx2uyIjIwlHAAAAAABAuRk9evRVGef8B3ZKqFGjRkpMTCzLuQAAAAAAADiFwytIRowYoYcfflirVq3SXXfdVYZTAgAAAAAAKLRq1aor6u9oRuFwQNK5c2dNmTJFL7zwgmJiYnTPPfeoVq1a8vb2dvSQAAAAAACUCUOS3dmTKGOGsyfgJE8//XSJapCcLdJ61QOSRo0aFf160aJFWrRo0SX7WywWxcTEODocAAAAAAAwobvuuuuCAUlBQYFSU1O1c+dOJSUl6bbbblOrVq0cHsfhgMQwriy7utL+AAAAAAAAr7766iW35+fn69VXX9Xy5cs1fPhwh8dxOCDZtWuXw4MCAAAAAACUBQ8PD/3zn//U+vXrNXPmTL3zzjuOHaeM5wUAAAAAQIVg6PJ1K+Aa3Nzc1KRJE0VHRzt+jDKcDwAAAAAAgFMcOXJE+fn5Du9fJitITpw4oa1btyo+Pl7Z2dkaNWpU0ba8vDwZhiGr1VoWQwEAAAAAABOx2y/+PqKCggIlJyfrgw8+UGxsrNq2bevwOKUKSJKTk/Xiiy/qm2++KVaE9dyA5JlnntFXX32l5cuXq0mTJqUZDgAAAAAAmMz1119fon4Wi0UjRoxweByHH7FJT0/XoEGDtHbtWoWEhKhv374KCQk5r1///v1lGIa+/fZbhycJAAAAAMCVshuu9WVWhmFc9EuSbDabWrVqpaioKN10000Oj+PwCpJ58+Zp3759uuWWWzRt2jR5e3trwIABio+PL9avbdu28vb21ubNmx2eJAAAAAAAMKer9RZdh1eQfP3117JarZo6daq8vb0vPoCbm2rVqqXjx487OhQAAAAAAIBOnTpV7PukpCTt3bu3TI7tcEBy7NgxRUREyN/f/7J9fXx8lJKS4uhQAAAAAADAxGJjY9W3b1+NHj26WHt0dLT69Omju+++W4cOHSrVGA4HJF5eXsrIyChR38TExBIFKQAAAAAAlBW7LC71ZVb79+/XgAEDFBsbe962qlWrqkmTJoqJiVH//v11+PBhh8dxOCCpV6+e4uLidOzYsUv2i42N1fHjx0tcdRYAAAAAAOCs2bNnKysrS88995wWLVpUbFu7du20fPlyTZ48WWlpaYqKinJ4HIcDkjvuuEMFBQV67rnnlJWVdcE+qampmjRpkiwWi+666y5HhwIAAAAAACa1fft2NWnSRAMGDLhonwceeEANGzbUDz/84PA4Dr/F5t5779VXX32lH374QX369FGvXr2UlJQkSfr000+1Z88erV69WikpKerYsaNuu+02hycJAAAAAADMKSkpSc2aNbtsv9q1a2vfvn0Oj+NwQOLu7q53331Xzz33nNasWaP58+cXvYN48uTJRb++9dZbNXXqVIcnCAAAAAAAzCs0NFS///67DMOQxXLxWix79uxRcHCww+M4HJBIkp+fn2bMmKGRI0fq22+/1e7du5Weni6bzaa6deuqR48eatKkSWmGAAAAAADAIYaJC5u6kq5du+qDDz7QjBkz9OSTT16wz9y5c3XgwAHdd999Do9TqoDkrAYNGqhBgwZlcSgAAAAAAIAiQ4cO1RdffKF58+bpu+++080336ywsDBJUlxcnDZs2KDY2FgFBgbq4YcfdnicMglIAAAAAAAAykPVqlU1f/58TZw4Ubt27dLu3buLHrU5W97juuuu0xtvvKGQkBCHxylRQHL8+HGHBzhXeHh4mRwHAAAAAACYR4MGDfTZZ59p+/bt2rJlixITE5Wfn6/g4GC1bt1a7du3L/UYJQpIunbteslCKCVhsVgUExNTqmMAAAAAAFAShiS7sydRxgxnT8DJLBaLWrRooVatWhW1paenF60iKS23knQKDw9XWFjYBb/c3d1lGIYMw5C7u7uCgoLOawsLC1NoaGiZTBgAAAAAAJjLiRMnNGrUKI0cObJY+4YNG9S+fXuNHj1aycnJpRqjRCtI1q9ff8H2F198UcuWLdOgQYM0YMAARUREyGKxyDAMHTp0SB9//LGWLFmiLl26aPLkyaWaKAAAAAAAMJ/4+Hj1799fJ0+e1HXXXVdsm9VqVUBAgL799lv9/vvvWr58uYKCghwap0QrSC7k448/1uLFizVt2jRNmjRJkZGRRY/hWCwWRUREaNKkSXrttdf0ySef6JNPPnF0KAAAAAAAYFJz5szRyZMnNWzYMK1YsaLYtm7duun777/XsGHDdPz4cUVFRTk8jsMBydKlSxUWFqbevXtfsl/v3r0VFhampUuXOjoUAAAAAABXzJDFpb7MKjo6WvXq1dP48eNltVrP2+7m5qbx48crIiJC//vf/xwex+GA5PDhwwoMDCxR38DAQB06dMjRoQAAAAAAgEnFx8erXr16l+1Xv359nTx50uFxHA5IbDab9u7dq7S0tEv2S0tL0969e+Xj4+PoUAAAAAAAwKSCgoK0b9++y/Y7evSoqlSp4vA4DgckHTp0UHZ2tsaPH69Tp05dsE9qaqrGjx+vnJwcderUydGhAAAAAACASXXq1El79+69ZG3T1atXKyYmRh06dHB4nBK9xeZCxo0bpw0bNmjDhg3q0qWLevXqpTp16igwMFDJycnav3+/1q5dq6ysLAUEBOiJJ55weJIAAAAAAFwpu7MngDIxbNgwffXVV3rppZf0/fffq0uXLgoLC5NU+PjN999/r//+97/y8fHRww8/7PA4Dgck4eHh+uSTTzRhwgTFxMTos88+K3qLjSQZhiFJaty4saZNm6bq1as7PEkAAAAAAGBOtWrVUlRUlCZOnKj/+7//03fffVdsu2EYCgoK0htvvKHIyEiHx3E4IJGkunXrauXKlYqOjtbGjRt18OBBZWZmymazKSIiQp06dVL79u1LMwQAAAAAADC59u3ba+3atVq3bp22bNmixMRE5efnKzg4WK1bt1bv3r1ls9lKNUapApJzJ0oQAgAAAAAAyovNZlOfPn3Up0+fcjm+w0VaAQAAAAAAKpKYmBiH9y3RCpLjx487PMC5wsPDy+Q4AAAAAABcDkVaXcfvv/+upUuX6ujRo8rNzS2qeypJdrtdOTk5SkpKUmJiosMhSYkCkq5duxYrwOoIi8VSqiQHAAAAAACYz44dOzRw4EDl5eUVBSMWi6VYSHL2+/r16zs8TokCkkut/EhISFB+fn7hwTw8FBAQoFOnThVrq1atmsMTBAAAAAAA5jVv3jzl5uaqa9eu6tevnzZs2KBly5Zpzpw5Kigo0IYNG7R8+XLVq1dPn376qcPjlCggWb9+/QXbX3zxRS1btkyDBg3SgAEDFBERUZTaHDp0SB9//LGWLFmiLl26aPLkyQ5PEgAAAAAAmNPPP/+sqlWr6q233pLValVAQICWLFkiNzc3denSRd27d1fDhg314osvatGiRRo6dKhD4zhcpPXjjz/W4sWLNW3aNE2aNEmRkZFFj+FYLBZFRERo0qRJeu211/TJJ5/ok08+cXQoAAAAAACumCGLS32Z1alTp9SkSRNZrVZJUoMGDSRJv/32W1Gf+++/X2FhYfr2228dHsfhgGTp0qUKCwtT7969L9mvd+/eCgsL09KlSx0dCgAAAAAAmJS3t7c8PT2Lvvfz81OVKlW0b9++Yv0aN26so0ePOjyOwwHJ4cOHFRgYWKK+gYGBOnTokKNDAQAAAAAAk6pdu7Z27959XttfXwSTk5OjjIwMh8dxOCCx2Wzau3ev0tLSLtkvLS1Ne/fulY+Pj6NDAQAAAAAAk7r55pt1+PBh/etf/1J6erokqWXLljp8+LD+7//+T5J04MABbdmyRdWrV3d4HIcDkg4dOig7O1vjx4/XqVOnLtgnNTVV48ePV05Ojjp16uToUAAAAAAAXBFDkt3iWl/GZc/aNT344IOqUaOGPvzwQ40bN06SNGDAALm7u2vUqFG6++67dffddys3N1e33Xabw+OU6C02FzJu3Dht2LBBGzZsUJcuXdSrVy/VqVNHgYGBSk5O1v79+7V27VplZWUpICBATzzxhMOTBAAAAAAA5lSpUiUtXbpUc+bMUeXKlSVJNWvW1Kuvvqrnnnuu6FGbrl27OvwGG6kUAUl4eLg++eQTTZgwQTExMfrss8+K3mIjSYZRmG01btxY06ZNK9UyFwAAAAAAYF6BgYF69tlni7Xdfvvt6tq1q/bs2aPAwEDVrFmzVGM4HJBIUt26dbVy5UpFR0dr48aNOnjwoDIzM2Wz2RQREaFOnTqpffv2pZogAAAAAADAhdhsNjVv3rxMjlWqgOSs9u3bE4QAAAAAACoQi+yyXL7bNcXVzqdicbhIKwAAAAAAgKso0QqS48ePF3b28FC1atWKtV2J8PDwK94HAAAAAACgvJUoIOnatassFovq1Kmjr776qlhbSVkslqLKsgAAAAAAABVJiQKSsys/goODz2sDAAAAAKAiMpw9AVxTShSQrF+/vkRtAAAAAAAA1yKKtAIAAAAAANMjIAEAAAAAAKZXokdsSiI1NVWZmZkyjIs/5UXdEgAAAAAAUBGVKiA5cOCAoqKitGHDBp0+ffqSfXmLDQAAAADgarI7ewK4pjgckMTGxmrgwIFFq0a8vLwUGBh4Ra/+BQAAAAAAqAgcDkhmzJihjIwMtW/fXs8884zq169flvMCAAAAAAC4ahwOSLZv3y6bzabZs2fLZrOV5ZwAAAAAAACuKocDErvdrsjISMIRAAAAAECFY0iyu1gJiIu/EgVlweHX/DZq1EiJiYllORcAAAAAAACncDggGTFihBITE7Vq1aoynA4AAAAAAMDV5/AjNp07d9aUKVP0wgsvKCYmRvfcc49q1aolb2/vspwfAAAAAABAuStRQNKoUaNLbl+0aJEWLVp0yT4Wi0UxMTElnxkAAAAAAKVAzQ5ciRIFJIZR+r9WZXEMAAAAAACA8lCigGTXrl3lPQ8AAAAAAACncbhIKwAAAAAAgKtwuEgrAAAAAAAVmd3ZE8A1hRUkAAAAAADA9AhIAAAAAACA6RGQAAAAAAAA0yMgAQAAAAAApkeRVgAAAACAS7JbnD0DXEtYQQIAAAAAAEyPgAQAAAAAAJgeAQkAAAAAADA9apAAAAAAAFyOIcku1ypCYjh7Ai6OFSQAAAAAAMD0CEgAAAAAAIDpEZAAAAAAAADTowYJAAAAAMAlUbMDV4IVJAAAAAAAwPQISAAAAAAAMJEDBw6oZcuWWrlyZVFbbGysBg4cqBYtWujmm2/W/Pnzi+2zYcMGdevWTTfccINee+21Ytvi4+PVoUMHJSUlXZX5lxcCEgAAAAAATCIvL0/jx49XZmZmUVtKSooGDx6siIgIrVixQqNHj9bMmTO1YsUKSZLdbtfEiRM1YsQIffjhh1qzZo2+++67ov3feustDRgwQEFBQVf9fMoSNUgAAAAAAC7JbnH2DCqet99+W76+vsXali1bJqvVqueff14eHh6qW7euDh06pPfee0/9+vVTSkqKkpKS1LdvX1mtVrVq1Up79uxR586d9ccff2jjxo36+uuvnXRGZYcVJAAAAAAAmMBPP/2kpUuXnveIzNatW9W2bVt5ePy5hqJdu3Y6cOCAkpKSVKVKFfn6+mrbtm3KyMjQrl27VLNmTUnS9OnT9eijj8pms13VcykPrCABAAAAAOAacfz4cQ0aNOii29etW3fB9rS0ND311FN69tlnFRYWVmxbXFyc6tevX6ytWrVqReMFBQVp8uTJGjlypPLz89W9e3f16NFD0dHROnr0qPr371/Ks6oYCEgAAAAAAHBxzz//vFq0aKE+ffqcty07O1tWq7VYm5eXlyQpJydHktS3b1/17t1bWVlZCggIkGEYmj59up588kklJCRo4sSJOnr0qO644w6NHTu2/E+oHBCQAAAAAABwjQgPD7/oKpGLWbVqlbZu3aovvvjigtu9vb2Vm5tbrO1sMHLuozNeXl5FwckXX3whq9WqHj166JFHHlGnTp00aNAgDRgwQE2bNlX37t2vaI4VATVIAAAAAAAuye5iX45asWKFkpKSdPPNN6tly5Zq2bKlJGnKlCm67bbbFBoaqoSEhGL7nP0+JCTkvOPl5uZq5syZmjBhgiRpy5Yt6t69u2w2mzp27KitW7eWYrbOwwoSAAAAAABc2Ouvv67s7OxibbfccovGjBmj3r1766uvvtKSJUtUUFAgd3d3SVJ0dLQiIyMv+Orejz76SA0bNlTr1q0lSW5ubsrPz5dU+BrhaxUrSAAAAAAAcGEhISGqXbt2sS9JCgoKUvXq1dWvXz+lp6dr0qRJ2rt3r1auXKmFCxdq5MiR5x0rLS1N8+bN07hx44ramjVrpsWLF2v37t1at26dWrVqddXOrSwRkAAAAAAAYGJBQUGaN2+eDhw4oL59+yoqKkpPPfWU+vbte17fd999V927d1fdunWL2iZNmqQdO3ZowIAB6tKli3r27Hk1p19meMQGAAAAAOCSDGdPoAL7448/in3frFkzLV269LL7PfXUU+e11alTRytXriyzuTkLK0gAAAAAAIDpEZAAAAAAAADTIyABAAAAAACmRw0SAAAAAIDLMSTZLc6eRdmipkr5YgUJAAAAAAAwPQISAAAAAABgegQkAAAAAADA9KhBAgAAAABwSXZnTwDXFFaQAAAAAAAA0yMgAQAAAAAApkdAAgAAAAAATI+ABAAAAAAAmB5FWgEAAAAALokirbgSrCABAAAAAACmxwoS4AJ+zcjVwpPp2pmZpxzDUDVPd91a2UdDqvlJknZn5WluQrr25eQpJd8uLzeLals91C/Qpl4BPpc89gvb/9BXRxMuuv29yEA1sVkVn1eg146n6tfMPAV7uOnREH/9rZJ3sb7rUrM17USqltQLVhUP8k7AWXbYs/V0fvwFt83wCFVDN68r6nch0w7H6JvcExfdfnb/RCNfb+cn6XcjR1Xlroc8qqi9m61Y3w32DEXlJ+vfnuGqbHG/3OkBKEe/Zubqw6R07cw6c8/hUXjPMbhq4T3H1owcfZ2Wrd+ycpWQZ5efu0UNvT01pKqfGnp7lmiMmKxc/ftk4RiGITXy8dSIqn5qZrMW6xefV6Bpcan6Navw3uORYH/9zb/4vcf6tGxNi0vV4jrcewBwPQQkwF98fSpLLx5LVbdK3ppco7JsbhYdyy1QYt6fC/TS7XZV83RTj8r+CvZ0U5bd0DensvXCsVSdyCvQ4GC/ix5/aP1aussz/7z2CYdT5GmxqJFP4c3OS8dSlWs39ErNAP2ckatnj57SJ/Wqqoa18J9teoFdb8WlaXSIPzcoQAXxoHuAmluK/zBR23L+DzAl7XeugSER6nXaOK/9+fwEecqi6yyFP+i8kX9SeZImeQRrhz1b/8pP1FzPcIWdOX6GYdfc/BQNda9COAI42TepWXrxRKq6+ntrclhl+bhZdCyvQCfz/7zn+OxUplILDN1bxVeRXh5KybdrcXKGhh9M0ps1q6iN78WDVUmKOXVajx5OViNvT00OqyxJ+igpQ2OOJOvtWoFq6vNnSPLSiVTlGYamVg/Q9sxcTT5+Sh9H/uXeIyFNo6px7wHANblUQDJnzhxFR0dr0aJFRW2xsbGaOnWqdu7cqYCAAA0aNEhDhw4t2m632xUVFaXly5crLS1NrVu31pQpU1S7dm1nnAKcLCGvQK+dSNNdVXw0IbxyUXtr3+L9Wvl6qdVfbkg6+Xvr+P4kfZ6SecmApIavj8L+8onNzxm5OlVg6KGqNrlbLMq2G/o5I1dzIwPV1GbVjX5e+l9atrak56pGYOE/29nxp1XL6qHbq9guNAwAJ6hu8bjkKpAr7XeucC+bqv5ln9/s2UqTXfe5VS68dhh2/Wbk6HWPUDVy81IrNx9tzM3Uz/Zs3eZeGJAsKEhRDYuHbnG/+HUKQPlLzCvQa3FpujPARxNCz7nn+Eu/J0MqKdCjeJjZzs+qe/ed1IdJGZcNSObuPiQ/Nze9WTNQ3m4WSVIbm1X99ycqKuG05tYOkiRl2w1t///27jw8qvJ+//g9M8lM9oUQEghhNyCCIEqhihuCuFuK+tUKRcH9V60LLi1gpVaqQhEttVYBRa0IAm1RqxaltRYQARfUsESBBBJCyL5ntvP7YyAwJoEkLJOc835dF5fmnDNnnjMhD0/u+ZzPVLv1QvdAaDIsOrD22FDlrg9Int9/YO2RwNoD7YdhC/UI0J6YJvp95ZVX9NxzzwVtKykp0c0336wePXpo+fLluvvuu/Xss89q+fLl9cc8//zzevPNN/W73/1OS5Yskc1m06233iq3232yLwFtwNslNarxGxrfsXW/OMQ77HKo5bPw2yXVskm6MjFwe47HMGRIirQfOlek3Sa3EXj3eHO1W++V1ujhLnGtGicAc/jAXymbpIsdgRTXK8mQFHHYPBRps8ujwNyR6a/Van+VfhGWdPIHCyDIyrIa1RiGxicdec3xw3BEkqLsdvVwhanA6zvq82wuKdcZUc76cESSoh12DY506usajwoPnKN+7WFrfO3xdbVb75XV6KFU1h4AzKvdV5Ds27dPU6dO1aZNm9SzZ8+gfUuXLpXT6dRjjz2msLAw9e7dW9nZ2XrppZc0btw4ud1uLVy4UA8++KDOP/98SdIzzzyjc889V6tWrdLll18eiktCCH1R7Vacw6Zst1cP7y7Rjlqv4hx2nR/n0i9SYhXtCM4U/YYhv6QKn6HV5bVaX1mnBzq3bOFQ6fPr3+W1OivaqS4H3qGJddjV3enQG4VVurdznL6ocuu7Wq8GRobLaxh6Kq9cP+8Yo26udv8jDJjK895iPalCuWTTqTaXbnDE6zR7RKuPO5Iqw681/moNskUo9cDtMzE2u9IVphX+ct1mS9TXRp12Gm6dausgr2Hoj75iXeuIU9ej3M4D4MT7stqtOLtN2XVePbKnRDvqvIp12HVBrEv/L7nhmuNwlT6/ttd6dOYPKlIb4zH8cjby3k34gcDk+zqvOoY56tcei4ur9MuUOH1R7dZ3dYfWHk/ml+vnSTHq5mTtAcC82v0M9+233yo+Pl4rV67Un/70J+Xm5tbv27hxo4YOHaqwsEOXOXz4cP3lL39RUVGRcnNzVVVVpeHDh9fvj4uLU//+/bVhw4bWByRhDjl6ndLqa0LoFO7aqDrDq2m55ZrYJ10DE2OVWVqpF7dla6fC9eI5p8t22DsrT2/O0t+y8yUFFhoPDOyta3p0afTc9rRuQf896MNde1VnFOjqvj3lSOtUv316Qooe3pipMVsLZJc0KaObTu/bXQu258gId+qmHw2Sw26aIjBIcoYdfaGLtim+ukI/LXFqUEyi4hxhynXXaGlBth6uK9ATPQdpaFxSi447XHjP9KD/HvRB4R7V5Rq6PL23nImp9dunVHXUjF1f6/88e2SXdGNKTw1M7aXX9+2USpy6MWOIwpk7TMWRRtVre1S4e6Pq5NW0/HJN7HVgzVFWqZe2Z2uHLVwvDg9ecxxuzpdbVWNINw86VY742EaPcXQNrDd6Jibo29pa2Xr2kf3A+bx+Q5k5GyVJFUmd5OgSWH9MS0zRI59n6pKswNrj5j7dNDCjuxZ+lyM5nZp4FmsP0wgnKAca0+4DkpEjR2rkyJGN7svPz1dGRkbQtk6dAv8A5OXlKT8/8Itt586dGxyzd2/TnxRwNPakToqfPb/Vj0fo2MaMUd2uXfrFfQ/otttukySNlBS7aJFmzpypzHGTdfbZZ9cff09enm4sKlJxcbFWr16t2UuXSpeOC+pz80Ox9z0a9PW748YpISFBVy9aJqfz0C/I50r62OPR7t27lZycrNjYWO3atUuLrr5aCxYsUIchQzRv3jytWLFCHo9HY8aM0cMPPyyXq2V9DdB2xB/9ELRR6QrMFYf7v/JyXXnllXrFWaOfLn2+Rcc1JvWpXwV9/dG4cUqoStD1774ZNHekS7qokbnjzauv1oKFC9SDuQNoEw6uOR44bM1xkaS4A2uOLdcFrzkOmjt3rt7/5yeaPn26fjx+/FGfZ+L9UzR16lQ9l5CuO++8s77/Xn6dR5IUO/EOJVx2mSTpPB157ZHE/IF2xpDkP+pR7UvDdu04ntp9QHIktbW1QYtGSfUTeF1dnWpqaiSp0WPKyspa/bz+ogJVPjm11Y9H6MSUFUmSBm9YrbLtn9VvP6OyWpK06ekZOq3PoXdxow/86SZpsCR3eif9YdYsXfTlx0p0Bf+9sqd1U+x9j6rimd/Kn5sjScoqr9I333yj63t2Uc2v71JNI2NKUmBiL5M0dd1mXdopQae8+YJefzpfy7bl6E8/HqioMIfuefvvem7DJ7qtLw2G26vyHCpIzOYsj0PvbNum7665XS57058Yc6TjwnumK/WpXyn/4d/Ls3O3JGlHTYW+2f6NftoxXfvG39voOcMllR7488j3n+uiqA5KeXqh5hf9Tkv37dCs3kMUaXfoV8v/oVkfrtXE1F7H45IRArFUkLRLMaWBNcegT1erdEsja44nZ6h/7+DKsflZ2XopK0d3ZnTXFZv+o9JN/2ny/I6u3RT7wKMak52pvL499PLSpVq8eLEkaWBCrG7s3lmv7tijqCUvq3TViqDHdpDkU2D+mLp+sy5JTlCfv76g15/M17KsHM0bNlBRDod+ufLvenb9J7otg7VHexM77fdyJCWHehhAm2PqgCQiIqJBs9W6ujpJUlRUlCIiAvd7u93u+v8/eExkZGTrn9jrk29HVusfj5DpLa++keTbkyNf0aHSQ1/dgY/lLS6Sb0dtk48/1VOtFYah3VlZimvivmB/bk79349/7C2XJF1hrzvq35l3S6q1s7RSMzu65NuRpbW7SzQy2qG0gj2SpCuj7Ho/J0+Tw1kot1fuLN6BMxufNxC2e7buaLJUvrnHeXbulnvLd5Kkd7zFkqRRpV65y7874hhW+Sq1y1euX4XHyL3lO33q2a8RNqc67ciTJI32hWt1Qa5uKDHbe2zW4TuwtkH7Ur/myA1ec3gPX3PYDq05FhRWakFhpSZ3jNGEZqwbDvLtydGNtlpd27ujdnu8irLb1TncoafyixRpsymjeJ98pQWNPvbd0sDa44kOgbXHmtwSXRjpUNq+wNrjiki73t+Tp8lhrD3aHY8n1CMA2iRT30SYmpqqgoLgCf/g1ykpKfW31jR2TGpqqmA9F8YFgrJ1lcGLzbUVga8HRB75fs1NVW7Zpfpmq0fi9ht6v6xG/SPD1TviyOct9fr1x30Vujc1VrEHmrYZhlTjP1RkV+03KLkD2pAKw6fP/DXqZQuX8wjhSHOPO8hjGPq3v0oZNqd62I9cdVRm+LTAV6LbHYmKsR2YOyTVHjZb1Iq5AwiFC2IDa45Pf7DmWFcV+Pq0w9YcLx8IR25KitbkVn7SntNuU29XuDqHO5Tv8emj8lpdlRApl73xeafU69e8/RW6N+XQ2kOGVGscmjEOX4cAgBmYuoJk6NChevPNN+Xz+eRwBEqW161bp549eyopKUmxsbGKiYnR+vXr1a1boJFVeXm5MjMzNb4Z93TCfIbFuDQi1qWX91fKUGBxsrXGowX7K3VOjEuDogO/jDyZV6You039I8PVIcyhMp9fq8tq9WF5rW5MilZi2KHs8fMqt+7ZVazJXqceOOy5/ltRq3KfoasSjl6t9Gx+uU6LdGpU/KFjh8W49Md9FRoQ5VSU3aa3iqubdS4Ax99T3v3qpDCdYncqTg7lGR6t8JWrVD7d70hq8XGS9LW/Vr/y7tOEfLt+fdj2tf5qVcivm+1H/yXpJV+J+tpcOu/AxwBL0pn2CM33lehUn0sRNptW+sp1cTPOBeD4Ghbt0ogYl14uqpRfgTdhttR6tLCwUudEuzToQCXqG0VVeqmwUsOjnTo7xqVvaoKrNQZEHgpKv6h2656cYt3cMUa3Hrhr7vuKKn1UWKl+EWFy2mzKqvXq9eJKdXU6dGty0z/7zxWUq3+EUxfFHVpb/CjGpXkFFRoQ6VSkzaa3Sqp1JWsPtHHUR6IlTB2QjBs3TvPnz9fUqVN1yy23aPPmzVq0aJFmzJghKdB7ZPz48Zo9e7Y6dOigtLQ0zZo1S6mpqRo9enSIR49Qebxrghbur9Q/Sqq1oMCv5HC7rk+K1uTDFhEDIsP1bmmN3iutUYXPUJTdpj4RYfpNWrwuabBQMOST9MM3Wd4uqVGk3aZR8Uf+aM8NlXX6uKJOf+3dMWj7lYmRyvP49Od9FfIYhi6Mi9DEIyx0AJw4PW1O/ddfpX96K1QjQ7Gy6zSbS1McHZVhd7X4OOlQY7kfLuz+5a9UhGw6zx6tI/nSX6O1/mr9OTz4k7Uutsco3/DqZV+pvDJ0jj1K1ztoEQyEwuNdErSgsFIrS6u1sNCvjmF2Xd8hWpOSDv17vqYycJvNp1VufVpV3OAca/sdqno2jINrjkOLjnCbTZuq6vRWcZVqDEMpYQ79JCFKE5KiFdnEJ9JsrAqsPV7v9YO1R3yk8tw+/bkgsPa4IDZCNyWx9gBgHjbDMExTG/fII48oNzdXr732Wv22zZs364knnlBmZqaSk5M1adKkoOoQn8+nOXPmaMWKFaqtrdXQoUP16KOPqmvXrq0ehy8/T2V33XBM1wLzcfQ6RfGz56tsyi30qEGjSuhBgkY4T+2j9KXPa/d1d9X3IAEOl9CLHiRoyNHrFCU8M1+l97HuQEMJf1ksR2qXox/YzpVlF+i1EfeHehjH1YT/zVF8906hHoZpmaqC5Mknn2yw7fTTT9eSJUuafIzD4dCDDz6oBx988EQODQAAAAAAtGGmCkgAAAAAADjINLdL4KQw9afYAAAAAAAANAcBCQAAAAAAsDwCEgAAAAAAYHkEJAAAAAAAwPJo0goAAAAAMB+b5LeFehDHmdmup42hggQAAAAAAFgeAQkAAAAAALA8AhIAAAAAAGB59CABAAAAAJiSP9QDQLtCBQkAAAAAALA8AhIAAAAAAGB5BCQAAAAAAMDy6EECAAAAADAdQ+brQWKEegAmRwUJAAAAAACwPAISAAAAAABgeQQkAAAAAADA8uhBAgAAAAAwJXp2oCWoIAEAAAAAAJZHQAIAAAAAACyPgAQAAAAAAFgeAQkAAAAAALA8mrQCAAAAAEzJbwv1CNCeUEECAAAAAAAsj4AEAAAAAABYHgEJAAAAAACwPHqQAAAAAABMyR/qAaBdoYIEAAAAAABYHgEJAAAAAACwPAISAAAAAABgefQgAQAAAACYjnHgj5mY7XraGipIAAAAAACA5RGQAAAAAAAAyyMgAQAAAAAAlkcPEgAAAACAKfnp2oEWoIIEAAAAAABYHgEJAAAAAACwPAISAAAAAABgeQQkAAAAAADA8mjSCgAAAAAwJX+oB4B2hQoSAAAAAABgeQQkAAAAAADA8ghIAAAAAACA5dGDBAAAAABgSkaoB4B2hQoSAAAAAABgeQQkAAAAAADA8ghIAAAAAACA5dGDBAAAAABgSv5QDwDtChUkAAAAAADA8ghIAAAAAACA5RGQAAAAAAAAy6MHCQAAAADAdAxJfluoR3F8GaEegMlRQQIAAAAAACyPgAQAAAAAAFgeAQkAAAAAALA8AhIAAAAAAGB5NGkFAAAAAJiSn7amaAEqSAAAAAAAgOURkAAAAAAAAMsjIAEAAAAAAJZHDxIAAAAAgCnRgQQtQQUJAAAAAACwPAISAAAAAABgeQQkAAAAAADA8uhBAgAAAAAwJX+oB4B2hQoSAAAAAABgeQQkAAAAAADA8ghIAAAAAACA5dGDBAAAAABgOoYM+WWEehjHlWGy62lrqCABAAAAAACWR0ACAAAAAAAsj4AEAAAAAABYHj1IAAAAAACmRMcOtAQVJAAAAAAAwPIISAAAAAAAgOURkAAAAAAAAMsjIAEAAAAAAJZHk1YAAAAAgCn5Qz0AtCtUkAAAAAAAAMsjIAEAAAAAAJZHQAIAAAAAACyPHiQAAAAAAFPyywj1ENCOUEECAAAAAAAsj4AEAAAAAABYHgEJAAAAAACwPHqQAAAAAABMiQ4kaAkqSAAAAAAAgOURkAAAAAAAAMsjIAEAAAAAAJZHDxIAAAAAgOkYkvyhHsRxRk+VE4sKEgAAAAAAYHkEJAAAAAAAwPIISAAAAAAAgOURkAAAAAAAAMujSSsAAAAAwJQM2pqiBaggAQAAAAAAlkdAAgAAAAAALI+ABAAAAAAAWB49SAAAAAAApuQP9QDQrlBBAgAAAAAALI+ABAAAAAAAWB4BCQAAAAAAsDx6kAAAAAAATMkvI9RDQDtCBQkAAAAAALA8AhIAAAAAAGB5BCQAAAAAAMDy6EECAAAAADAlOpCgJaggAQAAAAAAlkdAAgAAAAAALI+ABAAAAAAAWB4BCQAAAAAAsDyatAIAAAAATMeQ5DdZm1ZzXU3bQwUJAAAAAACwPAISAAAAAABgeQQkAAAAAADA8uhBAgAAAAAwJX+oB4B2hQoSAAAAAABgeQQkAAAAAADA8ghIAAAAAACA5dGDBAAAAABgSoaMUA8B7QgVJAAAAAAAwPIISAAAAAAAgOURkAAAAAAAAMujBwkAAAAAwJT8oR4A2hUqSAAAAAAAgOURkAAAAAAAAMvjFpsTwFPnUObajqEeBtqY6IoEnSFpx1cJqvqavx9oqP+PC0M9BLRBjnS3JCku3S2fpy7Eo0FbZFA/DgDAcUFAAgAAAAAwHUOSISPUwziuzHU1bQ+32AAAAAAAAMsjIAEAAAAAAJZHQAIAAAAAACyPgAQAAAAAAFgeTVoBAAAAAKbEB32hJaggAQAAAAAAlkdAAgAAAAAALI+ABAAAAAAAWB49SAAAAAAAJmTIbxihHsRxZrbraVuoIAEAAAAAAJZHQAIAAAAAACyPgAQAAAAAAFgePUgAAAAAAKZExw60BBUkAAAAAADA8ghIAAAAAACA5RGQAAAAAAAAy6MHCQAAAADAlPx0IUELUEECAAAAAAAsj4AEAAAAAABYHgEJAAAAAACwPAISAAAAAABgeTRpBQAAAACYjiHJMFmTVnNdTdtDBQkAAAAAALA8AhIAAAAAAGB5BCQAAAAAAMDy6EECAAAAADAlf6gHgHaFChIAAAAAAGB5BCQAAAAAAMDyCEgAAAAAAIDl0YMEAAAAAGBKfhmhHgLaESpIAAAAAACA5RGQAAAAAAAAyyMgAQAAAAAAlkcPEgAAAACAKRn0IEELUEECAAAAAAAsj4AEAAAAAABYHgEJAAAAAACwPAISAAAAAABgeTRpBQAAAACYjiHJH+pBHGe0nD2xqCABAAAAAACWR0ACAAAAAAAsj4AEAAAAAABYHj1IAAAAAACmZBh07UDzUUECAAAAAAAsj4AEAAAAAABYHgEJAAAAAACwPHqQAAAAAABMyS96kKD5qCABAAAAAACWR0ACAAAAAAAsj4AEAAAAAACTKy0t1aOPPqrzzjtPQ4YM0Q033KCNGzfW79+yZYvGjx+vwYMH64ILLtCCBQuCHv/JJ5/ooosu0o9+9CM99dRTQfv27duns88+W0VFRSflWk4UAhIAAAAAgCn5TfbnWNx///366quvNGfOHC1btkynnXaaJk+erO+//14lJSW6+eab1aNHDy1fvlx33323nn32WS1fvjzwOvr9evjhh3Xbbbfp1Vdf1T//+U99/PHH9eeeO3eufvaznykpKekYRxlaNGkFAAAAAMDEsrOztWbNGi1evFhDhgyRJE2dOlX//e9/9c477ygiIkJOp1OPPfaYwsLC1Lt3b2VnZ+ull17SuHHjVFJSoqKiIo0dO1ZOp1NDhgxRVlaWzj//fG3btk3/+9//9MEHH4T4Ko8dFSQAAAAAAJhYYmKiXnzxRQ0YMKB+m81mk2EYKisr08aNGzV06FCFhR2qoRg+fLh27typoqIiJSYmKjo6Wps2bVJVVZW2bt2q9PR0SdKsWbN01113KSoq6qRf1/FGBQkAAAAAAO1EXl6eJkyY0OT+jz76qMG2uLg4nX/++UHb3nvvPeXk5GjEiBF65plnlJGREbS/U6dO9c+XlJSk6dOn6/bbb5fX69WoUaM0evRorVu3Tnv27NG11157HK4s9KggAQAAAADAQjZt2qRf//rXuuiiizRy5EjV1tbK6XQGHeNyuSRJdXV1kqSxY8dqw4YNWrt2rZ577jnZbDbNmjVLDzzwgAoKCjRhwgRdeOGFeuaZZ0769RwvVJAAAAAAAEzJkBHqIRx3Xbp0abRKpLk+/PBDTZkyRYMGDdKcOXMkSREREXK73UHHHQxGDr91xuVy1Qcnb7/9tpxOp0aPHq0777xTI0aM0IQJE/Szn/1MAwcO1KhRo1o9xlChggQAAAAAAAt4/fXXdffdd+u8887TSy+9pIiICElSamqqCgoKgo49+HVKSkqD87jdbj377LN68MEHJUmfffaZRo0apaioKJ1zzjlBHx/cnhCQAAAAAABgcm+88YYef/xx3XjjjZo7d27QLTVDhw7Vpk2b5PP56retW7dOPXv2bPSje19//XX169dPZ555piTJbrfL6/VKkjwej/z+Y/1Q4tAgIAEAAAAAwMR27typmTNnavTo0br99ttVVFSk/fv3a//+/aqoqNC4ceNUWVmpqVOn6rvvvtOKFSu0aNEi3X777Q3OVV5ervnz5+v++++v33b66adr8eLF2r59uz766KP6jxJub+hBAgAAAAAwHUOG/CbrQdLanioffPCBPB6PVq1apVWrVgXtGzt2rJ588knNnz9fTzzxhMaOHavk5GQ99NBDGjt2bINzvfDCCxo1apR69+5dv23q1KmaMmWK3nnnHf3kJz/RmDFjWjXOUCMgAQAAAADAxO644w7dcccdRzzm9NNP15IlS456roceeqjBtl69emnFihWtHl9bwS02AAAAAADA8ghIAAAAAACA5XGLDQAAAADAfAzJMMzVg8RkLVXaHCpIAAAAAACA5RGQAAAAAAAAyyMgAQAAAAAAlkcPEgAAAACAKflDPQC0K1SQAAAAAAAAyyMgAQAAAAAAlkdAAgAAAAAALI8eJAAAAAAAUzJkhHoIaEeoIAEAAAAAAJZHQAIAAAAAACyPgAQAAAAAAFgeAQkAAAAAALA8mrQCAAAAAEzJT5NWtAAVJAAAAAAAwPIISAAAAAAAgOURkAAAAAAAAMujBwkAAAAAwHQMSYZhrh4k5rqatocKEgAAAAAAYHkEJAAAAAAAwPIISAAAAAAAgOXRgwQAAAAAYEp+unagBaggAQAAAAAAlkdAAgAAAAAALI+ABAAAAAAAWB49SAAAAAAApmTQgwQtQAUJAAAAAACwPAISAAAAAABgeQQkAAAAAADA8ghIAAAAAACA5dGkFQAAAABgQob8htmatJrtetoWKkgAAAAAAIDlEZAAAAAAAADLIyABAAAAAACWRw8SAAAAAIAp0bEDLUEFCQAAAAAAsDwCEgAAAAAAYHkEJAAAAAAAwPLoQQIAAAAAMB1Dkt9kXUjMdTVtDxUkAAAAAADA8ghIAAAAAACA5RGQAAAAAAAAy6MHCQAAAADAlMzWgwQnFhUkAAAAAADA8ghIAAAAAACA5RGQAAAAAAAAyyMgAQAAAAAAlkeTVgAAAACAKRkGTVrRfFSQAAAAAAAAyyMgAQAAAAAAlkdAAgAAAAAALI8eJAAAAAAAU/KLHiRoPipIAAAAAACA5RGQAAAAAAAAyyMgAQAAAAAAlkcPEgAAAACAKRn0IEELUEECAAAAAAAsj4AEAAAAAABYHgEJAAAAAACwPHqQAAAAAABMx5BkGObqQWKuq2l7qCABAAAAAACWR0ACAAAAAAAsj4AEAAAAAABYHj1IAAAAAACm5KdrB1qAChIAAAAAAGB5BCQAAAAAAMDyuMUGaMJ/w4u1KCpPLsOu58v7B+3LttdoZUSBdjpqVGPzqYM/XMM8CRpT11GuZuSOtfLpbxEF2hBepiqbT539Ll1a11HDPAlBxxXb3Ho1Mk9ZYdVK9IdrXG2KzvDGBR2zIaxMr0Xm6YnKUxRr8CMNhMpX1W4tKqzUN9Ue1RmGOoU7dGl8pCYlx0iSttd69JeCSn1f61GJzy+XzaburjCNS4zSJQmRRz3/psJS3ZmZ3+i+l3p00IAoZ/3X+zw+PbW3TF9Ve5QcZtddKbE6LzYi6DEfldfq6b1lerN3shLDeL8ECJWvqt16tahS39QcmDvCAnPHzR0Dc8fGqjp9UF6rr2vcKvD4FeOwqV9EuCZ1jFG/iPBmPUdmjVsvFgaewzCkUyPDdVvHGJ1+2LwhBeaOp/PL9FVNYO64M7nh3LG6vFZP55dpcS/mDgDm025mteeff14TJkwI2rZlyxaNHz9egwcP1gUXXKAFCxYE7ff7/Xruued07rnnatCgQZo0aZKys7NbdA5YU4nNo6WR+UrwNwwc8uy1+n3MDhXZPbqhtrPuqequH3ni9barQC9G7W7W+f8UlaO1zhJdVddJ91V1Vw9fpF6M2qNPw0uDjlsQlasam193VqfrDE+sXojarQJ7Xf3+avm0OHKvrqtNJRwBQuiDshrdtatYMXa7pqfFa063RE1Iig6667nS51encLvu6BSrOd0S9WhavDqHOzQjr0wv769s9nPd0SlGL/XoEPSnV0Twz//juWWq9Bma2TVB58dGaNqeUu1xe4PGMje/XHenxPILDhBC/yqr0f/LKVa03a7pneP1h66JGv+DueNvpdXa6/HpusRo/SE9Ufd2ilOJ169bdxVpY1Vdk+c+KLO0QnflFKvOb2h653g92iVedX5D9+wu1tc17qBjH99bpkq/oSfSAnPH9LxG5o6Ccv2iE3MHAHNqF79RvfLKK3ruuec0dOjQ+m0lJSW6+eabNWrUKM2YMUNffvmlZsyYoYSEBI0bN05SIFR588039fvf/14pKSmaNWuWbr31Vr3zzjtyOp3NOges6bXIPGV4oxVtOLQpvDxo36fhZfLYDN1Vna5Ofpck6VRfjMpsXn3sKlGVfIqWo8lzf1GzT5nhVbqtumt9xUi/mhgV2dx6KyJfP/LEyy6b6uTXNkeVHqnqpT6+KA3wxmpTeLkyw6rUyR143mUR+Ur1uzTCk3hiXggAR1Xg8empvHL9JDFSD3aOr99+ZnTwcUOiXRoS7QraNiI2Qnk7i/SPkmrdfKDS5GjSnWFB1SI/VOs39Hm1W3/p0UEDo5waFuPSvytq9VmlW107BP7Z/1NBhbo5w3RFQlQzrxLA8bbf49NT+eW6OiFSD6YeNnf84LgHUuLUISx4XTE8xqnrvi/Uq0VVOusH88oP/WV7tmLsdj2T3kERdpsk6awop67dsV/zCir0l+5JkgJzxxfVbr3QvYMGRjo1LNqlf5fXakOVW12dgbnj+f3MHWhvDBmG2Zq0mu162pY2Hf3u27dPt9xyi5599ln17NkzaN/SpUvldDr12GOPqXfv3ho3bpxuuukmvfTSS5Ikt9uthQsX6u6779b555+vfv366ZlnntG+ffu0atWqZp0D1rQuvFTbwqo0vqZLo/sdCiwuIo3gxUqkHLIZUtiB/U3ZUJ0vl2HXWZ74oO0jPIkqtXu1w1EjSfLJkGGTXMahH1OX7PLIL0n6zlGtdc5S/byJcQI4Od4urVGNYWh8UvMCjh+Kd9jlsB153mgJj2HIkBRpP3TOSLtN7gMLxM3Vbr1XWqOHO8c1cQYAJ8PKsubNHT8MRyQpym5XD1eYCry+oz7P5pJynRHlrA9HJCnaYdfgSKe+rvGo8MA56ucOW+Nzx9fVbr1XVqOHUpk7AJhXmw5Ivv32W8XHx2vlypUaNGhQ0L6NGzdq6NChCgs7VAQzfPhw7dy5U0VFRdq6dauqqqo0fPjw+v1xcXHq37+/NmzY0KxzwHrKbV69GbFX42pT1MFo/L7ec9wJijLsei0yT/ttbtXIp6/CyvWxs1gj3R2O2oNkt6dCXXyu+qDloK6+wD2+ufZaSVKUHOrsc+lfrkJVyafPw8q1216r3r4oeWVoUWSuLqtLVqr/yO8cATixvqhyK85hU7bbq59/X6gRmfm6bFuBntpbpiqfv8HxfsOQ1zBU4vVreXG11lfWaXzH6EbO3LjZe8s1IjNfF23dp3uzi/VVdXCJfKzDru5Oh94oqlK5z6+Py2v1Xa1XA6PC5TUMPbW3XD/vGKNurnZRRAqY1pfVbsXZbcqu82rizkKduzVfl2UV6On8xueOw1X6/Npe61FP59F/jj2GX85GMtjwA4HJ93WBW2gOzh2Liw/MHRW1+q7Oq4GRgbnjyfxy/TwpRt2a8ZwA0F616Rlu5MiRGjlyZKP78vPzlZGREbStU6dOkqS8vDzl5wca2XXu3LnBMXv37m3WOZKSklo38DCHogf2PPpxaHP+sn+juvjidEX3M2Wz2RRWVCpVVwR9P6MlPe7poj/s36hHwrfXb78kpqcmJp4mWxPvBEf2SZMkVTn8So6JUXSv4L8jyb5aKfc7uTvHKTo+sO+Oung9s3+j7nFukU3ST+MyNLB7X60o2y57dbiu6TFUYbY2nXOiBRy9uFWqPSrM3qg6w6tpeeWa2CddAxNilVlWqRe3ZWunwvXi2acHzQtPb87S33IC/0aF2216YEBvXdOj6Uowe1o3SVJsWldd37NIQ5LiFR8erj3VNXr9+z36f9nFmjN0gH7c6dDfn+kJKXp4U6bGbCuQXdKkU7rp9L7dtSArR0a4UzcNHSSHnbnDLIwj/y6NNqpw90bVyatp+eWa2CtdAxMDc8dL27O1wxauF4ef3uSaYs6XW1VjSDcPOlWO+NhGj3F0DcwdPRMT9G1trWw9+8h+4Hxev6HMnI2SpIqkTnJ0Cax/pyWm6JHPM3VJVmDuuLlPNw3M6K6F3+VITqcmnsXcYRrhzWvwC1hNmw5IjqS2tlZOZ/A92C5X4J30uro61dQEblNo7JiysrJmnaO1XF2SdMaq2a1+PELjgw8+0BcPvK+///3v6tOnjyQp6ZFH5Pjgg6Dv5549e/TwpElKPT1Dj9x0kzp06KCvvvpKf/7znxX9I5dmzpx5xOdxpiQqLj1dZ8wP/jtSUFAgnbtKabdcrjNuu02SdIakcR6Pdu/ereTkZMXGxmrXrl1aefXVWvDKAg0ZMkTz5s3TihUr5PF4NGbMGD388MP1f48BnHi2MWNUt2uXfnHfA7rtwM/uSEmxixZp5syZyrxmss4+++z64+/Jy9ONRUUqLi7W6tWrNXvpUumycZo8efIRn2fozLka+oNtV5WX68orr9TzZR5d8tr8+u3nSvq4kblj0dVXa8GCBerA3AGE3MG544HD5o6LJMUdmDu2XBc8dxw0d+5cvf/PTzR9+nT9ePz4oz7PxPunaOrUqXouIV133nmn/H6/5s2bp/w6jyQpduIdSrjsMknSeTry3JHE3IF2yE/PDrRAuw1IIiIi5HYHlxUfDDWioqIUERG4XcHtdtf//8FjIiMjm3WO1qrLK9KWm55s9eNx8tX6vZqet1oXR6cr//Y/6uAHae4r/lq+mjr976JfyiGbIuxherZwk0prCzXD118RswP9bM6UNN7ZWy8sX67TPslT/4iODZ4jsk+a+v35PrmKa5SX97W+GD0laP9ud4UkqezlVfrire0NHl924L+P71unc8I7yfGrN/VM5dNaXrZN0zr9WBGuMP1+6duqfXuDrk3oe7xeGpxEvU4vDfUQ0AoxpYFbMgd/tlpl2z6r335GZbUkadNTM3Ran/T67dEH/nSTNFiSO72T/jBrli764mMluho2X7WndVPs/Y+qYs5v5c/NabD/bJe0Yts27bv3ZkU4gnsVJEnyKzB/TF23WZd2StApi1/Q60/la9n2HP1p+EBFhTl0z8q/67nPPtFtfbsf02uB0KCCpH06OHcM+nS1Src0Mnc8OUP9e6cHPWZ+VrZeysrRnRnddcWm/6h003+aPL+jazfFPvCoxmRnKq9vD728dKkWL14sSRqYEKsbu3fWqzv2KGrJyypdtSLosR0k+SSVSpq6frMuSU5Qn7++oNefzNeyrBzNGzZQUQ6Hfrny73p2/Se6LYO5o72JnfZ7OZKSQz0MoM1ptwFJampq4B33wxz8OiUlRV6vt35bt27dgo7p169fs87Ral6fqr7e2frH46QrtLlVFlendyt26N2KHQ32T97zvgZ7YnV3dXftjClUqhEu37e7VXXYMZ3ttVKs9H32LnU/EHY0Js3n0pq6/Sr/ekdQH5Ks8FIpSkrOqVSVr/G/P/8LL9GeiFLdUZCsqtyd2hSVozP90Yrbul+SdI4zWutqduuy3U1/wgXaLl9MYaiHgFboLa++keTbkyNf0aGSZd+B+/pVXCTfjtomH3+qp1orDEO7s7IUd4RPp/Hn5si3I6vh9vJAfOrf+b189sbL8d8trdbOskrN7OiSb0eW1u4u0cgoh9IK9kiSroyy6/3deZoc7m708WjbCEjap/q5Izd47vAePnfYDs0dCwortaCwUpM7xmiCva7R+aAxvj05utFWq2t7d9Ruj1dRdrs6hzv0VH6RIm02ZRTvk6+0oNHHvltarZ2llXqiQ2DuWJNbogsjHUrbF5g7roi06/09eZocxtzR7ng8oR4B0Ca125sIhw4dqk2bNsnnO9S9e926derZs6eSkpLUr18/xcTEaP369fX7y8vLlZmZqbPOOqtZ54B1xBtherCyR4M/AzwxCjdserCyh8bWBkKzBCNcefZa1Sq4c/z3jsA7Pon+I9/TOTQqVXU2f4OPD14bXqoEf5h6+SIbfVyFzau3IvJ1Q21nRR34GGFDUp0OrYzrbD4ZlBECJ9WFcYEqxXWVwbdmrj3w9YCoI88Jm6rcskvq0orGh+U+v9ZU1OmUiDC5mghHSr1+/XFfhe5NiVWsI/DPviGp5rCPPaz2M3MAJ9sFsYG549MfzB3rqgJfnxZ5aO54+UA4clNStCZ3bN0nZjntNvV2hatzuEP5Hp8+Kq/VVQmRR5w75u0PnjtkSLWHzR01fmYOAObSbitIxo0bp/nz52vq1Km65ZZbtHnzZi1atEgzZsyQFOg9Mn78eM2ePVsdOnRQWlqaZs2apdTUVI0ePbpZ54B1hMuufr6GC441RqnssgXtG12XpHlROfpD9C5d7O6oGL9DO8Kq9a6rUF18Lg30Hjp2m6NKs6N36sq6TrpBgcarZ0SmqL8nWq9F5KlGPqX4nVrvLNM34ZW6tbqr7E18TPCSiHz19EVq6GEfDzzAE6Mlkfnq7YuSy7DrQ2exznXT6BM4mYbFuDQixqWXCytlKPBLzdZajxbsr9Q5MS4NOlAV8mRemaLsNvWPDFeHMIfKfH6tLq/Vh+W1ujEpWolhh96z+LzKrXuyi3Vzcoxu6xXYNu3zrUqpq1a/yHAlOGza7fbpjaIqFXv9mtYlvpGRBTy7r1ynRTo1Kv5Q+DosxqU/7qvQgEinouw2vVVcrasSGw9nAZwYw6IPzB1FlfJLGhAZri21Hi0srNQ50YfmjjeKqvRSYaWGRzt1doxL39QEV2sMiDxUefZFtVv35BTr5o4xuvXA3PF9RZU+KqxUv4gwOW02ZdV69Xpxpbo6Hbo1uemw5bmCcvWPcOqiuENzw49iXJpXEJg7Im02vVVSrSsTmDvQtvEWAFqi3QYkSUlJmj9/vp544gmNHTtWycnJeuihhzR27Nj6Y+655x55vV5NmzZNtbW1Gjp0qBYsWFDfmLU55wB+aLA3TlOqeuifrkItjtirGptPif5wne9O1OV1yQo7rDDLkCG/reHE/P+qu2lFRIH+EVGgKptPqX6XbqvuqmGehEafM9NRqc/Dy/Xbij5B28/1JGq/3a3lEfvklaEzvXG6oo77SYGT7fGuCVq4v1L/KKnWgv1+JYfbdX2HaE0+7JePAVHhere0Ru+V1ajCZyjKblOfiDD9pku8LmnwC4YhnyTjsHdqT4mL1r92lehvJdWq8RuKc9h1elS4fpOWoP6RjVepbKis08fldfpr7+C+SFcmRCrP7dOfCyrkMQxdGBuhia18VxpA6z3eJUELCiu1srRaCwv96hgWmDsmJR36eVxTGbjN5tMqtz6tKm5wjrX9Uuv/3zACc4f/sLkj3GbTpqo6vVVcpRrDUEqYQz9JiNKEpGhFNvGJNBur6vRxRZ1e7/WDuSM+eO64IDZCNyUxdwAwD5tx+OoLx0Vt9j5tHHZXqIeBNiZ6YE+dsWq2vhg9hR41aFT/H9ODBA05ep2i+D/MV9kDtzS75wCshR4kaIyj1ylKeGa+Su9j7kBDCX9ZLEdq0x8xbxZ7snN12bBrQj2M4+qf65epa/e0UA/DtNptDxIAAAAAAIDjpd3eYgMAAAAAQFMMBd9yZgbmupq2hwoSAAAAAABgeQQkAAAAAADA8ghIAAAAAACA5RGQAAAAAAAAy6NJKwAAAADAlAzamqIFqCABAAAAAACWR0ACAAAAAAAsj4AEAAAAAABYHj1IAAAAAACm5DfoQYLmo4IEAAAAAABYHgEJAAAAAACwPAISAAAAAABgefQgAQAAAACYkiF6kKD5qCABAAAAAACWR0ACAAAAAAAsj4AEAAAAAABYHj1IAAAAAADmYxjyGybrQWK262ljqCABAAAAAACWR0ACAAAAAAAsj4AEAAAAAABYHgEJAAAAAACwPJq0AgAAAABMyRBNTdF8VJAAAAAAAADLIyABAAAAAACWR0ACAAAAAAAsjx4kAAAAAADTMST5DXP1IDHX1bQ9VJAAAAAAAADLIyABAAAAAACWR0ACAAAAAAAsjx4kAAAAAABTMujagRagggQAAAAAAFgeAQkAAAAAALA8AhIAAAAAAGB59CABAAAAAJiSYfhDPQS0I1SQAAAAAAAAyyMgAQAAAAAAlkdAAgAAAAAALI+ABAAAAAAAWB5NWgEAAAAApuSXEeohoB2hggQAAAAAAFgeAQkAAAAAALA8AhIAAAAAAGB59CABAAAAAJiSYdCDBM1HBQkAAAAAALA8AhIAAAAAAGB5BCQAAAAAAMDy6EECAAAAADAdQ4b8MlcPEsNk19PWUEECAAAAAAAsj4AEAAAAAABYHgEJAAAAAACwPHqQAAAAAABMyTDo2YHmo4IEAAAAAABYHgEJAAAAAACwPAISAAAAAABgefQgAQAAAACYkp8eJGgBKkgAAAAAAIDlEZAAAAAAAADLIyABAAAAAACWR0ACAAAAAAAsjyatAAAAAABTMkSTVjQfFSQAAAAAAMDyCEgAAAAAAIDlEZAAAAAAAADLowcJAAAAAMCUDIMeJGg+KkgAAAAAAIDlEZAAAAAAAADLIyABAAAAAACWRw8SAAAAAIDpGJL8MlcPEnNdTdtDBQkAAAAAALA8AhIAAAAAAGB5BCQAAAAAAMDy6EECAAAAADAfQzIMk3XtMNnltDVUkAAAAAAAAMsjIAEAAAAAAJZHQAIAAAAAACyPgAQAAAAAAFgeTVoBAAAAAKbkN1uTVpxQVJAAAAAAAADLIyABAAAAAACWR0ACAAAAAAAsjx4kAAAAAAATMmSYrgeJ2a6nbaGCBAAAAAAAWB4BCQAAAAAAsDwCEgAAAAAAYHn0IAEAAAAAmJKfnh1oASpIAAAAAACA5RGQAAAAAAAAyyMgAQAAAAAAlkcPEgAAAACA6RiSDMNcPUjMdTVtDxUkAAAAAADA8ghIAAAAAACA5RGQAAAAAAAAyyMgAQAAAAAAlkeTVgAAAACAKflN1qQVJxYVJAAAAAAAwPIISAAAAAAAgOURkAAAAAAAAMujBwkAAAAAwJQM0YMEzUcFCQAAAAAAsDwCEgAAAAAAYHkEJAAAAAAAwPLoQQIAAAAAMCW/QQ8SNB8VJAAAAAAAwPIISAAAAAAAgOURkAAAAAAAAMujBwkAAAAAwJQMepCgBaggAQAAAAAAlkdAAgAAAAAALI+ABAAAAAAAWB4BCQAAAAAAsDyatAIAAAAATMiQIbM1aTXb9bQtVJAAAAAAAADLIyABAAAAAACWR0ACAAAAAAAsjx4kAAAAAADTMSQZhrl6dpjratoeKkgAAAAAAIDlEZAAAAAAAADLIyABAAAAAACWRw8SAAAAAID5GObrQUITkhOLChIAAAAAAGB5BCQAAAAAAMDyCEgAAAAAAIDlEZAAAAAAAEzJMNmfY+X3+/Xcc8/p3HPP1aBBgzRp0iRlZ2dLkurq6nT//fdryJAhGjt2rLZu3Rr02Mcff1yzZs06DqNouwhIAAAAAACwgOeff15vvvmmfve732nJkiWy2Wy69dZb5Xa7tWzZMu3cuVPLli3T+eefr+nTp9c/LicnR++//75uv/32EI7+xCMgAQAAAADA5NxutxYuXKi7775b559/vvr166dnnnlG+/bt06pVq5SVlaURI0aoV69euvLKK5WVlVX/2NmzZ2vy5MmKi4sL4RWceHzM7wng7JKks9Y/H+phoI2xOQM/bv1f/ZUMtzfEo0FbFO7yhXoIaINs4eGSpNhf/16GxxPi0QBoNw7OHdN+LzF34AfsHTuFeggnRbduadq+dW2oh3FcdeuWpry8PE2YMKHJYz766KNGt2/dulVVVVUaPnx4/ba4uDj1799fGzZsUHp6uv773//K7Xbr888/V3p6uiTpq6++0jfffKPZs2cf34tpgwhITgB7eJgiuqeEehhoY/Ly8qS8PHXp0iXUQwHQjjB3AGgN5g5ACgsLU69e3UM9jONu//79rXpcfn6+JKlz585B2zt16qS9e/dqypQp+uCDDzR48GDFxsZq7ty5kqSnn35av/zlL+V0Oo9p3O0BAQlwkhxMeZtKdAGgMcwdAFqDuQMwr0GDBrXqZ7umpkaSGgQdLpdLZWVliomJ0dKlS1VYWKiEhASFhYXpww8/VFVVla666iotWLBAf/3rX5WcnKzHH39cGRkZx+V62hJ6kAAAAAAAYHIRERGSAr1IDldXV6fIyMj6rzt27KiwsDD5fD7NmTNHU6ZM0ZYtW7Ro0SItW7ZMN954ox566KGTOvaThYAEAAAAAACTO3hrTUFBQdD2goICpaamNjj+rbfeUkpKikaMGKHPPvtMQ4YMUYcOHXTxxRdry5YtqqysPCnjPpkISAAAAAAAMLl+/fopJiZG69evr99WXl6uzMxMnXXWWUHHVldX609/+pMefPBBSZLNZpPPF/hAAc+Bxs9+v/8kjfzkoQcJAAAAAAAm53Q6NX78eM2ePVsdOnRQWlqaZs2apdTUVI0ePTro2IULF2rYsGHq37+/pEDfk3nz5mnz5s1as2aNTjnlFFN+5C8BCQAAAAAAFnDPPffI6/Vq2rRpqq2t1dChQ7VgwYKgxq1FRUV67bXXtHz58vptgwcP1vjx43XLLbcoJSVFTz75ZCiGf8LZDMMwQj0IwAouuugiSXSTB9AyzB0AWoO5AwBajoAEAAAAAABYHk1aAQBtysiRI9W3b1/t2bMn1EM5Icx+fQAAAO0VPUgAAMBJ9corr6iiokITJ040ZYM3AADQPhGQAABwEqWnp8vpdCo8PDzUQwmZV199Vbm5uRo7diwBCQAAaDMISAAAOIkWLVoU6iEAAACgEfQgAQAAAAAAlkcFCQCg3fB6vXrrrbe0cuVKZWVlqa6uTmlpaRozZoxuvfVWxcTEBB2/fft2vf/++1qzZo1yc3NVWlqqhIQEDR48WJMmTdKQIUMafZ6+fftKkrZt26YPPvhAr776qrZt26aKigp99NFH6tq1a9AxH3/8sV588UVlZmbKbrfrjDPO0P3336/+/fs3OPfIkSOVm5tbf57GnrMl55OknJwczZ07V2vXrlVNTY169Oih66+/XjfccEOTz3c0zXkNWvr6rlixQr/61a/qvz74MaQHvfrqqxo2bJikln+vAQAAjhUBCQCgXaisrNQdd9yhDRs2yG63q3PnzoqOjtbOnTv1wgsvaNWqVXrttdeUlJRU/5iZM2dq3bp1iouLU3Jysjp16qS8vDytWrVKq1ev1lNPPaUrr7yyyed88cUX9Yc//EEdO3ZUjx49lJub2+CYxYsXa8aMGerYsaN69uypnTt36pNPPtGmTZu0bNky9e7du0XX2dLzbd26VRMmTFB5ebkiIiLUu3dvlZaW6rHHHtP333/fouduzJFeg5a+vklJSRoyZIi++eYbud1uDRgwQE6ns35/bGyspNZ9rwEAAI6ZAQBAG3LhhRcaGRkZxu7du4O233fffUZGRoYxceJEIycnp357aWmp8Ytf/MLIyMgw7r777qDHvPfee8bWrVuDtvn9fmPVqlXG4MGDjSFDhhgVFRUNxpCRkWFkZGQYp512mrFkyRLD7/cbhmEYHo/H8Hg8QccMGjTIWL58ef1jKyoqjIkTJxoZGRnGvffe2+zra835fD6fccUVVxgZGRnGLbfcYpSWltbve//9940BAwYYp512WqPPdzTNeQ1a+/o29Roc1JrvNQAAwLGiBwkAoM3bunWr3n33XaWlpWnevHlKT0+v3xcfH6+nn35anTt31r/+9a+gCodLLrmk/laRg2w2m0aNGqWJEyeqsrJS//73v5t83uuvv17XXXedbDabJCksLExhYcHFl9dcc41++tOf1n8dExNTfxvJJ5980uJrbcn51qxZo+3btyshIUFz5sxRfHx8/b4xY8botttuk8fjafEYDnek1+BYX9/GtPZ7DQAAcKy4xQYA0OZ9+OGHkgK/kDfWeyIyMlI//vGPtWLFCm3cuFFpaWn1+/Ly8vTOO+/o22+/VUlJSX1gUFxcLCnwC3lTt9lcffXVRx3bNddc02Bb37595XK5VFFRoZKSEiUmJh79IltxvrVr10qSLr744vrbUw7305/+VPPmzWv2czfmaK/Bsby+jTmW7zUAAMCxICABALR527dvlxT45fmLL75o9Ji8vDxJ0r59++q3/e1vf9NvfvMb1dXVNXnusrKyJvc1p39It27dGt3eoUMH7d27V9XV1S0KSFpyvl27dklSgyqOg9LS0hQTE6PKyspmP/8PHek1ONbXtzGt/V4DAAAcKwISAECbV1FRIUnKzs5Wdnb2EY89+Mt6Tk6Opk+fLo/Ho0mTJumqq65Senq6oqOjZbPZ9NZbb2natGnyer1NnisqKuqoY2vqGLs9cBerYRhHPUdrz1dTUyNJio6ObvJ80dHRxxSQNDWe4/H6NqY132sAAIDjgYAEANDmHfwl/Xe/+52uvfbaZj3mvffek8fj0eWXX66HH364wf69e/ce1zGGQmRkpCSpurq6yWOqqqpOyHOfqNe3Nd9rAACA44EmrQCANq9Pnz6SDt1+0RwHG3ieccYZje7funXrsQ8sxHr06CFJ2rZtW6P78/Lyjql65EhO1Ovbmu81AADA8UBAAgBo80aNGiVJWrlypUpKSpr1GJfLJUkqLCxssO/7779v8aertEXnnHOOJOlf//pXo0HIihUrTthzH8vrGxERIanxW2Ra870GAAA4HghIAABt3sCBA3XppZeqtLRUkyZNUmZmZtB+n8+n9evX64EHHpDb7ZYknXnmmZKkxYsXa8uWLfXH7ty5U/fee6/Cw8NP3gWcIGeffbb69u2rkpISTZkyReXl5fX7PvzwQ7344osn7DqP5fU9+NG9n332WYN9rfleAwAAHA/0IAEAtAtPPPGEysvLtWbNGo0dO1ZdunRRcnKyampqlJOTo9raWknSzJkzJQUqEQYPHqwvv/xS48aNU48ePeRwOJSVlaWOHTvqzjvv1Ny5c0N4RcfObrfr6aef1oQJE/Tvf/9b5513nnr37q2SkhLl5uZqwoQJWr16tXJzc+VwOI7rcx/L63vppZfqP//5jx577DG98cYbSkhIkCT9+te/1qmnntri7zUAAMDxQAUJAKBdiI6O1vz58zV79myNGDFCNTU1yszMVElJifr27atbb71Vb731Vv2tH2FhYVqwYIEmTJigpKQk5eTkqLy8XNdcc41WrFihlJSUEF/R8dGvXz8tX75cl19+uVwul7KyshQdHa1HH31U06ZNq2/geqRPummNY3l9f/KTn2jq1Knq27evcnJy9Nlnn+mzzz6rr4Bp6fcaAADgeLAZLf38QQAA0C6UlJRo+PDhiouL04YNG0I9HAAAgDaNChIAAEzqYJPWpj5pBgAAAIcQkAAA0I5t27ZNS5YsUVVVVf02wzD0j3/8Q88++6wk6frrrw/V8AAAANoNbrEBAKAdW79+vX7+85/L4XCoS5cuSkhI0O7du1VaWipJ+r//+z/99re/De0gAQAA2gECEgAA2rGioiItXLhQa9as0d69e1VZWamYmBideuqpuu6663TZZZeFeogAAADtAgEJAAAAAACwPHqQAAAAAAAAyyMgAQAAAAAAlkdAAgAAAAAALI+ABAAAAAAAWB4BCQAAAAAAsDwCEgAAAAAAYHkEJAAAAAAAwPIISAAAAAAAgOURkAAAAAAAAMsjIAEAAAAAAJb3/wG3VRKwJXPbbgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "\n", "import tensorflow as tf\n", @@ -2494,9 +5304,7 @@ { "cell_type": "markdown", "id": "f4cd2098", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Fine-tuning neural network hyperparameters\n", "\n", @@ -2522,9 +5330,7 @@ { "cell_type": "markdown", "id": "d214193a", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Hidden layers\n", "\n", @@ -2545,9 +5351,7 @@ { "cell_type": "markdown", "id": "e9191060", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Which activation function should I use?\n", "\n", @@ -2576,9 +5380,7 @@ { "cell_type": "markdown", "id": "0c99bf5d", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Is the Logistic activation function (Sigmoid) our choice?\n", "\n", @@ -2608,9 +5410,7 @@ { "cell_type": "markdown", "id": "6bb9e82b", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The derivative of the Logistic funtion\n", "\n", @@ -2646,9 +5446,7 @@ { "cell_type": "markdown", "id": "ac8e2413", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The RELU function family\n", "\n", @@ -2671,9 +5469,7 @@ { "cell_type": "markdown", "id": "9fb72862", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "ELU(z) = \\left\\{\\begin{array}{cc} \\alpha\\left( \\exp{(z)}-1\\right) & z < 0,\\\\ z & z \\ge 0.\\end{array}\\right.\n", @@ -2683,9 +5479,7 @@ { "cell_type": "markdown", "id": "2dc439ba", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Which activation function should we use?\n", "\n", @@ -2706,9 +5500,7 @@ { "cell_type": "markdown", "id": "1ebcb4c9", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## More on activation functions, output layers\n", "\n", @@ -2726,9 +5518,7 @@ { "cell_type": "markdown", "id": "fb1c86c5", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Batch Normalization\n", "\n", @@ -2748,9 +5538,7 @@ { "cell_type": "markdown", "id": "116ff585", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Dropout\n", "\n", @@ -2766,9 +5554,7 @@ { "cell_type": "markdown", "id": "9b2393c6", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Gradient Clipping\n", "\n", @@ -2785,9 +5571,7 @@ { "cell_type": "markdown", "id": "5b05d371", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## A very nice website on Neural Networks\n", "\n", @@ -2797,9 +5581,7 @@ { "cell_type": "markdown", "id": "fe8cb1f9", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## A top-down perspective on Neural networks\n", "\n", @@ -2842,9 +5624,7 @@ { "cell_type": "markdown", "id": "03e2eeb7", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Limitations of supervised learning with deep networks\n", "\n", @@ -2870,7 +5650,25 @@ ] } ], - "metadata": {}, + "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" + } + }, "nbformat": 4, "nbformat_minor": 5 } diff --git a/doc/pub/week42/ipynb/week42.ipynb b/doc/pub/week42/ipynb/week42.ipynb index b6be36723..d0a476381 100644 --- a/doc/pub/week42/ipynb/week42.ipynb +++ b/doc/pub/week42/ipynb/week42.ipynb @@ -3,9 +3,7 @@ { "cell_type": "markdown", "id": "abd8d829", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", @@ -15,9 +13,7 @@ { "cell_type": "markdown", "id": "094896b1", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "# Week 42 Solving differential equations and Convolutional (CNN)\n", "**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n", @@ -30,9 +26,7 @@ { "cell_type": "markdown", "id": "154f7248", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Plan for week 42\n", "\n", @@ -70,9 +64,7 @@ { "cell_type": "markdown", "id": "b54219f8", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Using Automatic differentiation\n", "\n", @@ -83,9 +75,7 @@ { "cell_type": "markdown", "id": "e305d5ab", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Back propagation and automatic differentiation\n", "\n", @@ -100,9 +90,7 @@ { "cell_type": "markdown", "id": "9e2ee8a5", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Solving ODEs with Deep Learning\n", "\n", @@ -123,9 +111,7 @@ { "cell_type": "markdown", "id": "7be55a45", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Ordinary Differential Equations\n", "\n", @@ -137,9 +123,7 @@ { "cell_type": "markdown", "id": "ae3ed46d", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -154,9 +138,7 @@ { "cell_type": "markdown", "id": "545ce0d6", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where $g(x)$ is the function to find, and $g^{(n)}(x)$ is the $n$-th derivative of $g(x)$.\n", "\n", @@ -170,9 +152,7 @@ { "cell_type": "markdown", "id": "3886d27e", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The trial solution\n", "\n", @@ -182,9 +162,7 @@ { "cell_type": "markdown", "id": "0615c51a", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -200,9 +178,7 @@ { "cell_type": "markdown", "id": "760fd5b3", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where $h_1(x)$ is a function that makes $g_t(x)$ satisfy a given set\n", "of conditions, $N(x,P)$ a neural network with weights and biases\n", @@ -221,9 +197,7 @@ { "cell_type": "markdown", "id": "dd82a779", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Minimization process\n", "\n", @@ -238,9 +212,7 @@ { "cell_type": "markdown", "id": "5a885957", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "C\\left(x, P\\right) = \\big(f\\left(x, \\, g(x), \\, g'(x), \\, g''(x), \\, \\dots \\, , \\, g^{(n)}(x)\\right)\\big)^2\n", @@ -250,9 +222,7 @@ { "cell_type": "markdown", "id": "e0f8495d", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "If $N$ inputs are given as a vector $\\boldsymbol{x}$ with elements $x_i$ for $i = 1,\\dots,N$,\n", "the cost function becomes" @@ -261,9 +231,7 @@ { "cell_type": "markdown", "id": "4656e257", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -278,9 +246,7 @@ { "cell_type": "markdown", "id": "58c60357", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "The neural net should then find the parameters $P$ that minimizes the cost function in\n", "([3](#cost)) for a set of $N$ training samples $x_i$." @@ -289,9 +255,7 @@ { "cell_type": "markdown", "id": "a0e58166", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Minimizing the cost function using gradient descent and automatic differentiation\n", "\n", @@ -305,9 +269,7 @@ { "cell_type": "markdown", "id": "9eb6e1a7", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Example: Exponential decay\n", "\n", @@ -317,9 +279,7 @@ { "cell_type": "markdown", "id": "83e80946", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -334,9 +294,7 @@ { "cell_type": "markdown", "id": "8c004b4f", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "with $g(0) = g_0$ for some chosen initial value $g_0$.\n", "\n", @@ -346,9 +304,7 @@ { "cell_type": "markdown", "id": "d54fb1ec", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -364,9 +320,7 @@ { "cell_type": "markdown", "id": "18b9d06f", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "Having an analytical solution at hand, it is possible to use it to compare how well a neural network finds a solution of ([4](#solve_expdec))." ] @@ -374,9 +328,7 @@ { "cell_type": "markdown", "id": "97866a0a", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The function to solve for\n", "\n", @@ -386,9 +338,7 @@ { "cell_type": "markdown", "id": "69c6fcd3", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -403,9 +353,7 @@ { "cell_type": "markdown", "id": "d499d3e5", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where $g(0) = g_0$ with $\\gamma$ and $g_0$ being some chosen values.\n", "\n", @@ -415,9 +363,7 @@ { "cell_type": "markdown", "id": "c90ba23f", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The trial solution\n", "To begin with, a trial solution $g_t(t)$ must be chosen. A general trial solution for ordinary differential equations could be" @@ -426,9 +372,7 @@ { "cell_type": "markdown", "id": "f58b1555", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "g_t(x, P) = h_1(x) + h_2(x, N(x, P))\n", @@ -438,9 +382,7 @@ { "cell_type": "markdown", "id": "26c33544", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "with $h_1(x)$ ensuring that $g_t(x)$ satisfies some conditions and $h_2(x,N(x, P))$ an expression involving $x$ and the output from the neural network $N(x,P)$ with $P $ being the collection of the weights and biases for each layer. For now, it is assumed that the network consists of one input layer, one hidden layer, and one output layer." ] @@ -448,9 +390,7 @@ { "cell_type": "markdown", "id": "dbad6700", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Setup of Network\n", "\n", @@ -468,9 +408,7 @@ { "cell_type": "markdown", "id": "d3f1064b", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -485,9 +423,7 @@ { "cell_type": "markdown", "id": "eb16a920", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Reformulating the problem\n", "\n", @@ -504,9 +440,7 @@ { "cell_type": "markdown", "id": "d168e8b4", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "g_t(x, P) = g_0 + x \\cdot N(x, P)\n", @@ -516,9 +450,7 @@ { "cell_type": "markdown", "id": "d0d39e8c", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "has been chosen such that it already solves the condition $g(0) = g_0$. What remains, is to find $P$ such that" ] @@ -526,9 +458,7 @@ { "cell_type": "markdown", "id": "32b55a44", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -543,9 +473,7 @@ { "cell_type": "markdown", "id": "01755fdb", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "is fulfilled as *best as possible*." ] @@ -553,9 +481,7 @@ { "cell_type": "markdown", "id": "4f44cb94", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## More technicalities\n", "\n", @@ -569,9 +495,7 @@ { "cell_type": "markdown", "id": "a31c38af", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\min_{P}\\Big\\{ \\big(g_t'(x, P) - ( -\\gamma g_t(x, P) \\big)^2 \\Big\\}\n", @@ -581,9 +505,7 @@ { "cell_type": "markdown", "id": "99ce7f31", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "(the notation $\\min_{P}\\{ f(x, P) \\}$ means that we desire to find $P$ that yields the minimum of $f(x, P)$)\n", "\n", @@ -593,9 +515,7 @@ { "cell_type": "markdown", "id": "c483383a", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\min_{P_{\\text{hidden} }, \\ P_{\\text{output} }}\\Big\\{ \\big(g_t'(x, \\{ P_{\\text{hidden} }, P_{\\text{output} }\\}) - ( -\\gamma g_t(x, \\{ P_{\\text{hidden} }, P_{\\text{output} }\\}) \\big)^2 \\Big\\}\n", @@ -605,9 +525,7 @@ { "cell_type": "markdown", "id": "3c8ded88", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "for an input value $x$." ] @@ -615,9 +533,7 @@ { "cell_type": "markdown", "id": "649c7111", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## More details\n", "\n", @@ -627,9 +543,7 @@ { "cell_type": "markdown", "id": "fb9dffec", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -644,9 +558,7 @@ { "cell_type": "markdown", "id": "52b7cf5b", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "Letting $\\boldsymbol{x}$ be a vector with elements $x_i$ and $C(\\boldsymbol{x}, P) = \\frac{1}{N} \\sum_i \\big(g_t'(x_i, P) - ( -\\gamma g_t(x_i, P) \\big)^2$ denote the cost function, the minimization problem that our network must solve, becomes" ] @@ -654,9 +566,7 @@ { "cell_type": "markdown", "id": "4466939f", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\min_{P} C(\\boldsymbol{x}, P)\n", @@ -666,9 +576,7 @@ { "cell_type": "markdown", "id": "272354c9", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "In terms of $P_{\\text{hidden} }$ and $P_{\\text{output} }$, this could also be expressed as\n", "\n", @@ -680,9 +588,7 @@ { "cell_type": "markdown", "id": "74b1e07e", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## A possible implementation of a neural network\n", "\n", @@ -696,9 +602,7 @@ { "cell_type": "markdown", "id": "c7253f4a", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Technicalities\n", "\n", @@ -708,9 +612,7 @@ { "cell_type": "markdown", "id": "407ba791", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{aligned}\n", @@ -730,9 +632,7 @@ { "cell_type": "markdown", "id": "1c227fc8", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Final technicalities I\n", "\n", @@ -742,9 +642,7 @@ { "cell_type": "markdown", "id": "c2f07984", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{aligned}\n", @@ -765,9 +663,7 @@ { "cell_type": "markdown", "id": "c2da4d4c", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Final technicalities II\n", "\n", @@ -781,9 +677,7 @@ { "cell_type": "markdown", "id": "22eba71d", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "f(z) = \\frac{1}{1 + \\exp{(-z)}}\n", @@ -793,9 +687,7 @@ { "cell_type": "markdown", "id": "8418f5be", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "It is possible to use other activations functions for the hidden layer also.\n", "\n", @@ -817,9 +709,7 @@ { "cell_type": "markdown", "id": "c4fd8194", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Final technicalities III\n", "\n", @@ -829,9 +719,7 @@ { "cell_type": "markdown", "id": "0fe93c43", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{aligned}\n", @@ -850,9 +738,7 @@ { "cell_type": "markdown", "id": "1a3fc54b", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Final technicalities IV\n", "\n", @@ -862,9 +748,7 @@ { "cell_type": "markdown", "id": "df15e0a7", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\boldsymbol{z}_{1}^{\\text{output}} =\n", @@ -881,9 +765,7 @@ { "cell_type": "markdown", "id": "b15b66c2", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "In this case we seek a continuous range of values since we are approximating a function. This means that after computing $\\boldsymbol{z}_{1}^{\\text{output}}$ the neural network has finished its feed forward step, and $\\boldsymbol{z}_{1}^{\\text{output}}$ is the final output of the network." ] @@ -891,9 +773,7 @@ { "cell_type": "markdown", "id": "d81e1727", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Back propagation\n", "\n", @@ -905,9 +785,7 @@ { "cell_type": "markdown", "id": "6db9a985", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "C(\\boldsymbol{x}, P) = \\frac{1}{N} \\sum_i \\big(g_t'(x_i, P) - ( -\\gamma g_t(x_i, P) \\big)^2\n", @@ -917,9 +795,7 @@ { "cell_type": "markdown", "id": "f7f7ec9b", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "In order to minimize the cost function, an optimization method must be chosen.\n", "\n", @@ -929,9 +805,7 @@ { "cell_type": "markdown", "id": "913add99", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Gradient descent\n", "\n", @@ -946,9 +820,7 @@ { "cell_type": "markdown", "id": "ef0ca791", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\boldsymbol{\\omega}_{\\text{new} } = \\boldsymbol{\\omega} - \\lambda \\nabla_{\\boldsymbol{\\omega}} C(\\boldsymbol{x}, \\boldsymbol{\\omega})\n", @@ -958,9 +830,7 @@ { "cell_type": "markdown", "id": "e054149f", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "for a number of iterations or until $ \\big|\\big| \\boldsymbol{\\omega}_{\\text{new} } - \\boldsymbol{\\omega} \\big|\\big|$ becomes smaller than some given tolerance.\n", "\n", @@ -980,9 +850,7 @@ { "cell_type": "markdown", "id": "93cd08f7", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{aligned}\n", @@ -995,9 +863,7 @@ { "cell_type": "markdown", "id": "bd12f38d", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The code for solving the ODE" ] @@ -1006,11 +872,28 @@ "cell_type": "code", "execution_count": 1, "id": "a65da691", - "metadata": { - "collapsed": false, - "editable": true - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial cost: 367.01\n", + "Final cost: 0.0666807\n", + "Max absolute difference: 0.0437499\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "%matplotlib inline\n", "\n", @@ -1163,9 +1046,7 @@ { "cell_type": "markdown", "id": "3fd575a2", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The network with one input layer, specified number of hidden layers, and one output layer\n", "\n", @@ -1178,11 +1059,35 @@ "cell_type": "code", "execution_count": 2, "id": "b02e1252", - "metadata": { - "collapsed": false, - "editable": true - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3208: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n", + " return asarray(a).size\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial cost: 324.246\n", + "Final cost: 0.119936\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import autograd.numpy as np\n", "from autograd import grad, elementwise_grad\n", @@ -1347,9 +1252,7 @@ { "cell_type": "markdown", "id": "c1e9c039", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Example: Population growth\n", "\n", @@ -1360,9 +1263,7 @@ { "cell_type": "markdown", "id": "c6a1e130", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -1377,9 +1278,7 @@ { "cell_type": "markdown", "id": "9945d24c", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where $g(t)$ is the population density at time $t$, $\\alpha > 0$ the growth rate and $A > 0$ is the maximum population number in the environment.\n", "Also, at $t = 0$ the population has the size $g(0) = g_0$, where $g_0$ is some chosen constant.\n", @@ -1393,9 +1292,7 @@ { "cell_type": "markdown", "id": "f264d698", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Setting up the problem\n", "\n", @@ -1406,9 +1303,7 @@ { "cell_type": "markdown", "id": "4981f1e1", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -1423,9 +1318,7 @@ { "cell_type": "markdown", "id": "8fd7ea35", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where $g(0) = g_0$.\n", "\n", @@ -1435,9 +1328,7 @@ { "cell_type": "markdown", "id": "98e96613", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The trial solution\n", "\n", @@ -1462,9 +1353,7 @@ { "cell_type": "markdown", "id": "64c1380d", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The program using Autograd\n", "\n", @@ -1475,11 +1364,36 @@ "cell_type": "code", "execution_count": 3, "id": "a6e57516", - "metadata": { - "collapsed": false, - "editable": true - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3208: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n", + " return asarray(a).size\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial cost: 0.221805\n", + "Final cost: 0.000417932\n", + "The max absolute difference between the solutions is: 0.00424909\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import autograd.numpy as np\n", "from autograd import grad, elementwise_grad\n", @@ -1649,9 +1563,7 @@ { "cell_type": "markdown", "id": "1e447a21", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Using forward Euler to solve the ODE\n", "\n", @@ -1669,9 +1581,7 @@ { "cell_type": "markdown", "id": "5c370209", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{aligned}\n", @@ -1684,9 +1594,7 @@ { "cell_type": "markdown", "id": "6af02d57", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "along with the condition that $g(0) = g_0$.\n", "\n", @@ -1698,9 +1606,7 @@ { "cell_type": "markdown", "id": "95ddd05b", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{aligned}\n", @@ -1714,9 +1620,7 @@ { "cell_type": "markdown", "id": "47e35a4b", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "Now, if $g_i = g(t_i)$ then" ] @@ -1724,9 +1628,7 @@ { "cell_type": "markdown", "id": "40698593", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -1746,9 +1648,7 @@ { "cell_type": "markdown", "id": "ad3cd476", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "for $i \\geq 1$ and $g_0 = g(t_0) = g(0) = g_0$.\n", "\n", @@ -1760,11 +1660,48 @@ "cell_type": "code", "execution_count": 4, "id": "dc1e94ba", - "metadata": { - "collapsed": false, - "editable": true - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3208: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n", + " return asarray(a).size\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial cost: 0.221805\n", + "Final cost: 0.000417932\n", + "The max absolute difference between the solutions is: 0.00424909\n", + "Max absolute difference between Euler method and analytical: 0.011225\n", + "Max absolute difference between deep neural network and analytical: 0.00424909\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Assume that all function definitions from the example program using Autograd\n", "# are located here.\n", @@ -1836,9 +1773,7 @@ { "cell_type": "markdown", "id": "548bed23", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Example: Solving the one dimensional Poisson equation\n", "\n", @@ -1848,9 +1783,7 @@ { "cell_type": "markdown", "id": "2719f69d", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -1865,9 +1798,7 @@ { "cell_type": "markdown", "id": "e22196f1", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where $f(x)$ is a given function for $x \\in (0,1)$.\n", "\n", @@ -1877,9 +1808,7 @@ { "cell_type": "markdown", "id": "7d008f7f", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{align*}\n", @@ -1892,9 +1821,7 @@ { "cell_type": "markdown", "id": "52d2549c", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "This equation can be solved numerically using programs where e.g Autograd and TensorFlow are used.\n", "The results from the networks can then be compared to the analytical solution.\n", @@ -1904,9 +1831,7 @@ { "cell_type": "markdown", "id": "489dfc20", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The specific equation to solve for\n", "\n", @@ -1916,9 +1841,7 @@ { "cell_type": "markdown", "id": "89cd1faa", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "-g''(x) = f(x),\\qquad x \\in (0,1)\n", @@ -1928,9 +1851,7 @@ { "cell_type": "markdown", "id": "07cdc931", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where $f(x)$ is a given function, along with the chosen conditions" ] @@ -1938,9 +1859,7 @@ { "cell_type": "markdown", "id": "9358036f", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -1955,9 +1874,7 @@ { "cell_type": "markdown", "id": "bfc66633", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "In this example, we consider the case when $f(x) = (3x + x^2)\\exp(x)$.\n", "\n", @@ -1967,9 +1884,7 @@ { "cell_type": "markdown", "id": "9036d243", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "g_t(x) = x \\cdot (1-x) \\cdot N(P,x)\n", @@ -1979,9 +1894,7 @@ { "cell_type": "markdown", "id": "cbe88af0", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "The analytical solution for this problem is" ] @@ -1989,9 +1902,7 @@ { "cell_type": "markdown", "id": "a347107b", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "g(x) = x(1 - x)\\exp(x)\n", @@ -2001,9 +1912,7 @@ { "cell_type": "markdown", "id": "7433b8f3", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Solving the equation using Autograd" ] @@ -2012,11 +1921,36 @@ "cell_type": "code", "execution_count": 5, "id": "d0813f2d", - "metadata": { - "collapsed": false, - "editable": true - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3208: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n", + " return asarray(a).size\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial cost: 457.256\n", + "Final cost: 0.00310113\n", + "The max absolute difference between the solutions is: 0.000464088\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA04AAANVCAYAAABPsa7ZAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAA9hAAAPYQGoP6dpAAC47UlEQVR4nOzdd3gU5cLG4d9seichDULvIB0E6SJNmmAFVBCwl3PEco69oR/2fsQuggJiARQQFKVIb4JIkd5CCzWB9Oy+3x8ccgwJECDhTXnu69oLMpnZfXazO7PPzuw7jjHGICIiIiIiIqflsh1ARERERESkqFNxEhEREREROQsVJxERERERkbNQcRIRERERETkLFScREREREZGzUHESERERERE5CxUnERERERGRs1BxEhEREREROQsVJxERERERkbNQcSriPv/8cxzHyb54e3tToUIFhgwZwu7duwv0tjIyMrjrrrsoV64cXl5eNG7cuECvX05vwoQJXHLJJQQEBOA4DqtWrbIdqdBUqVKFwYMHX/Tb3bNnD88++2yRe2wdx+G+++6zHQPHcXj22WfPebmT66jt27cXeKaiZvHixVx//fWUK1cOX19fYmNjue6661i0aFGueU9dd/v7+xMbG0vHjh158cUXSUhIyLXMs88+m2OZUy+l4TG25fLLL+fyyy8/63wjRoxg8uTJBX77p7vek8+j5cuXF/htFhVz5szBcRzmzJlzzsuOGzeOt956K8/fne86Lb9sbMsu5D6NHDmSzz//PNf07du34zhOnr+T3LxtB5D8GTVqFHXq1CE1NZXffvuNF198kblz5/Lnn38SFBRUILfx/vvv8+GHH/Luu+/SrFkzgoODC+R65cwOHDjAwIEDufLKKxk5ciR+fn7UqlXLdqwSZ8+ePTz33HNUqVJFHwoUoJ49e7Jo0SLKlStnO0qhevfddxk2bBgtWrTglVdeoXLlyuzcuZP33nuPtm3b8vbbb+dZgE+uuzMzM0lISGD+/Pm8/PLLvPbaa0yYMIHOnTvnWmbGjBmEhYXlml7SH+PiYMSIEVx33XX07du3WFxvSTdu3DjWrFnDsGHDcv1u0aJFVKhQ4eKHKqJGjhxJZGRkrrJXrlw5Fi1aRPXq1e0EK2ZUnIqJ+vXr07x5cwA6duyI2+3m+eefZ/Lkydx0000XdN0pKSkEBgayZs0aAgICCvTT79TUVAICAgrs+kqijRs3kpmZyc0330yHDh1sxzknbrebrKws/Pz8bEcpdkrKayMqKoqoqCjbMQrVggULGDZsGD169GDSpEl4e/9v09m/f3+uvvpq7r//fpo0aUKbNm1yLPv3dTfAtddeywMPPEDbtm255ppr2LRpEzExMTmWadasGZGRkYV7p0qIk9svkVNddtlltiMUC35+fnqszoEO1SumTj7Jd+zYAYAxhpEjR9K4cWMCAgIIDw/nuuuuY+vWrTmWu/zyy6lfvz6//fYbrVu3JjAwkKFDh+I4Dp988gmpqanZh4Wc3G2blpbGY489RtWqVfH19SUuLo57772Xo0eP5rjuKlWq0KtXLyZOnEiTJk3w9/fnueeey94NP27cOB555BHKlStHcHAwvXv3Zv/+/Rw7dow77riDyMhIIiMjGTJkCMePH89x3e+99x7t27cnOjqaoKAgGjRowCuvvEJmZmae92/ZsmW0a9eOwMBAqlWrxksvvYTH48kx79GjR3nooYeoVq0afn5+REdH06NHD/7666/seTIyMnjhhReoU6cOfn5+REVFMWTIEA4cOJCvv9MPP/xAq1atCAwMJCQkhC5duuQ4rGfw4MG0bdsWgH79+uE4zhkPFzl52Mbs2bO5++67iYyMpGzZslxzzTXs2bMn1/wTJkygVatWBAUFERwcTLdu3Vi5cmWuxyyv2xw8eDBVqlTJ/vnk7vxXXnmFF154gapVq+Ln58fs2bNJS0vjoYceonHjxoSFhREREUGrVq34/vvv8/U45eXkIWxffPEFdevWJTAwkEaNGjF16tRc827atIkbb7yR6Oho/Pz8qFu3Lu+991727+fMmcOll14KwJAhQ7Kf488++yzTpk3DcRyWLVuWPf93332H4zj07Nkzx+00bNiQa6+9NvvnC31t5MUYw+OPP46Pjw8ff/zxGR+jb775hpYtWxIWFpb9XB86dGiOeXbu3MnNN9+c47F5/fXXc70e/u6PP/7AcRw+/fTTXL+bPn06juPwww8/AHkfqncur8O1a9fStWtXAgMDiYqK4t57783+m5zt0J3NmzczZMgQatasSWBgIHFxcfTu3Zs///wzx3wn10Hjx4/niSeeoHz58oSGhtK5c2c2bNhwxtsAePHFF3Ech/fffz9HaQLw9vZm5MiROI7DSy+9dNbrAqhUqRKvv/46x44d48MPP8zXMvk1btw4WrVqRXBwMMHBwTRu3DjX3/Gzzz6jUaNG+Pv7ExERwdVXX8369etzzDN48GCCg4P566+/6NatG0FBQZQrVy77Pi5evJi2bdsSFBRErVq1GD16dI7lTz4vZs6cyZAhQ4iIiCAoKIjevXvn2i7NnDmTPn36UKFCBfz9/alRowZ33nknBw8ezDHfyUMZf//9d6677jrCw8OzPyXP7zbQGJO9x9Df35+mTZsyffr0fD22juOQnJzM6NGjs9chf193rlmzhj59+hAeHo6/vz+NGzfO9bicz/UCHDt2rMDW+Xk5cOAA99xzD/Xq1SM4OJjo6GiuuOIK5s2bl2O+k9uB1157jTfeeIOqVasSHBxMq1atWLx4cY55ly9fTv/+/alSpQoBAQFUqVKFAQMGZL9vOZ0vvvgCx3HyPAR2+PDh+Pj4sGfPHi6//HKmTZvGjh07chzSelJeh7Xt3r2bO+64g4oVK+Lr60v58uW57rrr2L9/P0ChbMsKaz0N/3tNnOrU9XKVKlVYu3Ytc+fOzX6cTm7fT3eo3vz58+nUqRMhISEEBgbSunVrpk2bluft5Pc9SYlgpEgbNWqUAcyyZctyTH/77bcNYD766CNjjDG333678fHxMQ899JCZMWOGGTdunKlTp46JiYkx+/bty16uQ4cOJiIiwlSsWNG8++67Zvbs2Wbu3Llm0aJFpkePHiYgIMAsWrTILFq0yCQkJBiPx2O6detmvL29zVNPPWV+/vln89prr5mgoCDTpEkTk5aWln3dlStXNuXKlTPVqlUzn332mZk9e7ZZunSpmT17tgFM5cqVzeDBg82MGTPMBx98YIKDg03Hjh1Nly5dzMMPP2x+/vln8/LLLxsvLy/zj3/8I8f9feCBB8z7779vZsyYYWbNmmXefPNNExkZaYYMGZJjvg4dOpiyZcuamjVrmg8++MDMnDnT3HPPPQYwo0ePzp4vKSnJXHLJJSYoKMgMHz7c/PTTT+a7774z999/v5k1a5Yxxhi3222uvPJKExQUZJ577jkzc+ZM88knn5i4uDhTr149k5KScsa/3dixYw1gunbtaiZPnmwmTJhgmjVrZnx9fc28efOMMcZs3rzZvPfeewYwI0aMMIsWLTJr16496/OhWrVq5h//+If56aefzCeffGLCw8NNx44dc8z7f//3f8ZxHDN06FAzdepUM3HiRNOqVSsTFBSU4zY6dOhgOnTokOu2brnlFlO5cuXsn7dt22YAExcXZzp27Gi+/fZb8/PPP5tt27aZo0ePmsGDB5svvvjCzJo1y8yYMcM8/PDDxuVy5XjcjTnxPLnlllvO+NgZYwxgqlSpYlq0aGG+/vpr8+OPP5rLL7/ceHt7my1btmTPt3btWhMWFmYaNGhgxowZY37++Wfz0EMPGZfLZZ599lljjDGJiYnZj92TTz6Z/RzftWuXOXbsmPHx8TEjRozIvs677rrLBAQEmKCgIJORkWGMMWb//v3GcRwzcuRIY4wpkNfGyft57733GmOMSUtLM/379zchISFm+vTpZ3x8Fi5caBzHMf379zc//vijmTVrlhk1apQZOHBg9jwJCQkmLi7OREVFmQ8++MDMmDHD3HfffQYwd999d67H+5lnnsn+uUmTJqZNmza5bveGG24w0dHRJjMz0xjzv+fktm3bsufJ7+twz549pmzZsqZSpUrm888/Nz/++KMZOHCgqVKligHM7Nmzz/gYzJ071zz00EPm22+/NXPnzjWTJk0yffv2NQEBAeavv/7Knu/kOqhKlSrmpptuMtOmTTPjx483lSpVMjVr1jRZWVmnvY2srCwTGBhoWrZsecYsLVq0MIGBgdnXdbp190nHjx83Xl5eplOnTtnTnnnmGQOYffv2mczMzByXM2U86amnnjKAueaaa8w333xjfv75Z/PGG2+Yp556KnueESNGGMAMGDDATJs2zYwZM8ZUq1bNhIWFmY0bN2bPd8sttxhfX19Tt25d8/bbb5uZM2eaIUOGGMA89thjplatWubTTz81P/30k+nVq5cBzPLly7OXP3n/K1asaIYOHWqmT59uPvroIxMdHW0qVqxojhw5kj3v+++/b1588UXzww8/mLlz55rRo0ebRo0amdq1a2e//v7++FSuXNk88sgjZubMmWby5MnGmPxvA09ex6233pqdKS4uzsTGxua5Hvy7RYsWmYCAANOjR4/sdcjJdelff/1lQkJCTPXq1c2YMWPMtGnTzIABAwxgXn755fO+3sJY5+flr7/+Mnfffbf56quvzJw5c8zUqVPNrbfealwuV47X4cntQJUqVcyVV15pJk+ebCZPnmwaNGhgwsPDzdGjR7Pn/eabb8zTTz9tJk2aZObOnWu++uor06FDBxMVFWUOHDiQPd/J1+fJ20lPTzexsbHmpptuypExMzPTlC9f3lx//fXGmBPr/jZt2pjY2Njsx23RokXZ85+6TouPjzflypUzkZGR5o033jC//PKLmTBhghk6dKhZv369McYU+LassNfTJ5/Ppzp1vfz777+batWqmSZNmmQ/Tr///rsx5n9/01GjRmUvP2fOHOPj42OaNWtmJkyYYCZPnmy6du1qHMcxX331Va7byc/zs6RQcSriTj4pFy9ebDIzM82xY8fM1KlTTVRUlAkJCTH79u0zixYtMoB5/fXXcyy7a9cuExAQYP79739nT+vQoYMBzK+//prrtm655RYTFBSUY9qMGTMMYF555ZUc0ydMmJCjuBlzYiXi5eVlNmzYkGPekyvF3r1755g+bNgwA5h//vOfOab37dvXREREnPYxcbvdJjMz04wZM8Z4eXmZw4cP57p/S5YsybFMvXr1TLdu3bJ/Hj58uAHMzJkzT3s748ePN4D57rvvckxftmyZAbLfQJ8uY/ny5U2DBg2M2+3Onn7s2DETHR1tWrdunT3t5OPzzTffnPb6Tjr5fLjnnntyTH/llVcMYPbu3WuMMWbnzp3G29s7VwE9duyYiY2NNTfccEP2tHMtTtWrV8/xZiYvWVlZJjMz09x6662mSZMmOX53LsUpJibGJCUlZU/bt2+fcblc5sUXX8ye1q1bN1OhQgWTmJiYY/n77rvP+Pv7Zz8/Tv7d/r5xOKlt27bmiiuuyP65Ro0a5l//+pdxuVxm7ty5xpj/FeGTby4L4rVx8n7ee++95tChQ6Zt27YmLi7OrFq16qyPz2uvvWaAHG9UTvXoo4/m+Xq4++67jeM4OfKcukF+5513DJBjnsOHDxs/Pz/z0EMPZU87XXHKz+vwX//6l3EcJ9ebum7duuWrOJ0qKyvLZGRkmJo1a5oHHngge/rJ11iPHj1yzP/1118bIMebrVPt27fPAKZ///5nvO1+/foZwOzfv98Yc/biZIwxMTExpm7dutk/n3wTlNelevXqZ7z9rVu3Gi8vr1xvNv/uyJEj2W/Q/27nzp3Gz8/P3HjjjdnTbrnlllzrv8zMTBMVFWWA7Dddxhhz6NAh4+XlZR588MHsaSfv/9VXX53jthYsWGAA88ILL+SZ0ePxmMzMTLNjxw4DmO+//z7X4/P000/nWCa/28AjR44Yf3//02Y6W3EyxpigoKA811/9+/c3fn5+ZufOnTmmd+/e3QQGBp7xdXqm6y2MdX5+nFyHd+rUKcfjdXI70KBBgxxlfunSpQYw48ePP+N1Hj9+3AQFBZm33347e/qpxcmYE39rX1/f7NeTMf9bt55cJxtjTM+ePXNsp/7u1HXa0KFDjY+Pj1m3bl1+HoLszBeyLSvs9XR+i5MxxlxyySV5PsfzKk6XXXaZiY6ONseOHcuelpWVZerXr28qVKhgPB5Pjts52/OzJNGhesXEZZddho+PDyEhIfTq1YvY2FimT59OTEwMU6dOxXEcbr75ZrKysrIvsbGxNGrUKNfhLuHh4VxxxRX5ut1Zs2YB5Poy4fXXX09QUBC//vprjukNGzY87cAGvXr1yvFz3bp1AXIdDlW3bl0OHz6c43C9lStXctVVV1G2bFm8vLzw8fFh0KBBuN1uNm7cmGP52NhYWrRokSvX3w8PmD59OrVq1crzi9knTZ06lTJlytC7d+8cj2vjxo2JjY0942FEGzZsYM+ePQwcOBCX638vs+DgYK699loWL15MSkrKaZc/m6uuuirX/YP/Hbr5008/kZWVxaBBg3Jk9/f3p0OHDuc1etHfb9vHxyfX9G+++YY2bdoQHByMt7c3Pj4+fPrpp7kOAToXHTt2JCQkJPvnmJgYoqOjs+9nWloav/76K1dffTWBgYE57muPHj1IS0vLdfhIXjp16sSCBQtITU1lx44dbN68mf79+9O4cWNmzpwJwC+//EKlSpWoWbMmULCvjW3bttGqVSuSkpJYvHgxjRo1Omvmk4ce3nDDDXz99dd5jrI5a9Ys6tWrl+v1MHjwYIwx2fchLzfddBN+fn45Dt8YP3486enpDBky5Kz58vM6nDt3LvXr16devXo55hswYMBZrx8gKyuLESNGUK9ePXx9ffH29sbX15dNmzbl+bw72+vmQhhjAPI8bOZsy5zql19+YdmyZTkuZxvJbebMmbjdbu69997TzrNo0SJSU1NzPWcrVqzIFVdckes56zgOPXr0yP7Z29ubGjVqUK5cOZo0aZI9PSIiIsfr8u9O/Q5u69atqVy5MrNnz86elpCQwF133UXFihWz1x2VK1cGyPPv+PfDZYF8bwMXLVpEWlraaTNdiFmzZtGpUycqVqyYY/rgwYNJSUnJ87Czc3Ex1vkffPABTZs2xd/fP/vv8Ouvv+b5N+jZsydeXl6nzQNw/PhxHnnkEWrUqIG3tzfe3t4EBweTnJx81u3C3XffDZDjcOX//Oc/NGjQgPbt25/1vuRl+vTpdOzYMfu9x+kU5LassNfThSE5OZklS5Zw3XXX5RgkzMvLi4EDBxIfH5/rEOfCXLcWNSpOxcSYMWNYtmwZK1euZM+ePaxevTr7S8j79+/HGENMTAw+Pj45LosXL851nPi5jMx06NAhvL29c33523EcYmNjOXToUL6vOyIiIsfPvr6+Z5yelpYGnDj2t127duzevZu3336befPmsWzZsuzvsKSmpuZYvmzZsrlu28/PL8d8Bw4cOOtoO/v37+fo0aP4+vrmelz37duX63H9u5OPS16PR/ny5fF4PBw5cuSMt38mp97Hk4MznLyPJ4/XvvTSS3NlnzBhwhmzn01e92nixInccMMNxMXF8eWXX7Jo0SKWLVvG0KFDs/+O5+Nsf8tDhw6RlZXFu+++m+t+nnzDl5/72rlzZ9LT05k/fz4zZ84kMjKSJk2a0LlzZ3755RcAfv311xxFuyBfG0uXLmXjxo3069cv36NAtW/fnsmTJ2e/WapQoQL169dn/PjxOTKe7jl48venExERwVVXXcWYMWNwu93AiePZW7RowSWXXHLWfPl5HR46dCjXwAhAntPy8uCDD/LUU0/Rt29fpkyZwpIlS1i2bBmNGjXKtV7IK9Opr5u8REZGEhgYyLZt286YZfv27QQGBuZan51OcnIyhw4dyv5b/F2jRo1o3rx5jkv9+vXPeH0nv3d5pufP2dZLpz4fAgMD8ff3zzHN19c3z/vo6+ub52s9NjY2z2knb8vj8dC1a1cmTpzIv//9b3799VeWLl2a/YFHXn+bU/Pndxt48jZPl+lCXMhrLT8Ke53/xhtvcPfdd9OyZUu+++47Fi9ezLJly7jyyivP+7V044038p///IfbbruNn376iaVLl7Js2TKioqLO+JqDE+uAfv368eGHH+J2u1m9ejXz5s27oMGr8rPdL+htWWGvpwvDkSNHMMacU6bzWbcWVxpVr5ioW7dujpGZ/i4yMhLHcZg3b16eo5udOu1cPhEtW7YsWVlZHDhwIMcbRGMM+/bty/405XyuO78mT55McnIyEydOzPGp4IWcjycqKor4+PgzznPyS44zZszI8/d/3xNyqpMrkb179+b63Z49e3C5XISHh59D4nNzckSub7/99qyfpPr7+5OYmJhr+uk2tHn9jb/88kuqVq3KhAkTcvw+PT39XGKfs/Dw8OxPwU73SXvVqlXPej0tW7YkODiYX375he3bt9OpUyccx6FTp068/vrrLFu2jJ07d+YoTgX52ujXrx+xsbE88cQTeDwennzyybNmBujTpw99+vQhPT2dxYsX8+KLL3LjjTdSpUoVWrVqRdmyZU/7HATOOnLbkCFD+Oabb5g5cyaVKlVi2bJlvP/++/nKlh9ly5bNfsP3d/v27cvX8l9++SWDBg1ixIgROaYfPHiQMmXKFEREvLy86NixIzNmzCA+Pj7PN17x8fGsWLGC7t275/gU/kymTZuG2+3O17mD8uPkczA+Pj7XXo+TzrZeKoyR/PL6W+7bt48aNWoAJwZU+OOPP/j888+55ZZbsufZvHnzaa/z1NdSfreBJ+//6TL9fTCcc3Whr7ULdS7r/Lx8+eWXXH755ble38eOHTuvPImJiUydOpVnnnmGRx99NHt6eno6hw8fztd13H///XzxxRd8//33zJgxgzJlylzQKML52e4XxrasMNfTJz/YSE9Pz/Hcv5APR8PDw3G5XFafz0WZ9jiVAL169cIYw+7du3N9Stm8eXMaNGhw3tfdqVMn4MTK5O++++47kpOTs39fmE6uvP6+UjDGnHXEsTPp3r07GzduPOMu8F69enHo0CHcbneej2vt2rVPu2zt2rWJi4tj3LhxOQ7HSU5O5rvvvsseaa+wdOvWDW9vb7Zs2ZJn9r+X8CpVqrBx48YcG4ZDhw6xcOHCfN+e4zj4+vrm2NDs27fvgkYiyo/AwEA6duzIypUradiwYZ738+SbpTN9Aubj40P79u2ZOXMms2bNokuXLgC0a9cOb29vnnzyyewidVJBvzaefPJJ3nrrLZ5++mkee+yxc1rWz8+PDh068PLLLwNkj6LVqVMn1q1bx++//55j/jFjxuA4Dh07djzj9Xbt2pW4uDhGjRrFqFGj8Pf3z/dhdPnRoUMH1qxZw7p163JM/+qrr/K1vOM4ud4oT5s2rcBPDv7YY49hjOGee+7J3vt2ktvt5u6778YYk++/286dO3n44YcJCwvjzjvvLJCMXbt2xcvL64zFtlWrVgQEBOR6zsbHx2cfalbQxo4dm+PnhQsXsmPHjuzCmNf6HTin0Qbzuw287LLL8Pf3P22m/Dh1r+lJnTp1YtasWblGEhszZgyBgYFnHe75dNebX+eyzs9LXq+l1atXn/chho7jYIzJdZ2ffPJJrtfQ6TRr1ozWrVvz8ssvM3bsWAYPHpzrvJXn8rh1796d2bNnn3EkzcLclhXGevpk2V+9enWO6VOmTMnz9vPzWAUFBdGyZUsmTpyYY36Px8OXX35JhQoVSvW5JrXHqQRo06YNd9xxB0OGDGH58uW0b9+eoKAg9u7dy/z582nQoEH28cLnqkuXLnTr1o1HHnmEpKQk2rRpw+rVq3nmmWdo0qQJAwcOLOB7k3cGX19fBgwYwL///W/S0tJ4//33L+hQt2HDhjFhwgT69OnDo48+SosWLUhNTWXu3Ln06tWLjh070r9/f8aOHUuPHj24//77adGiBT4+PsTHxzN79mz69OnD1Vdfnef1u1wuXnnlFW666SZ69erFnXfeSXp6Oq+++ipHjx7N97DF56tKlSoMHz6cJ554gq1bt3LllVcSHh7O/v37Wbp0KUFBQdnDYQ8cOJAPP/yQm2++mdtvv51Dhw7xyiuvEBoamu/bOznU9j333MN1113Hrl27eP755ylXrhybNm0qrLsJwNtvv03btm1p164dd999N1WqVOHYsWNs3ryZKVOmZJfj6tWrExAQwNixY6lbty7BwcGUL18++9CDTp068dBDDwFk71kKCAigdevW/PzzzzRs2JDo6Ojs2y2M18b9999PcHAwd9xxB8ePH+edd9457Z6qp59+mvj4eDp16kSFChU4evQob7/9Nj4+PtnnA3vggQcYM2YMPXv2ZPjw4VSuXJlp06YxcuRI7r777rNu/Ly8vBg0aBBvvPEGoaGhXHPNNXmemPV8DRs2jM8++4zu3bszfPhwYmJiGDduXPYpAf7+/cC89OrVi88//5w6derQsGFDVqxYwauvvlrgJ71s06YNb731FsOGDaNt27bcd999VKpUKfsEuEuWLOGtt96idevWuZZds2ZN9vdNEhISmDdvHqNGjcLLy4tJkybleQ6sFStW5Pk416tX77SvyypVqvD444/z/PPPk5qayoABAwgLC2PdunUcPHiQ5557jjJlyvDUU0/x+OOPM2jQIAYMGMChQ4d47rnn8Pf355lnnrnwB+sUy5cv57bbbuP6669n165dPPHEE8TFxXHPPfcAUKdOHapXr86jjz6KMYaIiAimTJmS/d3C/MjvNjA8PJyHH36YF154IUemZ599Nt+H6jVo0IA5c+YwZcoUypUrR0hICLVr1+aZZ55h6tSpdOzYkaeffpqIiAjGjh3LtGnTeOWVV876ujnd9ebXuazz89KrVy+ef/55nnnmGTp06MCGDRsYPnw4VatWJSsrK985TgoNDaV9+/a8+uqrREZGUqVKFebOncunn356TnuD77///uxTdZx8zvxdgwYNmDhxIu+//z7NmjXD5XKdtiQOHz6c6dOn0759ex5//HEaNGjA0aNHmTFjBg8++CB16tQp8G1ZYa+ne/ToQUREBLfeeivDhw/H29ubzz//nF27duX5WH311VdMmDCBatWq4e/vf9oP1l988UW6dOlCx44defjhh/H19WXkyJGsWbOG8ePHF8rRRcXGRR+OQs5JfkZmOumzzz4zLVu2NEFBQSYgIMBUr17dDBo0KMcQsR06dDCXXHJJnsvnNaqeMcakpqaaRx55xFSuXNn4+PiYcuXKmbvvvjvHcLLGnBhhpmfPnrmWP92ocae7bydHifn7cKVTpkwxjRo1Mv7+/iYuLs7861//MtOnT881Es/p7t+pI8QZc2KEpfvvv99UqlTJ+Pj4mOjoaNOzZ88cwxhnZmaa1157Lfu2g4ODTZ06dcydd95pNm3alOt2TjV58mTTsmVL4+/vb4KCgkynTp3MggUL8vX45OV0j1leoxKdvP2OHTua0NBQ4+fnZypXrmyuu+4688svv+SYb/To0aZu3brG39/f1KtXz0yYMOG0o+q9+uqreWZ76aWXTJUqVYyfn5+pW7eu+fjjj/Mc8edcRtU7OUz32Zbftm2bGTp0qImLizM+Pj4mKirKtG7dOtfIXePHjzd16tQxPj4+uUYn+uOPPwxgatasmWOZ//u//zNAjhHDTrrQ18bp7uf48eONt7e3GTJkSI5RGf9u6tSppnv37iYuLs74+vqa6Oho06NHj+yh7k/asWOHufHGG03ZsmWNj4+PqV27tnn11VdzXe+pj8dJGzduzB7ZLa9RKE83ql5+X4dr1qwxnTt3Nv7+/iYiIsLceuutZvTo0QYwf/zxR573/aQjR46YW2+91URHR5vAwEDTtm1bM2/evFwjRZ7uNZbXaFJnsmjRInPdddeZmJgY4+3tbaKjo80111xjFi5cmGvek4/LycvJv1GHDh3MiBEjTEJCQq5lzjSq3uke/1ONGTPGXHrppdnrqyZNmuS6f5988olp2LCh8fX1NWFhYaZPnz65RjY83fbgdH/bU5/jJ+//zz//bAYOHGjKlCmTPaLfqevOdevWmS5dupiQkBATHh5urr/+erNz587TjiD2923D3+VnG+jxeMyLL75oKlasaHx9fU3Dhg3NlClTTju66KlWrVpl2rRpYwIDA3ONxPfnn3+a3r17m7CwMOPr62saNWqU7+fW6a63sNb5p0pPTzcPP/ywiYuLM/7+/qZp06Zm8uTJ57QdOPXvFR8fb6699loTHh5uQkJCzJVXXmnWrFmTax1+uvtyMpefn5+58sor88x9+PBhc91115kyZcoYx3FybG/yWqft2rXLDB061MTGxhofHx9Tvnx5c8MNN+QYva8gt2UXYz29dOlS07p1axMUFGTi4uLMM888Yz755JNc6+Xt27ebrl27mpCQEANk/11Ptx6cN2+eueKKK7JfT5dddpmZMmVKjnnO9flZEjjGnGZYHxEREQvuuOMOxo8fz6FDh7IHi5Hi5fPPP2fIkCEsW7bsrIeJiZzOlClTuOqqq5g2bVqOER5FbNGheiIiYs3w4cMpX7481apV4/jx40ydOpVPPvmEJ598UqVJpJRat24dO3bs4KGHHqJx48Z0797ddiQRQMVJREQs8vHx4dVXXyU+Pp6srCxq1qzJG2+8wf333287mohYcs8997BgwQKaNm3K6NGjS/d3aqRI0aF6IiIiIiIiZ6HhyEVERERERM5CxUlEREREROQsVJxERERERETOotQNDuHxeNizZw8hISH6sqGIiIiISClmjOHYsWOUL1/+rCdeL3XFac+ePVSsWNF2DBERERERKSJ27dpFhQoVzjhPqStOISEhwIkHJzQ01HIaERERERGxJSkpiYoVK2Z3hDMpdcXp5OF5oaGhKk4iIiIiIpKvr/BocAgREREREZGzUHESERERERE5CxUnERERERGRsyh133ESERERETkbYwxZWVm43W7bUeQC+fj44OXldcHXo+IkIiIiIvI3GRkZ7N27l5SUFNtRpAA4jkOFChUIDg6+oOtRcRIRERER+S+Px8O2bdvw8vKifPny+Pr65mvENSmajDEcOHCA+Ph4ataseUF7nlScRERERET+KyMjA4/HQ8WKFQkMDLQdRwpAVFQU27dvJzMz84KKkwaHEBERERE5hcult8klRUHtMdQzQkRERERE5CxUnERERERERM5CxUlERERERPLl8ssvZ9iwYRd0Hdu3b8dxHFatWlUgmeDE4XiTJ08usOvLiwaHEBERERGRQjF48GCOHj2ao9RUrFiRvXv3EhkZaS/YeVBxEhERERGRi8bLy4vY2FjbMc6ZDtUTERERETkNYwwpGVlWLsaYc8o6Y8YM2rZtS5kyZShbtiy9evViy5YtwP8Oj5s4cSIdO3YkMDCQRo0asWjRouzlDx06xIABA6hQoQKBgYE0aNCA8ePHn/b2hg8fToMGDXJNb9asGU8//TTPPvsso0eP5vvvv8dxHBzHYc6cOXkeqrd27Vp69uxJaGgoISEhtGvXLjv7smXL6NKlC5GRkYSFhdGhQwd+//33c3psCoL2OImIiIiInEZqppt6T/9k5bbXDe9GoG/+364nJyfz4IMP0qBBA5KTk3n66ae5+uqrcxSUJ554gtdee42aNWvyxBNPMGDAADZv3oy3tzdpaWk0a9aMRx55hNDQUKZNm8bAgQOpVq0aLVu2zHV7Q4cO5bnnnmPZsmVceumlAKxevZqVK1fyzTffEB0dzfr160lKSmLUqFEAREREsGfPnhzXs3v3btq3b8/ll1/OrFmzCA0NZcGCBWRlZQFw7NgxbrnlFt555x0AXn/9dXr06MGmTZsICQk5p8f0Qqg4iYiIiIiUANdee22Onz/99FOio6NZt24dwcHBADz88MP07NkTgOeee45LLrmEzZs3U6dOHeLi4nj44Yezl//HP/7BjBkz+Oabb/IsThUqVKBbt26MGjUquziNGjWKDh06UK1aNQACAgJIT08/46F57733HmFhYXz11Vf4+PgAUKtWrezfX3HFFTnm//DDDwkPD2fu3Ln06tUr34/PhVJxEhERERE5jQAfL9YN72btts/Fli1beOqpp1i8eDEHDx7E4/EAsHPnTurVqwdAw4YNs+cvV64cAAkJCdSpUwe3281LL73EhAkT2L17N+np6aSnpxMUFHTa27z99tsZOnQob7zxBl5eXowdO5bXX3/9nHKvWrWKdu3aZZemUyUkJPD0008za9Ys9u/fj9vtJiUlhZ07d57T7VwoFScRERERkdNwHOecDpezqXfv3lSsWJGPP/6Y8uXL4/F4qF+/PhkZGdnz/L2cOI4DkF2wXn/9dd58803eeustGjRoQFBQEMOGDcuxfF636efnx6RJk/Dz8yM9PT3Xnq+zCQgIOOPvBw8ezIEDB3jrrbeoXLkyfn5+tGrV6oy5CkPxeBaIiIiIiMhpHTp0iPXr1/Phhx/Srl07AObPn39O1zFv3jz69OnDzTffDJwoVJs2baJu3bqnXcbb25tbbrmFUaNG4efnR//+/QkMDMz+va+vL263+4y327BhQ0aPHk1mZmaee53mzZvHyJEj6dGjBwC7du3i4MGD53TfCoJG1RMRERERKebCw8MpW7YsH330EZs3b2bWrFk8+OCD53QdNWrUYObMmSxcuJD169dz5513sm/fvrMud9tttzFr1iymT5/O0KFDc/yuSpUqrF69mg0bNnDw4EEyMzNzLX/fffeRlJRE//79Wb58OZs2beKLL75gw4YN2bm++OIL1q9fz5IlS7jpppvOupeqMKg4iYiIiIgUcy6Xi6+++ooVK1ZQv359HnjgAV599dVzuo6nnnqKpk2b0q1bNy6//HJiY2Pp27fvWZerWbMmrVu3pnbt2rkGkbj99tupXbs2zZs3JyoqigULFuRavmzZssyaNYvjx4/ToUMHmjVrxscff5y99+mzzz7jyJEjNGnShIEDB/LPf/6T6Ojoc7pvBcEx5zpAfDGXlJREWFgYiYmJhIaG2o4jIiIiIkVIWloa27Zto2rVqvj7+9uOUywYY6hTpw533nnnOe/luhjO9Dc9l26g7ziJiIiIiMh5SUhI4IsvvmD37t0MGTLEdpxCpeIkIiIiIiLnJSYmhsjISD766CPCw8NtxylUKk4iIiIiInJeStO3fjQ4hIiIiIiIyFmoOImIiIiIiJyFipOIiIiIiMhZqDiJiIiIiIichYqTiIiIiIjIWWhUPRERESl2MrI8JKemkXz8GCkpx0hLOU56yjGCykRRt1ZtHMexHVFEShgVJxERESl4HjdkpkJmCmSm4ElPIS31OGkpx0hPOU5G2nEyUo+TmZaMOz0Fd3oyJiMFk5ECmam4slJwZaXicqfh7U7Fx52Gr0nD16Tjb9LxJ51wJ4tTzxrjNg6/1HiULgMftXK3RaTkUnESEREpbTweyEr9X7HJSPlvwflf0SEzlcy042SmJp/4Ny0Zd3oy7pPl5r/LOFmpuLJS8XKn4u1Ow8eThq8nDV8yc9ykCwj87+WCnbIzyYNDGn5kOj6EcYyOm19m/oyqtL2yX0HcmogIoOIkIiJS9BgDKYf/VmaSTyk5qXn8LhWTkUxW+ok9OJ70ZDx/K0Qn9uCk4fXfcpMfPv+9XKgU40cqvqTiR6rxI8PlR6bjT4bLH7eXP27vANzeARjvAByfQPANxOUbiJdfEF5+Qfj4B+ETEISvfzD+gSH4BwYTEBRCYGAw3v7BuLz9CHQcMIY/37uJBgen0WjR/ayOrkTDpq0K4B6IiKg4iYiIFC1Hd5H5ZT98Dq4950Udzr3spBkfUvAjFT/SjG+O/6fiRwp+ZDp+ZHmdKDcerwDwCcD4BOL4BuLld6LkePsF/7fgBOMbEERAUDD+ASEEBAYT7O9DkJ83Zf288fdxFd73jxyHS+74jE1vdqFm6moifxjIjshfqFypSuHcnpQOxpz4AMIGn0A4h9fL5ZdfTsOGDfH39+eTTz7B19eXu+66i2effRYAx3H4+OOPmTZtGj/99BNxcXG8/vrrXHXVVYV0B0oWFScREZGi4uBm0j7rhX/KXgDSjQ+p/LfMGD/S/rvXJuUM/0/Fl3T88PgEgHcg/LfguPyC8PIL+O8enGB8A4LxCwgi2N+XID9vgv28CPLzJsjPmzL//TfYz5sgXy+8vYrPILwuX38q3jWRPe+0p7x7D2s/v4Ej/5xJeJkw29GkuMpMgRHl7dz243vAN+icFhk9ejQPPvggS5YsYdGiRQwePJg2bdrQpUsXAJ577jleeeUVXn31Vd59911uuukmduzYQURERGHcgxJFxUlERKQo2LeG9FFX4Z9+iC2ecjwZ+gL+ZSsR7O9zotT4/q3M+HkT5OdFiL83sadMDy7svTrFgH9YFP6DviNpVBcu8WxgwQcDaf7Qd/j5FMSBhyJFW8OGDXnmmWcAqFmzJv/5z3/49ddfs4vT4MGDGTBgAAAjRozg3XffZenSpVx55ZXWMhcXKk4iIiK27VpKxuhr8ctKYq2nMmNqvMnoGzvi61189vQUNRGV67Hrqs8I+H4AbdLm8vOHD9Ll3ndKdaGU8+QTeGLPj63bPkcNGzbM8XO5cuVISEjI8/dBQUGEhITk+L2cnoqTiIiITVtmkzluAL7uVJZ5avFjg7cYcW1rvFx6g3+hKjbtxsaE/6PW4kfpenAMP4+vTtcbh9mOJcWN45zz4XI2+ZyyZ9VxHDweT75/L6enj7JEREQsMeunkvXl9fi4U/nN3YC5l37I09epNBWkWlfezdpqtwLQYcPzzPvle8uJRKS4UnESERGxwLPqKzwTBuFtMpnuvpS1l3/EQ72a6FCyQnDJza/xV3hH/Jws6s+7lz9X/247kogUQypOIiIiF5l7yUe4Jt+JF26+dbfnUPcPubtTPZWmwuJyUfOusWzzq024c4yQiTexa/du26lEpJjRd5xEREQuosw5r+Ez53kARru7Ueaa17muSUXLqUo+L78gYu6cRMJ/OlDFs4eVn/UjdNhPhIUUn++uiJzNnDlzck2bPHly9v+NMbl+f/To0cILVMJoj5OIiMjFYAwZM57KLk3vea6hwoB36KPSdNEERsThddMEkvGniftPVrw/lIxMt+1YIlJMqDiJiIgUNo+H9MnD8F38DgCveG6m6S2v0alerOVgpU/Z6s041P1D3MbhipQZzPzk8Tw/hRcROZWKk4iISGFyZ5L69a34/fE5HuMw3LmTK+/4P1pVL2s7WalVqWVftjR/CoCe+z9gxjcfW04kIsWBipOIiEhhyUwj5cubCPhrIpnGi6e9hzHgrqdoWKGM7WSlXq3eD/FXpf4AXL72CebN/dlyIhEp6lScRERECkP6cZI/v4bAbT+RZnx40v8x7rz339SMCbGdTP6rzi3vsSm0FQFOBrVn3c6f69bajiRFiA7hLDkK6m+p4iQiIlLQUg6T/EkvgnYv4Ljx56mQ53jo3n9QMSLQdjL5Oy9vqt09gXifqkQ7R/H7egDx+xJspxLLfHx8AEhJSbGcRApKRkYGAF5eXhd0PRqOXEREpCAd20/yp70JOrqBIyaYEREv8MTtN1Em0Nd2MsmDV0AYEbdP5sj7HahldrD4kwGEPDCNsCB/29HEEi8vL8qUKUNCwokSHRgYqHOsFWMej4cDBw4QGBiIt/eFVR8VJxERkYJydOeJPU3Hd7DflOGN2Jd5duh1BPlpc1uUBUZXIbX/eNLG9+GyrOXMeP8uOj3wGT5eOjCntIqNPTHi5cnyJMWby+WiUqVKF1yAHVPKDuBMSkoiLCyMxMREQkNDbccREZGS4uAmUj7pRWDaPnZ5ovigyhs8Pagnft4XdmiIXDy75o+j4i93A/B9+Qe46vZntKehlHO73WRmZtqOIRfI19cXlyvvD0LOpRvoIzAREZELtXc1qZ9dRWDmETZ54hhX+x2e638F3tpjUaxUbHsjm/dvosafb9Br91tMn1SNHtcMsh1LLPLy8rrg78VIyaE1uoiIyAUwOxaR/kl3AjKP8KenCpMbf8RTAzqpNBVTNa55mk3lr8LLMbT749/MXzDXdiQRKSK0VhcRETlPZvOvZI7ui5/7OEs8dZjXehQPX90Gl0uHdxVbjkPNoZ+yLbgJIU4qVX8eytoNG22nEpEiQMVJRETkPLjXfo97bD98PWnMcTfir06juOfKpvpOTEng7UvFu75jn3cccc5BPONvJP7AIdupRMQyFScREZFzlLniS5xvBuNtMpnmbsnB3p9zS4d6tmNJAfIOLkvIrZNIckJowCa2fjSQpNR027FExCIVJxERkXOQsfB9fKbciwsP33gux+uGz7iuRTXbsaQQBJWrTca1o8nEm/aZC5j9/jCy3B7bsUTEEhUnERGR/DCGtF9fxvfnRwEYbXpQ7uaPubJBBcvBpDBF1u/EvvYvA9AnaRzfj36NUnYmFxH5LxUnERGRszGGlGmP4z9vBAAjuZ4GQ9+jba1oy8HkYqh4xW1srXMXAL13vMS0Kd9YTiQiNqg4iYiInInHzfHv7iNw+UgAXncNoeOdb9C0coTlYHIxVbvhRbZGd8HXcdNmxQPMX7zYdiQRuchUnERERE7HncmxcUMIXvMlbuMwwvterr3n/6hb7sxnl5cSyOWi6m1j2BlYj3DnOBWmD2bd5h22U4nIRaTiJCIikpfMVJI+v4GQzd+TYbz4v8B/MfQfT1MlMsh2MrHE8Q2k3J2TOOgVTRVnL6ljB7DnUKLtWCJykag4iYiInCr9GEmf9CV01yxSjS8jQp/mvvv+RWyYv+1kYplPWCz+t3xLMgE0M2v588OhHE/LtB1LRC4CFScREZG/SzlM0oc9CN2/mGMmgJciR/DQvfcSEeRrO5kUEcGVGpHS5xPcuOiW8QvTP3hEw5SLlAIqTiIiIicd20fS+10IPbyawyaYN8u/zmN3DSXE38d2Miliopr0Ym+rZwG4/uinTPzyPbuBRKTQqTiJiIgAHNnOsZGdCD22mX0mnA+q/YfHbhuAv4+X7WRSRFXodj/bqw8E4KqtzzH1xx8sJxKRwqTiJCIicmADx9/vQkhqPDs80Yyt9yGPDuyLj5c2k3JmVW56mx1l2+LvZNJyyX0sWLHSdiQRKSTaIoiISKlm9qwk5cOuBGcksNETx7Tmn/HgDV1xuRzb0aQ4cHlR6fbx7PGvTpSTSOQPg1i/Pd52KhEpBCpOIiJSarm3LSD9k54EZh3lD081Frb/gnuuaofjqDRJ/jn+oUTdMZkjrghqOzs5Mnog+44ctx1LRAqYipOIiJRKWRt+xj3mavw9ySz21GVjt7EM7tzMdiwppnwiKuFz81ek4Utr8ztLP7iL5PQs27FEpACpOImISKmTsXoijB+Ar0lntqcJh68ex/Vt6tmOJcVccLWWHO8xEoCr0qfw/YfP4PYYy6lEpKCoOImISKmSunQ03hNvxZsspnla4fT/kh5NqtmOJSVEZIvriW/2CAD9Dr3H1+M+tZxIRAqKipOIiJQayXPfJeDHf+LCw7fmCqJu+YLL61WwHUtKmAq9HmNn5Wvwcgy9Nz3BlJkzbUcSkQKg4iQiIiWfMRz76QWCZj8JwBdOb+rcPooW1aMsB5MSyXGoNPBD4sOaE+yk0Wz+nSxctdZ2KhG5QCpOIiJSshlD4uR/E7LoVQA+8hpA67vfp36FMnZzScnm7Uvcnd+S4FuR8s4hQiYNZMOu/bZTicgFUHESEZGSy+Pm6IS7CPvjIwDe9b2Nnv94k+rRIZaDSWngBIZT5rbJHHNCaOBsYc+oQSQkptiOJSLnScVJRERKpqwMDo8ZSJm/vsJtHF4PHEb/f4wgrkyA7WRSivhG14D+Y8nEm46excz94H5SMjRMuUhxpOIkIiIlT0YKhz+7jojt08gwXrxR5nFu+8eTRIX42U4mpVBI7Q4kdn4dgOtTv+abj1/Co2HKRYodFScRESlZ0pI4/FFvIvbMJdX48nb089x774OEBfjYTialWGTbwexpeB8ANya8wfivx1pOJCLnSsVJRERKjuRDHH6/GxEHl5NkAhhZ8TXuv/NuAn29bScToXzf54mP646P46bn+n8zddZvtiOJyDmwXpxGjhxJ1apV8ff3p1mzZsybNy9fyy1YsABvb28aN25cuAFFRKR4SNrDkZGdiUhcxyETwqga/2HY0EH4elvf1Imc4HJRYfAo9oY0oIyTTP25t7NozUbbqUQkn6xuTSZMmMCwYcN44oknWLlyJe3ataN79+7s3LnzjMslJiYyaNAgOnXqdJGSiohIUWYOb+Poe50IT97KXhPBtw0/5p83X4uXy7EdTSQnnwBi7/iOwz6xVHH24fPNIDbtOWg7lYjkg2OMsfbtxJYtW9K0aVPef//97Gl169alb9++vPjii6ddrn///tSsWRMvLy8mT57MqlWr8n2bSUlJhIWFkZiYSGho6IXEFxGRIsCzbx3Jn/YmJPMg2z0x/NbqEwZe2Q7HUWmSoitj7xqyPupCoEnhR1dHLh32FVGh/rZjiZQ659INrO1xysjIYMWKFXTt2jXH9K5du7Jw4cLTLjdq1Ci2bNnCM888k6/bSU9PJykpKcdFRERKhqxdK0j5+EpCMg/yl6ciy64Yy6Du7VWapMjzLVcf97WjcOOih2c2P33wb9Iy3bZjicgZWCtOBw8exO12ExMTk2N6TEwM+/bty3OZTZs28eijjzJ27Fi8vfP3Rd8XX3yRsLCw7EvFihUvOLuIiNiXvvk3Mj/rRbA7kVWe6mzu+RXXX36p7Vgi+RZS/0oOd3gBgJtTRjP207c0TLlIEWb9G7OnfipojMnzk0K3282NN97Ic889R61atfJ9/Y899hiJiYnZl127dl1wZhERsStt7XScL68lwKSwyHMJh6/9hl4t69uOJXLOojrey966QwC4ae+LjP3uO8uJROR0rI3PGhkZiZeXV669SwkJCbn2QgEcO3aM5cuXs3LlSu6778R5EDweD8YYvL29+fnnn7niiityLefn54efn054KCJSUiSvmIDflLvxxs1s0wz/m7/gilpxtmOJnLdy17/O3g+3Um7/XK5c8wBTYyrQq/1ltmOJyCms7XHy9fWlWbNmzJw5M8f0mTNn0rp161zzh4aG8ueff7Jq1arsy1133UXt2rVZtWoVLVu2vFjRRUTEkqQFnxAw5U68cTOdNkQMnUArlSYp7lxelBs6joTAmkQ5SdT65VaWrNtmO5WInMLqGQEffPBBBg4cSPPmzWnVqhUfffQRO3fu5K677gJOHGa3e/duxowZg8vlon79nIdhREdH4+/vn2u6iIiUPEd+eYPw+c8BMNHVlfq3f0ytcmXshhIpKH7BRN0xicR3O1CLeBZ8fQub7/qBGrFlbCcTkf+yWpz69evHoUOHGD58OHv37qV+/fr8+OOPVK5cGYC9e/ee9ZxOIiJSwhnDoanPUnbFWwCM876atne9R6XIILu5RAqYU6Yi/rd8Q9pn3WnDH0z85E7C7x9N2RANUy5SFFg9j5MNOo+TiEgx4vFw4NsHiVo3CoDP/AbR695Xidb5bqQEO7ZyIkHfD8WF4dOQu7jpnyPw9/GyHUukRCoW53ESERE5I3cWCWNvyy5NHwbfzTX3v67SJCVeSJNrONzqCQAGJ33I559/oGHKRYoAFScRESl6stLZP+pGord8R5Zx8X74v7j5Hy9QJtDXdjKRiyKy68Psr3EDXo5hYPxzfPn9VNuRREo9FScRESlaMlLY/9HVxMT/RLrx5qPYZxhyz2ME+Vn9Wq7IxeU4xAwYyf6yLQly0um86p9MW7DSdiqRUk3FSUREio60RPaP7EFMwgJSjB+fVX6ZO+74p77fIaWTlw8xt03gkH9lyjuHqfTTEJZu2GU7lUippeIkIiJFgjl+gIT/dCHm6EoSTSDjar/DnYNvxdtLmyopxQLCCb9tMsddoTRwbSNp/K1sTUiynUqkVNLWSERErDOJ8Rz6Tyeij2/ggAnl+yYfc+uAfrhcju1oIta5Iqvhc/NXZOJNZ5aw+OP7OZycYTuWSKmj4iQiIla5D27h6H86EZm2g92mLHNaf8Ggvr1wHJUmkZP8qrUhtcc7ANyYOZEJH/4f6Vluy6lEShcVJxERsSZzzxqOv9+Z8Mx9bPPEsrLzV1zf7XLbsUSKpNAWN3Go2TAAbkt8l0/GjKaUnY5TxCoVJxERsSJt+xLSP7mSMPdh1ptKbO39Lb3atbAdS6RIK9vrWRIq98bHcXPzjicYM+UX25FESg0VJxERueiO/zULM7oPwZ5jrDI1OXL9JDpd2sB2LJGiz3GIvvkTDpRpRJiTQofl9zJ9yRrbqURKBRUnERG5qJL++AHfr24gwKSymAaYgZNoXb+G7VgixYePP1G3fcdR33JUce0natpQlm/ZazuVSImn4iQiIhfN4cXjCJw0GF8ymeNcSplbJ9KkRkXbsUSKn+AoQm+dRKoriOauDez74na2HzhuO5VIiabiJCIiF0XCnA8oM+MevHEzw9WBynd9S52K0bZjiRRbrpi6uPqNwY2LXsxj1sf/5miKhikXKSwqTiIiUuj2/fgy0XMewYXhe5/uNPrneKrGlLEdS6TY86vdmeTOLwMwNGMsoz9+nYwsj+VUIiWTipOIiBQeY9j93ePELh0BwNcB19P2n59TrkyQ5WAiJUdo2zs43PB2AO46/DoffDlew5SLFAIVJxERKRzGED/uH8T9+R4AY0OGcuX971M2xN9yMJGSJ6LvyxyM64Sfk8mN2x7li+m/2Y4kUuKoOImISKHYOfM/VNj0BR7jMCbin1z7z9cI9fexHUukZHJ5ETloDIdDahPpJHHZ4ruZvnyD7VQiJYqKk4iIFLiMw/GUXfh/AHxb9g4G3Psc/j5ellOJlHB+wUTcNokkn0hquXYT8sOtrNiWYDuVSImh4iQiIgXLGHZ9eTdBpPInNeky5Dl8vLS5EbkowuIIGvwt6Y4/bV1/sm3Mvew8mGw7lUiJoC2ZiIgUqD2LvqL64d/INF4c7vQa4SEBtiOJlCpecU0w13yMB4frzM9M+fgpElMzbccSKfZUnEREpMC4k48QMPMxAGaED6B92w6WE4mUTv4NruJ4+2cAuDvtMz7+5D0y3RqmXORCqDiJiEiB2Tz2AcLNEbaaOC4dOALHcWxHEim1QjsO43CdG3E5hrsPjuC9cZM0TLnIBVBxEhGRApHwx8/U3jMJgC2XjSC2bJjlRCKlnOMQcf07HIppTZCTTr/ND/PFzCW2U4kUWypOIiJywUxGCmbK/QD8HNiLTt36WE4kIgB4+VB28HiOBlWlnHOYJvPv5OeVW2ynEimWVJxEROSCbfj6KWKy9rDPRFDrptdwuXSInkiREVCGMrdOItm7DA1c23Em3cmqnYdtpxIpdlScRETkghzespwamz8DYGXDp6gSV85yIhHJJaIqfjd/RSY+dHEtY/WoYcQfSbGdSqRYUXESEZHz587i2Nd3442H+b5t6NJ3sO1EInIa3lVa4b7qPwAMMt/zzUcvkJSmYcpF8kvFSUREztuG71+hcvpGEk0QUTe8jbdOdCtSpPk37c+xyx4G4L6UDxj56Scaplwkn7SFExGR85K0dxOVV78JwMLqw6hdo6blRCKSHyHdnuRo9b74OG7uSRjOf76epmHKRfJBxUlERM6dMewfexf+ZLDSqwEd+z9oO5GI5JfjUKb/hxwp24RQJ4Vr1j/I2FkrbKcSKfJUnERE5JxtnvkxNY8vJ8344PR+C39fb9uRRORc+PgTPvRbkvzjqOxKoPbcu5mzdqftVCJFmoqTiIick9TDe4le+BwAc8rfSuPGzS0nEpHzEhRJyNCJpLqCudS1kcNTn7WdSKRIU3ESEZFzsu3LfxDKcTY6VWlz8zO244jIBXCi65DZ820A2qfMZNv+o3YDiRRhKk4iIpJv2xdOpN7hmbiNw9HOrxMSFGg7kohcoNDGfUlylSHSSWLVnEm244gUWSpOIiKSL5kpiQTO/DcAsyNuoEWbTpYTiUiB8PLmQOWeAARvnKgR9kROQ8VJRETyZf3YfxFtDrCLGBoPetl2HBEpQOXbDQKgTdYS/ty2x3IakaJJxUlERM4q/s851I//GoAdrf6PyPBwu4FEpEAFVG1Jgk8cgU46m+d+ZTuOSJGk4iQiImfkyUzHfP8PXI5hflAX2nS9znYkESlojkNyrasBiNn5A1luj+VAIkWPipOIiJzR6q+eoWLWTg6ZUKrd9BaO49iOJCKFoEKHwQC09Kxm6dqNdsOIFEEqTiIicloJW1ZxyeaPAFjT8HHKl69gOZGIFBaf6JrEB9bD2/Gwb8FY23FEihwVJxERyZPxuEn8+h58HDfLfVvQru+dtiOJSCHzNLgegBr7fiQlI8tyGpGiRcVJRETytHrym9RMX8tx409kv3dxeWmTIVLSVWx7E1m4aOhsZuHSJbbjiBQp2gqKiEguR/duo/rq1wBYUeMfVKlex3IiEbkYnJAYdpVpAcDx5RpdT+TvVJxERCQnY4j/8m6CSWWdVx1a9XvEdiIRuYgCmt0IQOMjP3P4eLrlNCJFh4qTiIjksGbm59RPXkSG8cLV5118fX1sRxKRiyi2xbWk4UcVZx+L5/1sO45IkaHiJCIi2Y4fPUD5hc8AsDhuMHUatrCcSEQuOr9g4mM6AmBWf2M5jEjRoeIkIiLZNowZRgSJbHcqcOnNL9iOIyKWlG01EIAWKbPZeSDJchqRokHFSUREAPhr4RSaHZ6KxzgkdX2DgMBA25FExJLwBt1IcoUR5SSxcs4k23FEigQVJxERIS3lGCEzHwZgcdm+NGzVzXIiEbHKy4eEij0ACNwwEWOM5UAi9qk4iYgIq798jDizj/1EcMnAN2zHEZEioFz7WwBonbmIdTv2WU4jYp+Kk4hIKbd19QKa7h4LQHzrFwgLj7CcSESKgqBql3HApzxBTjob5+qcTiIqTiIipVhWZgbm+3/g7XhYHnw5zbreZDuSiBQVjkNSjb4ARG3/AbdHh+tJ6abiJCJSii3/6gWqu7eQSBCVb37XdhwRKWIqXj4YgMs8q1ixboPdMCKWqTiJiJRS8ZvX0HjzSAA2NHyUqNhKlhOJSFHjG1Ob+IA6eDseds8fZzuOiFUqTiIipZDxeDjy9b34O5ms8WvMpX3vsx1JRIood/3rAai690fSMt2W04jYo+IkIlIKLZ38HxpkrCLN+BDR730clzYHIpK3iu1uxo2Lxs4mFi1bZjuOiDXaUoqIlDIH9u6kzuqXAPij5r2Ur1bPciIRKcpcobHsDLsUgKRlOlxPSi8VJxGRUsQYw/Yv/0EYyWzxqk7z/k/ajiQixYB/0wEANDj8M0eT0y2nEbFDxUlEpBRZ8fM4Lk2eQ5Zx4er7Ll7ePrYjiUgxUO6y60jDl2rOXhbP/9V2HBErVJxEREqJo0cOUWHRUwCsjLuRqg3aWE4kIsWGXwi7ojsC4P5jguUwInaoOImIlBJrv3iIWA6xx4ml4cCXbMcRkWIm4rITJ8hukTyb3YePWU4jcvGpOImIlAJ/LJhBq0OTATje9TX8AkLsBhKRYqdsox4kuUKJchL5fc5k23FELjoVJxGREi4lJZnQXx7C5RhWlu1JrVa9bUcSkeLIy4f9FboDELD+O8thRC4+FScRkRJu2RdPUdXEc5gwag1823YcESnGyrW7BYDLMhbx1879ltOIXFwqTiIiJdj6P5bQas/nAOxtM5ygMlF2A4lIsRZcozUHvMsR7KTx128aJEJKFxUnEZESKiMzC88P/8TXcbMmuDWXdL7FdiQRKe4ch8QafQGI2joZj8fYzSNyEak4iYiUUPPHv8Ql7r84TgAVbn4fHMd2JBEpASp2OPEhTAv3Kn7/a7PlNCIXj4qTiEgJtHXLBlpseffE/xs9TJnYKnYDiUiJ4VeuLvH+tfBx3OyeP852HJGLRsVJRKSEcbs9HJpwH8FOGpv9LqFBnwdsRxKREibzkusBqLxnKulZbstpRC4OFScRkRLmt0kfcWnGUjKMN2H9RuK4vGxHEpESpnL7gbhx0ZiNLF7+u+04IheFipOISAmye3c8Df/8PwD+qnk7UdUa2w0kIiWSK6wcO0ObAZC4dKzlNCIXh4qTiEgJYYxhy9gHKOskscu7EvVveNZ2JBEpwXya9geg/qGfSErNsJxGpPCpOImIlBDzfvqW9ik/4zEOrj7v4vL1tx1JREqwuMuuJx1fqjl7WLJglu04IoVOxUlEpAQ4cPgI1RY/DsCaCjcQ1+Byu4FEpMRz/MPYGdUBgMyVOhmulHwqTiIiJcAfXzxCBRI44Iqk7k2v2o4jIqVEmZY3A9D8+Cz2HUm2nEakcKk4iYgUc4vm/0LHw18DkNL5FXwCwywnEpHSIqpxD445IUQ7R1kxd7LtOCKFSsVJRKQYS0pOIeLXh/FyDOvKdqFy62ttRxKR0sTbl70VrgTAd913lsOIFC4VJxGRYmzBF8OpbbaRSAjVBv7HdhwRKYVi294CQKv0BWyOT7CcRqTwqDiJiBRTK1euoOPeTwA42OZp/MvEWk4kIqVRaK22HPCOJdhJY91cDRIhJZeKk4hIMZSWkYWZcj/+TiabgptTvfPttiOJSGnlOByt1geAiC3fY4yxHEikcKg4iYgUQ7+Of4Omnj9Jw5fYmz8Ax7EdSURKsYodThyu19L9O39s2GI5jUjhUHESESlm1m/cRNutbwGws+EDhMTWtBtIREo9/7hLiPeviY/jZuf8cbbjiBQKFScRkWIk0+3hwDfDCHOS2elXk1p9/m07kogIABn1TozqWTF+KhlZHstpRAqeipOISDEyc+JntM+cTxYuQm74ALy8bUcSEQGgcodb8ODQhA0s/f1323FECpyKk4hIMbEtfg/N1rwAwJYaQwiv3txyIhGR//EKK8/2kGYAHFmiw/Wk5FFxEhEpBjwew8axDxPjHGG/d3lq3fCC7UgiIrn4NOkHQN2DMzielmk5jUjBUnESESkGfv5pMt1SpwHguuodHN9Ay4lERHKr0OoGMvChhrObxQvn2I4jUqBUnEREirg9B49Sa/ETAGwsfzVRDbtYTiQikjcnoAzbIzsAkPH7eMtpRAqWipOISBFmjGHZF09SzdnNEVc41W9603YkEZEzCmtxIwDNjv1KQmKy5TQiBUfFSUSkCJs9by7dj574knVa5xfxCgq3nEhE5MximvbmmBNMjHOUFXOn2I4jUmBUnEREiqjDx1KJmvUwvo6bLWU7UK5Vf9uRRETOztuXPXFXAuCz9hvLYUQKjoqTiEgRNfuLETRgE8kEUvGmkeA4tiOJiORLTNtBALRMW8C2vQcspxEpGCpOIiJF0MLfV3Hl/g8BONz6cXwjKlhOJCKSf2VqteOgVzQhTipr5mivk5QMKk4iIkXM8bRMmPoAQU46u4IbUrHzvbYjiYicG5eLQ9X6ABC+eRLGGMuBRC6cipOISBEzffx/aO35nQy8ibzxA3BpVS0ixU/FDoMBaJG1gj83b7eaRaQgaGssIlKErPprM1dsfx2AvQ3vI6D8JZYTiYicn8AK9Yn3q46v42bHb+NsxxG5YCpOIiJFRFqmm4RvH6asc4x9flWpfNUTtiOJiFyQtLrXAVBh1w9kuj2W04hcGBUnEZEiYsrEL+maNRsPDsHXvw/evrYjiYhckCodBuHBoQl/seyPP2zHEbkgKk4iIkXAXzv30XLdCwDsrH4zwTVaWU4kInLhvMMrsD24CQCHF421nEbkwqg4iYhYluX2sH78o1RyEjjkHU2VG160HUlEpMB4NT5x8u46CdNJSc+0nEbk/Kk4iYhY9sP0aVyVMhkAr95vgV+I1TwiIgWpUpt+pONDDSeeJYvm2o4jct5UnERELNqRcJS6y57AyzHsKN+DMo162o4kIlKgnIAy7IhoB0Da719ZTiNy/lScREQsMcaw8IvnqOvs4JgrlEo3vmM7kohIoQhpeRMATRJ/4VBSiuU0IudHxUlExJJpc+ZzTdIXAGRc8TxOcJTlRCIihaNcs94cd4KIdY6w4rcptuOInBcVJxERC/YnphAz99/4OZnEh19G2Ta32I4kIlJ4vP2IL98NANeaby2HETk/Kk4iIheZMYafvniVS1lHGn7E3vQBOI7tWCIihSqm7SAAWqTOY8f+Q5bTiJw7FScRkYts1rLV9D3wAQCJrf6Nd2RVy4lERApfeO0OHPSKItRJZe3sr23HETlnKk4iIhfR0ZQMmP5vQp0U9gXVJabzMNuRREQuDpeLg1WvAiB08ySMMZYDiZwbFScRkYto8viP6GQW48ZFxI0fgpe37UgiIhdNxfYnvs95aeYK1m/ZaTmNyLlRcRIRuUgWrt3KlTtfAyChwV34xjWynEhE5OIKqtSIeN9q+DlZbJ031nYckXOi4iQichGkZGSxf+KjxDpHOOhXkXJXPW07koiIFSl1rgWg/M4puD06XE+KDxUnEZGL4OvvvuZq908ABF73HvgEWE4kImJHlQ6D8ODQ1Kzj99V/2o4jkm8qTiIihWzV1r20Wz8cgD3V+xFYs4PlRCIi9viWrcT2oMYAJCz8wm4YkXOg4iQiUogysjysnfAM1V17SfKKoPx1r9iOJCJindPoBgBq7p9OWqbbchqR/FFxEhEpRF9P+4kb0r4FwOn5GgSUsRtIRKQIqNx2ABl4U8vZxZLFv9mOI5IvKk4iIoVk496jNFjxBD6Om73lOhHS5BrbkUREigRXYDjbItoCkLp8nOU0Ivmj4iQiUgjcHsO8sf9HI9cWUpxAYvu/C45jO5aISJERcumNADQ6+gtHk9MspxE5OxUnEZFCMHHWAvofGw1A5hXP4oTF2Q0kIlLElG/eh+NOEOWcwyybO9V2HJGzUnESESlguw4lEzvvCYKcdPaHNyWsze22I4mIFD0+/uyK7QqA68+vLYcROTsVJxGRAmSMYcrYd2jnrCITH6IGfAgurWpFRPIS02YgAJemzCP+wGHLaUTOTFtzEZECNHXRGvodeg+AYy0fwBVdy3IiEZGiK6JeRw65Igl1Uvhz9re244ickYqTiEgBOXAsHX5+nLLOMQ4G1SCiy79sRxIRKdpcLhKq9AYgZON3GGMsBxI5PRUnEZECMuGrUfTmNzw4lOn3AXj72o4kIlLkVegwGIBLM5ezYfsuu2FEzkDFSUSkAMz6Yyt9418F4HD9oXhXutRyIhGR4iGkcmN2+1bFz8li61yd00mKLhUnEZELlJSWyb7vn6KCc5CjvrFE9h5uO5KISLFyvNaJE4TH7vgBj0eH60nRpOIkInKBvvh2Iv3c0wAIvPpt8Au2nEhEpHipfPktADQ1a1m5Zo3lNCJ5U3ESEbkASzbv44qNz+PlGA5U7YNv3SttRxIRKXb8IyuzNagRAPsXfGk5jUjeVJxERM5TWqab1ROGU9e1i2SvMKKue8N2JBGRYss06AdAjf0/kpbptpxGJDcVJxGR8zR6yi8MyjhxtntX9xchKNJyIhGR4qtquxvJwJta7GT50nm244jkouIkInIe1sQfodGqZ/BzMjkY246AZjfajiQiUqy5gsLZFt4GgOPLxltOI5KbipOIyDnKdHv4ddxrXOZaT7rjT2S/98BxbMcSESn2Av/7IVTDIzNJTEm3nEYkJxUnEZFz9OXMJQxJ/gyAzA5PQHhly4lEREqGCi37cJwgyjuHWPHbVNtxRHJQcRIROQdbDhyn/MKnCXVSOFymPsHt77UdSUSkxHB8AtgZ2xkAs/oby2lEclJxEhHJJ4/HMGns+3RzLSULL8L7fwAuL9uxRERKlKjWNwPQPHkuew8dtRtG5G9UnERE8unrBWsYeOQ/ACRfeh9ObAPLiURESp6o+p045IokzElh9WztdZKiQ8VJRCQf9iam4vrlWWKcoyQGVias6+O2I4mIlEwuL/ZX7glA0IaJlsOI/I+Kk4jIWRhjGD1+LDc4vwAQfN174ONvOZWISMlVsf0tAFyasYzNO+MtpxE5QcVJROQspq/azg17XgXgaL2b8KrWznIiEZGSLaRKU3b7VMHPyWTTnHG244gAKk4iImeU6faw78eXqebax3GfSMr0HmE7kohIyec4JNW6GoDo7d/j8RjLgURUnEREzmjKojVcnzEZAO8eL0FAGat5RERKi6odBwPQxL2W1evX2Q0jgoqTiMhppWe5SZ79BiFOKgeDa+Pf6FrbkURESg3/yCpsDWyIyzHsnf+l7TgiKk4iIqfzw/yVXJc1DYDQHs+CS6tMEZGLyVP/egCq7Z1GRpbHchop7fQuQEQkD2mZbrJ+e4MAJ4MDYQ3wrdvddiQRkVKnaoebycSb2uxg+bIFtuNIKafiJCKSh0lzlnCN+ycAwno+B45jOZGISOnjFRTB1jKtATi2VKPriV0qTiIip0hOz8Jn4Rv4OVkkRDTHt+YVtiOJiJRa/k37A1D/8M8cS023nEZKMxUnEZFTTJo1nz6eWQBE9NbeJhERmypddjXJBBDnHGTFvBm240gppuIkIvI3SWmZhCx5Ax/Hzf6oNnhXbWs7kohIqeb4BrI9pjMAnj++spxGSjMVJxGRv5n082x6md8AiLzqectpREQEILLVQACaHp9LwpEky2mktFJxEhH5r6MpGUSveBMvx7CvXCe8KjazHUlERICYhp057IqgjJPMqtnf2I4jpZSKk4jIf02a/hPdnYUARF/1nOU0IiKSzeXF3kq9AAj86zvLYaS0UnESEQEOHk+n0uq3ANhXoTuucg3sBhIRkRzi2t0CwKXpS9kWv8dyGimNrBenkSNHUrVqVfz9/WnWrBnz5s077bzz58+nTZs2lC1bloCAAOrUqcObb755EdOKSEn1/bQpdHKW48ZFzFXP2o4jIiKnKFOtGbt9KuHnZLJxts7pJBef1eI0YcIEhg0bxhNPPMHKlStp164d3bt3Z+fOnXnOHxQUxH333cdvv/3G+vXrefLJJ3nyySf56KOPLnJyESlJ9ielUWvtOwAcqNIHJ7qO5UQiIpKL45BY42oAIrdNxhhjOZCUNo6x+Kxr2bIlTZs25f3338+eVrduXfr27cuLL76Yr+u45pprCAoK4osvvsjX/ElJSYSFhZGYmEhoaOh55RaRkuXTsV9y66Z7ycILr38sxylbzXYkERHJQ2rCVgJGNsFjHNb2X0SDunVtR5Ji7ly6gbU9ThkZGaxYsYKuXbvmmN61a1cWLlyYr+tYuXIlCxcupEOHDqedJz09naSkpBwXEZGTdh9JocGG/wBwsMb1Kk0iIkVYQHQ1tgY0wOUY9sz70nYcKWWsFaeDBw/idruJiYnJMT0mJoZ9+/adcdkKFSrg5+dH8+bNuffee7nttttOO++LL75IWFhY9qVixYoFkl9ESobpP3xFC9d6MvEhtvdTtuOIiMhZZF1yLQCV9kwj0+2xnEZKE+uDQziOk+NnY0yuaaeaN28ey5cv54MPPuCtt95i/Pjxp533scceIzExMfuya9euAsktIsXfjoPHabblPQAO170JwipYTiQiImdTrcNAsvCiLtv4ffki23GkFPG2dcORkZF4eXnl2ruUkJCQay/UqapWrQpAgwYN2L9/P88++ywDBgzIc14/Pz/8/PwKJrSIlCgzvx/Dba7NpDt+xPR43HYcERHJB++QSDaEtaJ24nwSl46Dlm1sR5JSwtoeJ19fX5o1a8bMmTNzTJ85cyatW7fO9/UYY0hPTy/oeCJSwm3en0SrHR8AkFh/CISc+QMbEREpOnyb9AOg3sGfSE7LsJxGSgurh+o9+OCDfPLJJ3z22WesX7+eBx54gJ07d3LXXXcBJw6zGzRoUPb87733HlOmTGHTpk1s2rSJUaNG8dprr3HzzTfbugsiUkzNmfwpl7h2kOoEEt39EdtxRETkHFRpfS3JBFDBOcCK+TNsx5FSwtqhegD9+vXj0KFDDB8+nL1791K/fn1+/PFHKleuDMDevXtznNPJ4/Hw2GOPsW3bNry9valevTovvfQSd955p627ICLF0PrdR+iw+2NwwfEmdxAQGGE7koiInAPHN4jt0VdwScI0slZNgM5X2Y4kpYDV8zjZoPM4icgn773EbQdeJNkVQtC/1kBAGduRRETkHO39/UfK/TCAIyYY94N/ERkWYjuSFEPF4jxOIiI2/LnzIJ32fwZA2qX3qjSJiBRT5Rp347ATTrhznD9mfWs7jpQCKk4iUqosm/weVV37OeZVhrJX/MN2HBEROV8uL/ZU7AmA31/fWQ4jpYGKk4iUGr9v3UvXQ2MAyGw1DPyC7QYSEZELUr79iUHEmqctZseefWeZW+TCqDiJSKnxx/fvUsE5SKJ3JBEd7rIdR0RELlBE9Rbs9q6Iv5PJhtnjbMeREk7FSURKhSUb4ul5dCwAnnYPg0+A5UQiInLBHIej1fsCELF1MqVszDO5yFScRKTEM8bw19Q3iXaOcsQnlvA2t9qOJCIiBaRKx8EANMlazfqNG+2GkRJNxUlESrwF67bTK2kCAK6Oj4K3r+VEIiJSUIJia7DF/xK8HEP8vC9tx5ESTMVJREo0Ywzbp71OWecYh/wqEtZyoO1IIiJSwDLrXQdAxfipZLk9ltNISaXiJCIl2pw/NnFV8olhan06PwFe3pYTiYhIQavecSCZeFGXraz8fYntOFJCqTiJSInl8Rj2zniNUCeFAwHVCW3Wz3YkEREpBD4hUWwJbQnA0SVjLaeRkkrFSURKrF9XrOOq1O8BCOj6FLi0yhMRKal8GvcHoM6Bn0hNz7KcRkoivYsQkRLJ7TEcmfkqwU4a+4PqENy4r+1IIiJSiKq1vY4U/KnoJLBiwU+240gJpOIkIiXSz0v+4Kr0qQCE9HgGHMdyIhERKUyObxBbo64AIHPleMtppCRScRKREifL7SH115fxdzLZG9qQwHrdbUcSEZGLILzlTQA0SprNkaRky2mkpFFxEpESZ8b8ZfTKPHGYRpmez2lvk4hIKRHX5EqOOGWIcI6zas53tuNICaPiJCIlSkaWB89vr+DruNlT5lICal9hO5KIiFwsXt7Ex/UAwHftN5bDSEmj4iQiJcqPcxfQI2sWAGV7P2c5jYiIXGzl2t0CQNO0xcTvS7CcRkoSFScRKTHSMt34LXgFb8dDfGRb/Kq3sR1JREQusshaLdnjXYEAJ4O/Zo+zHUdKEBUnESkxpv06m27ueQBE9xluOY2IiFjhOByu1geAMpsnY4yxHEhKChUnESkRUjKyCFvyKi7HsCumE74Vm9mOJCIillTuOBiAJlmr2Lhls90wUmKoOIlIiTDtp5/obBbjwSG2j77bJCJSmoWUq8VWv3p4OYadv31pO46UECpOIlLsHUvLJHrF6wDEx3XHp3wDy4lERMS29HrXAhC3awpujw7Xkwun4iQixd6PP06hAytw4yKur77bJCIiUO3ygWThop7Zwh8rl9mOIyWAipOIFGtHUzKo9McbAOyu1BevqJqWE4mISFHgFxbD5pAWABxerMP15MKpOIlIsTZ96re0cv4kE28q9H3GdhwRESlCvBr1A6B2wgzSMrIsp5HiTsVJRIqtQ8fSqLn2bQD2Vr8eV0QVu4FERKRIqd72BlLwp6Kzn5ULf7YdR4o5FScRKbZ+nvoVzZ2/yMCHilc9ZTuOiIgUMS7/YLaWvRyAtN+/shtGij0VJxEplhISU7nkr3cA2F/7ZpywOMuJRESkKAptcSMAjRJnkXgsxXIaKc5UnESkWPr1+9E0dLaQhh8Vej9uO46IiBRRlZr35IhThgjnGKvmTrQdR4oxFScRKXZ2H0mmyZb3ADhwyVCc4GjLiUREpMjy8mZX+e4AeK/9xnIYKc5UnESk2Jk76WPqODtJcQKp0PPftuOIiEgRF9tuIABNUxayN+GA5TRSXKk4iUixsvNAEi13fAjAkUZ34gRGWE4kIiJFXXTt1uzxiiPAyWDd7HG240gxpeIkIsXK/EnvU93Zw3FXCHFXPmg7joiIFAeOw8GqfQAI2zTZbhYptlScRKTY2LLvCG13fwxAUrP7wD/UciIRESkuqnS8BYAmmSvZvHWr5TRSHKk4iUixsXTSu1RyDpDoFU75Lv+wHUdERIqR0Lg6bPWri5dj2D53jO04UgypOIlIsbAhPoEO+0YBkNLyfvANspxIRESKm9Q61wBQfucUPB5jOY0UNypOIlIsrJz0FuWdwxzxjqJcx7ttxxERkWKoesdBZBkX9cxm1qxeYTuOFDMqTiJS5K3ZtpdOB78EIKP1Q+DjbzmRiIgUR/5lYtkccikABxZ+aTmNFDcqTiJS5K37/jWinEQO+pQjpv2ttuOIiEgx5mrUD4CaCT+Skem2nEaKExUnESnSVm7aSZcjXwFg2j8C3r6WE4mISHFWvd0NpOJHJfazctEvtuNIMaLiJCJF2pYprxLuHCfBtxJRrQfajiMiIsWcl38IWyI6AJC6YrzlNFKcqDiJSJG1bP0WuiZ+A4DrisfBy9tyIhERKQmCW9wEQIOjv3IsOcVyGikuVJxEpEgyxhA/9WVCnVT2+VcnskU/25FERKSEqHxpT444YZR1klg1d7LtOFJMqDiJSJG0+M8NdDs+GQC/rk+BS6srEREpGI6XD7vKdQPAteYby2mkuNA7EREpcowxJEx/kUAnnd2BdQlv0td2JBERKWFi2t4CQJPkBSQcPGQ5jRQHKk4iUuTMX7GaK1OmARDU/RlwHMuJRESkpImp24a9XuUIdNJZO1uDRMjZqTiJSJHi8RgSZ76En5PJrpBGlKl/pe1IIiJSEjkOCVWuAiBk40TLYaQ4UHESkSLlt6XL6Zr2EwBlej6nvU0iIlJoKl8+BIDGGSvZtn273TBS5Kk4iUiR4fYY0n59EV/HzY6wFoTU6Wg7koiIlGBlKtZlq29tvB0P2+aOsR1HijgVJxEpMmYvXEiXjFkAlO093HIaEREpDZJrXwNAzI4pGGMsp5GiTMVJRIqELLcH5ryEl2PYFtGO4BqtbEcSEZFSoEbHW8gyLi7xbGTtnyttx5EiTMVJRIqEX+bO4YrMeQDE9NHeJhERuTgCIsqxObg5AAkLv7ScRooyFScRsS4jy4P//JdxOYatUZ0JrNzUdiQRESlFTMPrAai+70cys9yW00hRpeIkItb98utPXO5ZjAeH8n21t0lERC6umu37k4ofldnLqiW/2o4jRZSKk4hYlZbpJmzxKwBsK9cD/7hLLCcSEZHSxjsglM3h7QFIXvaV5TRSVKk4iYhVv/z0A23M72ThokLf52zHERGRUiqo+QAA6h/5heTUNMtppChScRIRa1Iysohe8ToA2ytcjV9MTcuJRESktKrasjdHCSXSSWTV3Em240gRpOIkItb88uM3tDB/koE3la95xnYcEREpxRxvX7bHdjvxw+pv7IaRIknFSUSsOJ6WSaVVbwKws8oN+ERUtpxIRERKu+g2AwFonDyfA4cPW04jRY2Kk4hYMWvKWBqzgXR8qdL3KdtxREREKF+/PXtdsQQ56aybNd52HCliVJxE5KJLTM6gxtq3ANhV4ya8y5S3G0hERATAcdhf+SoAgjbqe06Sk4qTiFx0c374jHpsIxV/qvV5wnYcERGRbJUuHwxA4/QV7Ni1024YKVJUnETkojp8LJV6f70LwJ46g3GFRFlOJCIi8j8RlS9hm29NvB0PW2ePsR1HihAVJxG5qOZN/oiaTjzHnSCqXfWo7TgiIiK5JNW8BoDo7T9gjLGcRooKFScRuWgSEo/TaPPIE/+vfztOYLjlRCIiIrnVuOIW3MbhEs8G/lq32nYcKSJUnETkolk48T2qOPtIckKp2vMh23FERETyFFQ2jk1BzQDYt+ALy2mkqFBxEpGLYs+hRJpv/wiAQ43vwfEPtZxIRETk9DwNrgeg2p5pZGW5LaeRokDFSUQuimUT36aCc5AjrnCqdP+n7TgiIiJnVLPDAFLxpTJ7+GPZXNtxpAhQcRKRQhefcIiW8Z8BkNj8fhzfIMuJREREzswnMIzNZdoBcHzZOMtppChQcRKRQrfiuzeIdY5w0CuKKl3vsR1HREQkXwKaDwDgkkM/k5qWbjmN2KbiJCKFatvu/bTZd+I8GCmXPQTefpYTiYiI5E/1y/pwlBAinURW/faD7ThimYqTiBSqNZNeJdJJYr93eSpdcZvtOCIiIvnmePuyPaYrAJ4/JlhOI7apOIlIodm0I572B8YCkNH23+DlYzmRiIjIuYlsPRCARsfncfjoUbthxCoVJxEpNBsnv0yYk8Jen8pUbD/IdhwREZFzVqHh5exzxRDspLF21njbccQiFScRKRTrt2yn/eFvADAdHwOXl+VEIiIi58Fx2FupNwABf020HEZsUnESkUKx7fsRhDipxPvVoPxl/WzHEREROW8VO9wCQKP0FezevctyGrFFxUlECtzqvzbQMXESAN6dnwSXVjUiIlJ8RVZtyDafGvg4bjbN/sJ2HLFE72ZEpMDtmfoiAU4GOwLqEdu8r+04IiIiFyyx5tUARG77HmOM5TRig4qTiBSolX/+ScdjUwAI6PYMOI7lRCIiIheuesdBuI1DffdfbPzrT9txxAIVJxEpMMYYDk4fgZ+TxdagxkQ36mY7koiISIEIiarExsCmAOydr8P1SiMVJxEpMMtXruTy5J8ACO3xrPY2iYhIieKufx0AlXdPw+32WE4jF5uKk4gUCGMMx376P3wcN5tDWxJ5SUfbkURERApUrctvJA0fqrKbP5fPtR1HLjIVJxEpEEuWLaJD2q8ARPR+znIaERGRgucbVIaNYe0ASFo6znIaudhUnETkghljSP9lBF6OYWOZ9kTUbGU7koiISKHwa9ofgLoHfyYtPcNyGrmYVJxE5IItWDCXDhnzAIjpM9xyGhERkcJTs3VfEgkmyjnK6nk/2I4jF5GKk4hcELfH4JozAoANZTsTVrWJ5UQiIiKFx+Xjx5borgBkrZpgOY1cTCpOInJB5s/9idZZS3DjUL6v9jaJiEjJF9nqZgAaHptHYmKi5TRysag4ich5y3J7CJj/EgAbo3sQUvESy4lEREQKX6VGHdnvRBPspLJmtvY6lRYqTiJy3ub98gMt3CvJxItK12gkPRERKSVcLnZX6gWA3/pvLYeRi0XFSUTOS0amm/AlLwOwqXwfgmJrWk4kIiJy8ZRvc+JwvQZpv5OWlmY5jVwMKk4icl7m/fwtjT3ryMCbatc8azuOiIjIRRVTozHHCMTPyWTb+uW248hFoOIkIucsLSOLmOWvArC54vX4R1a2nEhEROTiclxe7PKvBcCRzUstp5GLQcVJRM7ZvB/HUt9sIg1fql3ztO04IiIiViSXbXDiP3tWWc0hF4eKk4ick9T0TCqtehOALVVvwj+8vOVEIiIidvhVagZA2aR1lpPIxaDiJCLnZP6Uz6jNNpIJoObVT9qOIyIiYk35uq0AqJK1jdTUVMtppLCpOIlIvh1PTaf6mrcB2F7zFnxDIy0nEhERsSeyYm2SCMLPyWLbumW240ghU3ESkXxbOPkDqrGbJIKp3fdR23FERETschx2+dcGNEBEaaDiJCL5kng8hTob3gMgvt5teAeFW04kIiJiX0pkfQBce1fZDSKFTsVJRPJlyaR3qcR+jjph1L7qYdtxREREigT/Ss0BKJu03nISKWwqTiJyVocTj9Fg84cA7G1wN17+IZYTiYiIFA3l6l4GQBX3NpKTky2nkcKk4iQiZ7Vs4luUcw5x0Imgds/7bccREREpMiIr1CKRYHwdN9vXa4CIkkzFSUTOKOHwYZps/wSAg03/icsv0HIiERGRIsRx2BVQB4Cjm1WcSjIVJxE5o1UTXyfaOUqCK5ra3e+xHUdERKTISS3bAADXvpWWk0hhUnESkdPad+AAzXeNBuBoiwdxvP0sJxIRESl6/Cs3AyBSA0SUaCpOInJaf373MhHOMfZ4xVGzy22244iIiBRJ5eu1AqCKewfHk49bTiOFRcVJRPIUv2cPLfeOBSCl9b9wvHwsJxIRESmaypavzhFC8XHcbF+rE+GWVCpOIpKnvya+SKiTwi7vKtToeIvtOCIiIkWX47A7oDYAiVtUnEoqFScRyWXHrp1cduBrADLaPwourSpERETOJDXyxAARXvv+sJxECoveDYlILlsmvUCwk8Z235pUb9ffdhwREZEiL6BKcwAij2mAiJJKxUlEctiydTOtD0088UPHJ8Bx7AYSEREpBuLq/m+AiGPHkiynkcKg4iQiOez6/nn8nUy2+NWjymV9bccREREpFsLLVeUwYXg7Hnas0/ecSiIVJxHJtnHDWlofnQKAb9dntLdJREQkvxyH3YF1AEjcssxyGCkMKk4ikm3flOfxddxsDGxCxWZX2o4jIiJSrKRlDxCxym4QKRQqTiICwLq1q2h97CcAgq58xnIaERGR4ufkABFRxzVAREmk4iQiAByeNhxvx8P64JbENexoO46IiEixU6FeawCquHeSdCzRchopaCpOIsLqlUtonTwLgPBez1lOIyIiUjyVianEQcLxcgw71iy2HUcKmIqTSClnjCHlp+G4HMPasPbE1mllO5KIiEjx5DjsDaoNQOLW5ZbDSEHzPtcFEhMTmTRpEvPmzWP79u2kpKQQFRVFkyZN6NatG61bty6MnCJSSP5Y9huXpc3HYxyiemtvk4iIyIVIi2wIyYvx3r/KdhQpYPne47R3715uv/12ypUrx/Dhw0lOTqZx48Z06tSJChUqMHv2bLp06UK9evWYMGFCYWYWkQJijCHzlxcAWBvRmegaTS0nEhERKd4Cq1wKQPSxvywnkYKW7z1OjRo1YtCgQSxdupT69evnOU9qaiqTJ0/mjTfeYNeuXTz88MMFFlRECt7vC37m0oyluI1D+b7a2yQiInKhKlxyGcyFyp5dJB49QliZcNuRpIDkuzitXbuWqKioM84TEBDAgAEDGDBgAAcOHLjgcCJSeIwxuOaOAGBNVE8aVb7EciIREZHiLyy6EgecCKI4zI51S2jYWudFLCnyfaje2UrTScaYc5pfROxYNvsHmmSuItN4UenqZ23HERERKTH2BtYBIGnrMstJpCCd16h6AwcO5Pjx47mmb9++nfbt219wKBEpXB63h8AFLwGwNrYv4XE1LScSEREpOdKjGgLgu/8Py0mkIJ1XcVq3bh0NGjRgwYIF2dNGjx5No0aNiImJKbBwIlI4lvz6LfXd60jHh2rXPGs7joiISIkSVLU5ANHH11tOIgXpvIrTkiVL6NevH1dccQWPP/44119/Pffddx9vvvkm3377bUFnFJEClJXlJnzxywCsi7ue0JhKlhOJiIiULBUuOXF6nkqe3Rw5cthyGiko53weJwBvb29eeukl/Pz8eP755/H29mbu3Lm0aqUTZ4oUdUt+Gksbz2ZS8aPmNU/ZjiMiIlLihEbGkeCUJZpD7Fy7iPC2PW1HkgJwXnucMjMzeeihh3j55Zd57LHHaNWqFVdffTU//vhjQecTkQKUmZVFzPLXAVhf6UaCy5a3nEhERKRk2htYF4Bj25ZbTiIF5bz2ODVv3pyUlBTmzJnDZZddhjGGV155hWuuuYahQ4cycuTIgs4pIgVgybTPaGu2c5xA6lz7hO04IiIiJVZGdEPYNl8DRJQg57XHqXnz5qxatYrLLrsMAMdxeOSRR1i8eDG//fZbgQYUkYKRmZVF3Kp3ANhY7RYCw3TKABERkcJycoCImOS/LCeRgnJexenTTz8lKCgo1/TGjRuzYsWKCw4lIgVvxcyvqGp2cZxA6l39iO04IiIiJVrF+icGiKhsdnP48CHLaaQg5Ls4JScn52s+Pz+/c5pfRAqfMYbgFe8BsKHCdfiHhFtOJCIiUrKFRJRjv3Pi6I6daxdaTiMFId/FqUaNGowYMYI9e/acdh5jDDNnzqR79+688847BRJQRC7cH4t+pn7WOjKMF9V7/8t2HBERkVJhb9CJASKOa4CIEiHfg0PMmTOHJ598kueee47GjRvTvHlzypcvj7+/P0eOHGHdunUsWrQIHx8fHnvsMe64447CzC0i5yBr3lsArInsTlOdt0lEROSiyIxpCMd/wzdBA0SUBPkuTrVr1+abb74hPj6er7/+mnnz5rFw4UJSU1OJjIykSZMmfPzxx/To0QOX67y+OiUihWDj2t9pmrIIHCjXXXubRERELpbgqpfCFojVABElgmOMMbZDXExJSUmEhYWRmJhIaGio7TgihW7hmzfROnEqfwa1psG/ptuOIyIiUmocO7KfkLdrAXDovk2UjYy2nEhOdS7d4LzO4/Tggw/mOd1xHPz9/alRowZ9+vQhIiLifK5eRArInvjtNDs6AxwIuiLv162IiIgUjpDwGPY60ZQzCexcu4iyHfrYjiQX4LyK08qVK/n9999xu93Url0bYwybNm3Cy8uLOnXqMHLkSB566CHmz59PvXr1CjqziOTTlqmvU97JYqNvPWo17Ww7joiISKmzP7gu5Y4lkLx9Oag4FWvn9WWkPn360LlzZ/bs2cOKFSv4/fff2b17N126dGHAgAHs3r2b9u3b88ADDxR0XhHJp6NHDtNo77cAZLb8BziO5UQiIiKlT2ZMIwD8ElZbTiIX6ryK06uvvsrzzz+f4zjA0NBQnn32WV555RUCAwN5+umndTJcEYv+nPIOoU4K8a446nXsZzuOiIhIqRRS9VJAA0SUBOdVnBITE0lISMg1/cCBAyQlJQFQpkwZMjIyLiydiJyXtLQ0am4dA8CBBnfguLwsJxIRESmdKl7S6sS/7OPAgX2W08iFOO9D9YYOHcqkSZOIj49n9+7dTJo0iVtvvZW+ffsCsHTpUmrVqlWQWUUkn37/8VNiOcQhylC/h86pJiIiYktQmSj2OLEAxK9daDmNXIjzKk4ffvghnTp1on///lSuXJlKlSrRv39/OnXqxAcffABAnTp1+OSTTwo0rIicndvtIXbNhwBsrT4QH79Ay4lERERKt/3BdQFI3q6vsRRn5zWqXnBwMB9//DFvvvkmW7duxRhD9erVCQ4Ozp6ncePGBZVRRM7Bylnf0Nyzg2T8qdd7mO04IiIipV5WbEM4Nht/DRBRrJ1XcTopODiYhg0bFlQWEblAxhj8lv4HgPXlrqZ5mUjLiURERCS0WgvYBOVSNEBEcXZeh+qJSNG0fsUcGmSuJtN4UaXXw7bjiIiICP8bICKOBBL277GcRs6XipNICZI8+w0A/gzvTGRcDctpREREBCAwtCy7XeUAiF+7yHIaOV8qTiIlxI5Nq2l6fB4AUVf+y3IaERER+buE4HoApOxYbjmJnC8VJ5ESYvf01/FyDH8GtKBinUttxxEREZG/yYo5MS6A/wENEFFcqTiJlAAJ++JpemgaAL4dhtkNIyIiIrmEVm8BQPmUDRhjLKeR86HiJFICbJzyBv5OJpu9a1K7ZQ/bcUREROQUleqdGCCiPAdI2Lfbcho5H9aL08iRI6latSr+/v40a9aMefPmnXbeiRMn0qVLF6KioggNDaVVq1b89NNPFzGtSNFz/Fgi9XdPACC5+b3gOJYTiYiIyKkCQsOJd8UBEL9uoeU0cj6sFqcJEyYwbNgwnnjiCVauXEm7du3o3r07O3fuzHP+3377jS5duvDjjz+yYsUKOnbsSO/evVm5cuVFTi5SdKye8h/KcJw9TiwNOg+0HUdEREROIyGkLgCpO1ZYTiLnw2pxeuONN7j11lu57bbbqFu3Lm+99RYVK1bk/fffz3P+t956i3//+99ceuml1KxZkxEjRlCzZk2mTJlykZOLFA2ZmRlU2fg5AHvq3YbL+4LOaS0iIiKFyB3bCICAA39aTiLnw1pxysjIYMWKFXTt2jXH9K5du7JwYf52X3o8Ho4dO0ZERMRp50lPTycpKSnHRaSkWDnjc8qTwBFCqd/zbttxRERE5AzC/jtARFyqBogojqwVp4MHD+J2u4mJickxPSYmhn379uXrOl5//XWSk5O54YYbTjvPiy++SFhYWPalYsWKF5RbpKgwHg8Rq07snd1YeQD+gcGWE4mIiMiZVKp3GR7jEMtB9u/ZZTuOnCPrg0M4p3yR3RiTa1pexo8fz7PPPsuECROIjo4+7XyPPfYYiYmJ2Zddu/QklZJh9bzvqeHeSorxo+5VD9mOIyIiImfhH1yGeK+TA0QsspxGzpW14hQZGYmXl1euvUsJCQm59kKdasKECdx66618/fXXdO7c+Yzz+vn5ERoamuMiUhI4C98BYE3MVYSWPfNrRkRERIqGgyH1AEjbqQEiihtrxcnX15dmzZoxc+bMHNNnzpxJ69atT7vc+PHjGTx4MOPGjaNnz56FHVOkSNq4aj4N038ny7io1PNftuOIiIhIPrnLnRggIvDgastJ5FxZHYLrwQcfZODAgTRv3pxWrVrx0UcfsXPnTu666y7gxGF2u3fvZsyYMcCJ0jRo0CDefvttLrvssuy9VQEBAYSFhVm7HyIXW+KvbwCwOuxymlaubTmNiIiI5Fd49Zbw1/8GiMjPV1SkaLD6Had+/frx1ltvMXz4cBo3bsxvv/3Gjz/+SOXKlQHYu3dvjnM6ffjhh2RlZXHvvfdSrly57Mv9999v6y6IXHS7t22gSdJsAMI7P2w5jYiIiJyLCvVa4DEOMRxm7+7ttuPIOXBMKRsLMSkpibCwMBITE/V9JymWFr13G60OfMMa/6bUf3S27TgiIiJyjnY834DK7p2saP0+zbreaDtOqXYu3cD6qHoikn9HDu6jUcIPADhttKdVRESkODoUemKAiNSdv1tOIudCxUmkGFn/w5sEOuls8apGvTZX2Y4jIiIi58ETe2KAiCANEFGsqDiJFBOpyceps3McAIlN7sZx6eUrIiJSHIXXaAlAXNpGStm3Zoo1vfMSKSb+mPb/7d15dN91ne/x5y972ibpni5J031Jd1pKW+wgCGWpePWOIzPOBfSCyogywOi9jtwzzsyZM56jDlQQcBjBGRlRRtQZwYIUZbVlaWlL971Z2nRJ2jRJ0+zf+0daxkolTZvk81uej3N6DsRUnj18wd/LX/POQwymjqrYMGYtvTl0jiRJOk9FpQtoj2IM5xgHKvaGztE5cjhJCaC9rY2irY8CUD7502RkZQcukiRJ5ys7N4+KjDEAVG1dHbhG58rhJCWA9Ssfpyg6SC0DmHn9F0LnSJKkC1SdPx2ApvK1gUt0rhxOUpyLOjrIW/sQANuKbqDfAL/ZsyRJCW9k54GIATUbA4foXDmcpDi35fXnmNy2g6Yok8nX3x06R5Ik9YDTByKKmrYTdXQErtG5cDhJca7t1fsA2DB0GYMLiwLXSJKknlA8bQFtURpDOc7+it2hc3QOHE5SHNu7+U1mn3yTjijG6Gu/HDpHkiT1kKzc/lRklAAeiEgUDicpjlU//y0A1uctoWjijMA1kiSpJ9UUlALQUv524BKdC4eTFKcOVe5mTu0LAPS/4kuBayRJUk+LjZwDQH8PRCQEh5MUp/Y+8y0yY+1szprJlIsuC50jSZJ62KBJnQciipt30NHugYh453CS4lBdbTUzq34GQNvCLwaukSRJvaF42sW0RukMoY7K8p2hc9QFh5MUh7b8Yjn9Y03sTSth5mV/EjpHkiT1gszsflRkdh6IOOSBiLjncJLiTHNTIxP3PA7AkVmfJS3df0wlSUpWR/OnA9BS4YGIeOcrMinOvLPiEYZSy2EGM+faW0PnSJKkXhQbPQeAAUc9EBHvHE5SHOlob6dw0yMA7J5wM1nZOYGLJElSbxp86kDEmKadHoiIcw4nKY5sfPHHjOnYT13Uj+kfuSN0jiRJ6mXFU+bTEqUzKFZP5b7toXP0PhxOUhzJfuM7AGwa9XHyCwYHrpEkSb0tIzuXisxxABzc5oGIeOZwkuLEjrdWMrV1Cy1RBhM/7De8lSQpVRwr6DwQ0eqBiLjmcJLiRONL9wGwbtDVDB9dErhGkiT1ldMHIvKObgobovflcJLiQOXO9cw58VsAhl/z5cA1kiSpLw2ZtBCAkuYdHoiIYw4nKQ4cfPabALydu5hxU+cGrpEkSX2paMpFtEQZFMROUL5na+gc/QEOJymwowfLmFXzHABZl90VuEaSJPW1jKwcyk8diDi83QMR8crhJAW26+lvkRVrY0tGKdMvuSp0jiRJCuDYwBmAByLimcNJCqix/ijT9j/V+ccX304sFgtcJEmSQkg/dSAi/5gHIuKVw0kKaPMvHiCPRspio5l75Z+FzpEkSYEMmXz6QMRO2j0QEZccTlIgbS1NlOz8NwAOTP8M6enpgYskSVIoRZMvojnKJD/WSMVu33WKRw4nKZCNzz3GcGqoZiBzl30udI4kSQooPTOL8qzxABza9nrgGp2Nw0kKIOroYNCGhwHYNvZ/kZPbL3CRJEkKrXbgdADaKtcFLtHZOJykALa++lPGtpdzIsphxvV3hs6RJElxIH105/dyLPBARFxyOEkBxFbdD8D6wo8xcMiwwDWSJCkeDD11IGJsy07a2toC1+j3OZykPrZ3/ctMa36H1iidccu+FDpHkiTFiaLJc2mKMhkQO0n5rs2hc/R7HE5SHzv+628BsLbgSkaVTAxcI0mS4kVaRiblWRMAOLx9deAa/T6Hk9SHDu7ZxKy6VwEYfKXvNkmSpDMdHzQDgA4PRMQdh5PUhypXfIu0WMS67AVMnrUgdI4kSYoz7x6IqPVARLxxOEl9pO7IfmYeeabzTz5wR9gYSZIUl4ZNOX0gYpcHIuKMw0nqI9uf/ieyY61sS5/MnEuXhc6RJElxaPTE2TRG2fSPNVG2453QOfodDiepDzSdOM7k8icBqJ37F8TS/EdPkiS9V1pGJhWnDkQc2f564Br9Ll+9SX1g8y8fooAGKmIjmXf1jaFzJElSHKsbfOpAxAEPRMQTh5PUyzpaWyja+igAZZM/TWZmZuAiSZIUz9KLTh2IOOb3coonDiepl2164d8ojI5QQz5zrv986BxJkhTnCqcsAmBc6y5aW1sD1+g0h5PUm6KIvLUPAbC56M8YMCAvcJAkSYp3oybMpJFs+sWaKduxPnSOTnE4Sb1ox6r/YlzbHhqjbKZdf1foHEmSlABi6RmUZ00EoHr7G4FrdJrDSepFba99G4C3h36EYYUjA9dIkqREUTd4JuCBiHjicJJ6ScXm1ZSefJu2KI2i674UOkeSJCWQzFMHIgbVeiAiXjicpF5S8/w3AVgz4HLGTpgauEaSJCWS0wcixrbupqWlJXCNwOEk9Yrqyu3MrP0NAHlX3B24RpIkJZqRE2ZwghxyYy2UbV8fOkc4nKReUfb0N0mPRazLuojp8z4QOkeSJCWYWFo65VmTAKjZ+XrgGoHDSepxDccOUXrovwBoW3hH4BpJkpSo6oecOhCxf33YEAEOJ6nHbf/FveTSws608cy77H+EzpEkSQkq69SBiMHHNwUuETicpB7V2tTA+L1PAHB41m2kpfuPmCRJOj+FU08fiNhDc0tz4Br5qk7qQZtXPMwg6tjPcOZd+6nQOZIkKYGNGFdKPbnkxFop2/p26JyU53CSekjU3sbwTd8DYPfET5GTnR24SJIkJbJYWjoV2ZMBqNn5RuAaOZykHrLlxR8yquMgtdEAZn/49tA5kiQpCTQMntH5BwfWB+2Qw0nqGVFE7hsPALBh1CcoGDgwbI8kSUoKWWMuAmDw8c2BS+RwknrA3jXPMb51J01RJpM+fFfoHEmSlCRGTFsMwNi2vTQ1NQWuSW0OJ6kHnHzpXgDeGrSMUaPHBK6RJEnJonDMVOrpR3aslbJta0PnpDSHk3SBDu5YQ+mJN2mPYoy45q9C50iSpCQSS0uj/NSBiKMeiAjK4SRdoEPPfROANf2WMGnqrMA1kiQp2ZwYMrPzD6rWB+1IdQ4n6QLUVu2htGYlANmX+bVNkiSp550+EDHk+JbAJanN4SRdgL3PfJPMWDsbMmYx+5LLQ+dIkqQkNPLUgYiStr00NZ0MXJO6HE7SeWqqq2HK/p8B0Hjx7cRiscBFkiQpGQ0vnsxxBpAda2PvljWhc1KWw0k6T1ufvo9+NLErVsLFH/qT0DmSJClJxdLSqMzpPBBRu8sDEaE4nKTz0N5ykjG7HgfgQOlnyMhID1wkSZKSmQciwnM4Sedhy3OPMCSqpYohzFt2a+gcSZKU5LLHzANgaJ0HIkJxOEndFLW3MXjDPwOwbeyN9O+XG7hIkiQlu1HTFgFQ0raPk42NgWtSk8NJ6qadr/4Ho9v3czzqz8zr7widI0mSUsDQoonUkkdWrJ19W98KnZOSHE5Sd0QRaavvB+Dtwv/J0CFDAgdJkqRUcMaBiJ0eiAjB4SR1Q/mG3zCxeSvNUQYTlv1V6BxJkpRCTgydBUDs4PqwISnK4SR1Q/2vvwXAm/lXM6ZkXOAaSZKUSnJLLgI8EBGKw0k6R0f2bGB6/So6ohiDl94dOkeSJKWYUdMWA1DSXk5jY0PgmtTjcJLO0YFnvwHAWzmLmD5zfuAaSZKUaoaOnsBR8smMtVO2+c3QOSnH4SSdg4Yj5ZQefhaA2AfuDBsjSZJSUyzG/twpABzb5XDqaw4n6RzsevpbZMba2ZheyvxLl4bOkSRJKarx1IGI9IMbApekHoeT1IWWhmNMLP8JALVzP09aWixwkSRJSlX9SuYBMLTeAxF9zeEkdWHbLx9gAI3soYgFV/9p6BxJkpTCRpX+94GIhob6wDWpxeEkvY+orZlR274PwL4pt5CdmRm4SJIkpbIhI8dSw0AyYh2UbfHrnPqSw0l6H9uef4yh0VEORYOY9+HPhs6RJEmp7ncORBzf9UbgmNTicJL+kI4OBrz9EACbij9JQd6AwEGSJEnQNOzUgYhDHojoSw4n6Q/Ys/rnFLeV0xDlMv0jfxk6R5IkCYCcUwcihtVvDVySWhxO0h/Q/up9ALw59H8wYnhh4BpJkqRORdP/+0BEfV1t2JgU4nCSzqJq0ytMatpIS5TOmGvvDp0jSZL0rsEjSqhmEOmxyAMRfcjhJJ3F0ZXfAuD1AVcyceKUwDWSJElnOtCv8/VJ3W6HU19xOEm/51j5FqbVvgJA3hV3Ba6RJEl6r6ZhswFIP/RO4JLU4XCSfk/FL79BWizizcwFzLloYegcSZKk98gd23kgorBhS+CS1OFwkn5H49H9TDn0DABti75ILBYLXCRJkvReRaWLABjTXkld3bHANanB4ST9jl1P30s2rWxKm8KCP1oWOkeSJOmsBhWO4TBDSItFlG32G+H2BYeTdErbyTrG7f0RANWzPktGRnrgIkmSpD+sqn/ngYh6D0T0CYeTdMq2FQ+SxwnKGMkl19wUOkeSJOl9NQ2bBUCGByL6hMNJAqK2Fgo3PwrAzgmfIjcnK3CRJEnS++s/dj4AhQ1bA5ekBoeTBOz8zQ8Y1nGE6qiAudf/RegcSZKkLhVP7zwQUdyxn+PHjgauSX4OJymKyH3rOwCsG3UDQwYWBA6SJEnqWsGwIg7FhpIWiyjf8nronKTncFLKK3/raYpb93Iiymbqh+8MnSNJknTOqvpNBaB+z1uBS5Kfw0kpr+ml+wB4Y9D1FI8eHbhGkiTp3LUM7zwQkXl4Q+CS5OdwUko7sn01kxvfpi1KY8TVd4fOkSRJ6pb+4zwQ0VccTkpph5/7JgCr+32Q0mnTA9dIkiR1T3HpYgDGRAeoPVYduCa5OZyUsuqqdjL16G8AyL7srsA1kiRJ3Zc/dCRVsWEAlG9eHbgmuTmclLLKnv4G6bGINRkXcfElS0LnSJIknZdD/TsPRDR4IKJXOZyUkpqPH2LSgf8EoPHi24nFYmGDJEmSzlPz8NkAZB5+J3BJcnM4KSXtfOY+cmhha2wCC6/4WOgcSZKk8zbg1IGIESe2BS5Jbg4npZyOpgaKd/07AAdKP0tWZnrgIkmSpPM3ZkbngYjiqIpjNUcC1yQvh5NSzo5ffZeCqJ6KqJAFy24OnSNJknRB8gYVciBWCHggojc5nJRa2tsY9M6/ALB17I3k9csNHCRJknThTh+IOLHXAxG9xeGklLLnlScobD/I0WgAsz9ye+gcSZKkHtFa2HkgIssDEb3G4aTUEUWkr74fgDXD/4TCIYMDB0mSJPWM0wciRjZuDVySvBxOShkH1j1HSctOTkZZTFjmN7yVJEnJo/jUgYjR0SFqjhwMXJOcHE5KGQ2/uReAVfnXMmFsSeAaSZKknpM3cBj7YyMAqNjigYje4HBSSji6ey2TG96kPYox5Kq/Cp0jSZLU4w4PmAZA4941gUuSk8NJKeHgs98AYFXOEmbPnBW4RpIkqee1nD4QccQDEb3B4aSk13h4L5Ornwcg/QN3EIvFAhdJkiT1vPzxFwMw6sS2wCXJyeGkpLf36W+QQQdr02ZxyaVXhs6RJEnqFWOmLwJgFIepPlwVuCb5OJyU1Fobahhf8VMAai/6POlpvtskSZKSU/+CIVSmjQKgcvOqwDXJx+GkpLbrl8vJpZntjOXSpX8SOkeSJKlXHR4wFYDGfR6I6GkOJyWtqKWREdt+AMC+KbeQk5URuEiSJKl3tRXOASDbAxE9zuGkpLXrhe8xKKrlQDSUBctuCZ0jSZLU6wpOH4ho9EBET3M4KTl1tJP39sMAbCj+cwbl9w8cJEmS1PuKZywEYCTVHDlYGbgmuTiclJTKV/2EEW0HqI36M/PDXwydI0mS1Cf65Q2mIm00AJVbVgeuSS4OJyWfKKLjteUAvDHkYxSNGBa2R5IkqQ8dHjAN8EBET3M4Kekc3vQbxjZtpTnKZMy1d4XOkSRJ6lPtI2YDkFu9MXBJcnE4KenUrvwWAK8NWMq0SRMD10iSJPWtggkLABjduI0oigLXJA+Hk5LK8bJ3mFy3io4oRt7lvtskSZJSz5jShXREMQqp4UhVReicpOFwUlLZv+IbAKzKWsTF8+YHrpEkSep7uXkDqUgvAmD/Vg9E9BSHk5JGU005kw49C0D7wi8Si8UCF0mSJIVRndd5IOKkByJ6jMNJSWPPM98ikzbWxUpZ/MFrQudIkiQF0z5yDuCBiJ7kcFJSaG88Rsne/wDgyOzbyEz30ZYkSalr4PiLARh9crsHInqIry6VFHateID+nGQXxVx6zZ+FzpEkSQpqzPRLaI9iDOcohw6Uhc5JCg4nJbyotYlhW74PwI4Jn6J/TlbgIkmSpLBy+hdQkV4MwIEtHojoCQ4nJbx9L/0rgzuOcigaxPzrPxs6R5IkKS5U53ceiGgq80BET3A4KbF1dJDz5oMArB31ZwwfmB84SJIkKT50jJwLQG71psAlycHhpIR24K2fM7K1nLool6kfviN0jiRJUtwYOKHzQERR0zaijo7ANYnP4aSE1vzyfQD8duD/YPzokYFrJEmS4seY0s4DEcOopWr/3tA5Cc/hpIR1dNurjGvcSEuUzuhr7gydI0mSFFdy+uVRnlECQNXW1wPXJD6HkxJW9a++CcCruR9i1rRpgWskSZLiT82pAxHNHoi4YA4nJaQT+7cw8dgrAORcdmfYGEmSpDgVnToQ0a/GAxEXyuGkhFTxzDdII+K3GQtYdMni0DmSJElxadDEzgMRxU3bPRBxgRxOSjitx6sYX/U0ACfnf4G0tFjgIkmSpPhUPG0BbVEaQzhOVcXu0DkJzeGkhLN7xf1k0cZGJvGBD304dI4kSVLcys4d4IGIHuJwUkKJWk8yYscTAFRO/TQ5memBiyRJkuJbTX4pAE3lawOXJDaHkxLKvpceZ2BUS1U0mIuvvTl0jiRJUvwb1XkgYkDNxsAhic3hpMQRRWS89V0A1o34BEMLBgQOkiRJin+DJy4AoLjZAxEXwuGkhHFk068pbtlNY5TNxGs/HzpHkiQpIRRPu5jWKJ3B1HOgbGfonITlcFLCOPbr5QD8dsBVTB5bEjZGkiQpQWTl9KM8YywAVdtWh41JYA4nJYTGqh1MrH0NgAF/dHvgGkmSpMRytKDzQERLxduBSxKXw0kJYd+z95FGxOvp87jk4kWhcyRJkhJKbNQcAAbUbAobksAcTop7HY21jC3/GQD1c271G95KkiR106BJnQcixngg4rw5nBT3dj3/XfrRxG5Gs/iqPwmdI0mSlHDGTL2YliidgTSwf9/20DkJyeGk+NbRzsCNjwGwfeyN9M/JDBwkSZKUeDKzcynPHAdA1bbXA9ckJoeT4lrl608xvP0Qx6IBzFn22dA5kiRJCetYwXQAWivWBi5JTA4nxbWW1x4E4M3BH2HUsCGBayRJkhJXbNRcwAMR5yv4cHrooYcYN24cOTk5zJs3j1dfffUPfm5VVRWf/OQnmTJlCmlpadx55519F6o+d2z3W4xv3EBrlM7Iq74QOkeSJCmhDTl1IKKkeQcd7R6I6K6gw+nJJ5/kzjvv5J577mHdunUsWbKEa6+9lvLy8rN+fnNzM8OGDeOee+5h9uzZfVyrvlb1q/sAWJXzAWZOKw1cI0mSlNiKp86jJcqgIHaCyr1bQucknKDD6d577+WWW27h1ltvZdq0aSxfvpzi4mIefvjhs37+2LFj+fa3v81NN91EQUFBH9eqLzXXHmDS4V91/snCvyAW8wS5JEnShcjIyqEsczwAh7Z7IKK7gg2nlpYW1q5dy9KlS8/4+NKlS1m1alWP/XWam5upq6s744fi3+4V95NJG+/EprD4j64OnSNJkpQUagd2/i6e1vK3A5cknmDDqbq6mvb2dgoLC8/4eGFhIQcPHuyxv87Xv/51CgoK3v1RXFzcY//d6h1R60lG7XwCgKqpnyIzPfiX4kmSJCWHUwci8o5tDhySeIK/Iv3934IVRVGP/rasv/7rv+b48ePv/qioqOix/271jj0v/hsDo+MciIaw4LpPhc6RJElKGsMmLwROH4hoD1yTWIINp6FDh5Kenv6ed5cOHz78nnehLkR2djb5+fln/FAciyKy1vwzABtGfYJBef0CB0mSJCWPoslzaY4yyY81UrHbd526I9hwysrKYt68eaxcufKMj69cuZLFixcHqlJoh955geKWPTRG2Uy+5vOhcyRJkpJKRlY2ZVkeiDgfGSH/4nfffTc33ngj8+fPZ9GiRTzyyCOUl5dz2223AZ2/zW7//v384Ac/ePfnrF+/HoCGhgaOHDnC+vXrycrKorTUc9XJ4NhvllMIrM5byodKxoTOkSRJSjq1BdOhejvtlR6I6I6gw+mGG26gpqaGv//7v6eqqooZM2awYsUKSkpKgM5vePv739Np7ty57/7x2rVreeKJJygpKWHfvn19ma5e0FC1ncm1v4UY5H3wi6FzJEmSklLa6Iug+mceiOimWBRFUeiIvlRXV0dBQQHHjx/3653izOZHb2N6xY94I2M+C+55we/dJEmS1AvKtrxByX8spSHKJfdv9pOenh46KZjubIPgV/UkgPbGWsZV/ByAhrmfcTRJkiT1kqLJF9EUZTIgdpKKnRtD5yQMh5Piws7nHqYfTeymiMVXfjx0jiRJUtJKz8ikLGsiAId3eCDiXDmcFF57G4M3PwbAznE3kpsd9EvvJEmSkt7xgdMBPBDRDQ4nBVe++imGtx/maDSA2R/+bOgcSZKkpJde1HlwLd8DEefM4aTgWlc9CMBbQz7KyCGDA9dIkiQlv2GTFwIwtmUXba2tgWsSg8NJQR3d+QYTGt+hNUqnaKknyCVJkvpC0aTZNEbZ9I81UbHLAxHnwuGkoA4+fx8Aq3OXMH3q1MA1kiRJqSEtI5PyrAkAHNnxRuCaxOBwUjBNR/cz6chKANIWfj5wjSRJUmo5PmgGAO2VawOXJAaHk4LZveLbZNLGO7GpLFxyVegcSZKklJJRdBEABbVbApckBoeTgohaTzJ6948AODT902Sk+yhKkiT1peFTLgE8EHGufLWqIHb9+l8ZGNVRFQ1hwTU3h86RJElKOaMnzKIxyqZfrJmyHetD58Q9h5P6XhSRs/afAXhn9A0UDMgNHCRJkpR60jIyKMueBED1Tg9EdMXhpD53YP2vKG7dS2OUzdTrvhA6R5IkKWXVDZoOQEflusAl8c/hpD5X9+L9ALyefzUlRaMD10iSJKWujOJ5AAys3Ry4JP45nNSn6iu3Mfn4KgAGftBveCtJkhRS4ekDEa27aW1tCVwT3xxO6lNlz91HWizizYz5zL3o4tA5kiRJKW3U+JmcIIfcWAtl2/3teu/H4aQ+03biGOMr/xOAxnmfIxaLhQ2SJElKcWnp6ZRlTQagZocHIt6Pw0l9ZsdzD9OPJnZRzMIP/c/QOZIkSQLqB3ceiIj2+47T+3E4qW+0tzF08/cB2DPhRnKyMgIHSZIkCSDz9IGI41sCl8Q3h5P6xN7f/gfDOw5zNBrAnOs+GzpHkiRJp/zugYiW5ubANfHL4aQ+0bH6IQDWDvsYw4cMClwjSZKk00aNn049ueTEWinf/nbonLjlcFKvq96+mgknN9IapVO81BPkkiRJ8SSWlk559qkDETvfDFwTvxxO6nWHVi4H4PXcP2Lq5ClhYyRJkvQeDYNnABAd8B2nP8ThpF51sqaSydUrAci49PbANZIkSTqbrFMHIgZ5IOIPcjipV+1a8W0yaeedtKksuPTK0DmSJEk6ixFTFwIwtnUvzc0nA9fEJ4eTek1HcyPFe34MwJHpt5Ce5je8lSRJikcjxk6jnn5kx1op3+pv1zsbh5N6zc7ffJ+BUR0HoqEsuPbG0DmSJEn6A2JpaZRld34tes2uNwLXxCeHk3pHFNHv7UcA2Fz0p+T1yw0cJEmSpPdz4tSBCA6sD9oRrxxO6hWVbz9Hces+TkTZTL3OoxCSJEnxLquk80DEYA9EnJXDSb2i/uX7AXiz4BqKR48KXCNJkqSuvHsgom0PTScbA9fEH4eTetzxyq1Mq1sFwKAr/Ia3kiRJiWDEmCkcZwBZsXbKt60NnRN3HE7qcWUr7gPgzcyLmT17fuAaSZIknYtYWhrlOZMBOLrrzcA18cfhpB7VeuIYEw/8FwDN8z5HLOYJckmSpETROHgmALED6wKXxB+Hk3rU9hUP0o8mdlPMgg99LHSOJEmSuiFrzKkDEXUeiPh9Dif1mKi9lWFb/w2AfZNuJjszI3CRJEmSumPUtM4DESVt+2g6eSJwTXxxOKnH7HntPyjsOMzRKI/Z130mdI4kSZK6aXjxJGrJIyvWTtmWt0LnxBWHk3pMtPphAN4e/jGGDhoYNkaSJEndFktLoyJnCgDHdnsg4nc5nNQjDm1bxcSmjbRE6Yy9xhPkkiRJiapxyAwAYgfWhw2JMw4n9YgjK5cD8Gb/DzJxwuSwMZIkSTpv2SWd305mSP3WwCXxxeGkC9ZYXcGUmhcAyLr09sA1kiRJuhCjpi0CoKStjJMnGgLXxA+Hky7YrhXfJpN23kmbxvxFV4TOkSRJ0gUYNno8R8knM9bOvq1+ndNpDiddkI7mRkr2/BiAmpm3kpbmN7yVJElKZLG0NCpzpwJQu8vhdJrDSRdk+8pHKaCe/QxjwTX/K3SOJEmSesDpAxHpBzcELokfDiedvyhiwPp/AWBr8Z/SPzcncJAkSZJ6Qu7pAxF1WwKXxA+Hk85b+doVFLeVcSLKZtoyj0JIkiQli1Glpw5EtJfTeKIucE18cDjpvJ14+QEA3hp4HaNHjAxcI0mSpJ4ybNQ4ahhIRqyDss1+nRM4nHSejpZvYVr9agCGfshveCtJkpRUYjEqc6cAULv7rcAx8cHhpPNS8ey9ALyZuYDpMy8KXCNJkqSe1jR0FgDpB9eHDYkTDid1W3N9DZOrfgFA64LPEYt5glySJCnZ5IydB8Cw+q2BS+KDw0ndtv3Zh8ilmV2xMSy4/GOhcyRJktQLRp86EDGmvZwT9ccD14TncFK3RO2tjNj2bwCUT7qZzIz0wEWSJEnqDUNHjuUIg0iPRZRteSN0TnAOJ3XLrld+zPCOIxyN8ph73WdC50iSJKkX7e83FYDjHohwOKl7Ym98F4D1hf+TQQMLAtdIkiSpNzUNnQlAhgciHE46dwe3rGJi0yZaonTGXnNH6BxJkiT1sn5j5wMwrMEDEQ4nnbPDLywH4M3+lzN+/MSwMZIkSep1o6efPhBRSX3dscA1YTmcdE4ajpQzreYFAHKW3B64RpIkSX1hSOEYDjOYtFhE+ebXQ+cE5XDSOdn1y2+TGWvnnfRS5i28PHSOJEmS+siBUwci6vasCVwSlsNJXWpvbmTsvicBqJ11q9/wVpIkKYU0DZsFeCDC4aQubXv+ewyknv0M4+KrbwydI0mSpD7Uf9zFAAxv2Ba4JCyHk95fFJG/4V8A2F7ySXJzsgIHSZIkqS8VlXYeiCju2E/d8aOBa8JxOOl97X3zGYrbymmIcihd5lEISZKkVDNo+GgOMvTUgYjVoXOCcTjpfZ189TsArB10HSOGFwaukSRJUghV/TsPRNSn8IEIh5P+oJqyTZQ2vE5HFGPYlX7DW0mSpFTVfOpAROah9WFDAnI46Q+qePY+ANZmL6B0xtzANZIkSQql/7j5QGofiHA46aya6mqYcvBpANoX/EXgGkmSJIVUXLoYgDHRAY4fqwlcE4bDSWe1fcV3yKWZXbES5n/wI6FzJEmSFNDAYSOpig0DoGLzqsA1YTic9B5Reysjt/8AgMopN5ORkR64SJIkSaEd7D8NgPq9qXkgwuGk99jx0o8YHlVzNMpj7nWfDZ0jSZKkOPDfByI2BC4Jw+Gk90h787sAvDPyjynIzwtcI0mSpHgwYPzFAIw4kZoHIhxOOsP+za8xqXkzLVE6467xBLkkSZI6jSldBEBRVMXxo4cD1/Q9h5POUPPCcgDWDLickrETwsZIkiQpbuQPKeRArBCA8s2rA9f0PYeT3lV3uJxpR38DQL8/+kLgGkmSJMWbg/2nAql5IMLhpHft/uV9ZMba2Zg+ndkLPhg6R5IkSXGmZfhsALIPp96BCIeTAGhramBc2U8AOD7nM8RiscBFkiRJijd54+cDqXkgwuEkALY+/ygDqWc/w5m/9M9D50iSJCkOFc9YDMDo6BC11YcC1/Qth5Mgihi44XsA7Bz7SXKyswIHSZIkKR7lDxzG/tgIAMo3rwpc07ccTmL3609T3F5OQ5TD9GW3h86RJElSHDs4YBoAJ/al1oEIh5Nofu07ALw9eBnDhg0PXCNJkqR41nbqQERWih2IcDiluCN7N1J64g06ohgjrvrL0DmSJEmKc3kTLgZgZOP2wCV9y+GU4iqfuw+AtTmXMLl0duAaSZIkxbvi0kUAjIoOc/TwgcA1fcfhlMJOHq9m6qFnAIgu+YvANZIkSUoEeQOHUBEbBUDFltWBa/qOwymFbVvxHXJpZldsLPMu+0joHEmSJCWIw3mdByIa96bOgQiHU4qK2loYveNxAA5M+xTp6T4KkiRJOjdtw2cBkH3kncAlfcdXyylq64tPMDyqpibKZ+51t4bOkSRJUgLJG78AgJGN2wKX9B2HU4rKfOufAdg06uPkDcgLXCNJkqREMmbGQgBGUk31ocrANX3D4ZSCKja+zKSWLbRE6Uy41hPkkiRJ6p4B+YMpTxsNwP7NqXEgwuGUgo7++n4A1uRdQdGYsWFjJEmSlJAODygF4MS+1DgQ4XBKMbUH91F67EUABlz2xcA1kiRJSlTthZ0HInKrU+NAhMMpxexesZzMWDsbM2Ywc/4fhc6RJElSgsqf2HkgYlTj9sAlfcPhlEJamxqYWP4TABrmfIZYLBa4SJIkSYlqTOlCOqIYhdRQXVUeOqfXOZxSyJbnvkcBDVQynIuWfjJ0jiRJkhJY/7yBVKQXAVC5ZVXgmt7ncEoRUUcHgzd+D4Dd4/4X2VlZgYskSZKU6A7nTQOgcd/awCW9z+GUIna+/jTF7RU0RLnM+PDtoXMkSZKUBDpGzAagX/XGwCW9z+GUIlpfexCAdUOXMWTI0MA1kiRJSgYFE04diDiZ/AciHE4p4NCed5je+AYdUYyRV/kNbyVJktQzSkoX0h7FGM5RjhzYFzqnVzmcUsD+5+4F4O3chUycOitwjSRJkpJF7oB8KtKLAajcsjpwTe9yOCW5E7VHmHb4lwDEFn0+cI0kSZKSzZH8UgCa9q0JXNK7HE5JbvsvHyCXFnaljWXuBz4cOkeSJElJJjp1ICK3JrkPRDicklhHawtFu/4dgIPT/jdp6f7tliRJUs8qmNh5IKLo5Haijo7ANb3HV9JJbMuL/87wqIYaCph73S2hcyRJkpSESkovoT2KMZRaDlftC53TaxxOSSx7zSMAbBn1cfr3HxC4RpIkSckop18e5eklAOzfvCpwTe9xOCWpsg0vMallK81RBhOuuyN0jiRJkpJY9akDEc3lbwcu6T0OpyR17Df3A/B2/ocYVTQ2bIwkSZKSWsfIzgMR/arfCVzSexxOSehY1V5m1L4IQP4Hvxi4RpIkSclu0MRLAChq2pG0ByIcTklo9y+XkxHrYGPGTEov+kDoHEmSJCW5MdMupi1KYwjHObR/T+icXuFwSjItJxuYVPkUAI0XfYZYLBa4SJIkSckup98AyjI6D0Qc2JKcByIcTklm87OPUEAD+xnO3Cs/GTpHkiRJKeJokh+IcDglkaijnaGbHgVgz4QbycrKDFwkSZKkVNExcg4A/Ws2hg3pJQ6nJLJj1S8o7qikIcplxrLbQ+dIkiQphQye1Hkgorhpe1IeiHA4JZHW3z4IwIZhH2bQ4CGBayRJkpRKxkybT2uUziDqqSrfGTqnxzmcksSBneuZcfItOqIYo66+M3SOJEmSUkx2Tn/KMsYCcHDb6rAxvcDhlCQOPL8cgLf7LWbcpBlhYyRJkpSSjhYk74EIh1MSqD92mOmHfwlA+uK/CFwjSZKklHXqQMSAJDwQ4XBKAtt++R1yYy3sTBvH7MXLQudIkiQpRb17IKJ5R9IdiHA4Jbj21hZKdv87AEem/2/S0v1bKkmSpDDGTJ1PS5TOQBo4sG9H6Jwe5avsBLf5hccZHtVQQwFzr701dI4kSZJSWFZOLmWZ4wA4uG1V4Jqe5XBKcLlvPwLA1qJPkNuvX+AaSZIkpbpjBdMBaKlIrgMRDqcEtmfdi0xq3UZLlMGk6+4InSNJkiS9eyAi72hyHYhwOCWw4y/eD8DbBVdSOGpM4BpJkiQJhkzuPBAxpnlnUh2IcDglqOr9u5l5/CUABl3hu02SJEmKD2OmzqMlyiCfE+zfuzV0To9xOCWovSuWkxHrYGPmLKbMuTR0jiRJkgRAZlYO+zLHA3Bo2+rANT3H4ZSAmk7UMXn/zzr/eN5nA9dIkiRJZzpWUAok14EIh1MC2vzcIxTQwH4KmfuhPw2dI0mSJJ0hbdRcAPKObgpc0nMcTgkm6mhn2ObvA7Bv4o1kZGYGLpIkSZLONGTKQqDzQERHe3vgmp7hcEowW1/7T8Z0VNIQ5TJj2e2hcyRJkqT3GDN5Ls1RJvmxRir3bA6d0yMcTgmmY/VDALwz/HoKBg0OXCNJkiS9V0ZW9rsHIg5vfz1wTc9wOCWQyh3rmHFyDR1RjKKr7wydI0mSJP1BtQOnA9CaJAciHE4J5ODzywFY338xYyZODxsjSZIkvY+00Z0HIvKPJceBCIdTgqg7eojpR1YAkLHYr22SJElSfBs6+RIASpp3JcWBCIdTgtj2zAPkxlrYmTaemYuvDZ0jSZIkva/iyXM5GWUxIHaSil0bQ+dcMIdTAmhraWbsnicAODrzFmJp/m2TJElSfMvIzKIsawIAR5LgQISvwBPAphceZzg1VDOQ2df879A5kiRJ0jk5fupARFvlusAlF87hlAD6rXsEgO3FnyAnt1/gGkmSJOncJNOBCIdTnNu55tdMbt1OS5TB5GV3hM6RJEmSztmwKQsBKGnZRXtbW+CaC+NwinMNLz8AwLqBVzFsRHHgGkmSJOncFU+aQ2OUTf9YExW73gmdc0EcTnHsSOUuZta9DMDgK/4ycI0kSZLUPekZGZQnyYEIh1Mc27NiORmxDjZlzWbS7EWhcyRJkqRuqx00A4D2BD8Q4XCKUycb6ph24GcANM//XOAaSZIk6fxkFHUeiCioTewDEQ6nOLXp2X8mnxNUxkYw54obQudIkiRJ52XY5NMHInYn9IEIh1McijraKdz6fQAqJt1IekZG4CJJkiTp/BRNnMWJKId+sWYqdq4PnXPeHE5xaNMrP2dMx37qo1xmLPt86BxJkiTpvKVnZFCWNRFI7AMRDqd49PpDAGwq/Ah5BYMDx0iSJEkXpm7wqQMR+xP3QITDKc6Ub1vLzKa1tEcxxlxzV+gcSZIk6YJlnjoQMfDY5sAl58/hFGcOPr8cgA39L2X0+GlhYyRJkqQeMGzKqQMRrbtpa20JXHN+HE5x5HjNQWbVPAtA9pIvBK6RJEmSekbRhJk0RLnkxlqo2JGYv13P4RRHtj7zADmxVnalT6D0kqtD50iSJEk9Ii09nbLsUwcidrwRuOb8OJziRGtLMxP2PgHA0Zm3EEvzb40kSZKSR/2gzgMRUYIeiPDVeZx45/kfMIyjVDOQ2dd8OnSOJEmS1KMyii8CYGBtYh6IcDjFgygib/0jAOwc86dk5/QLHCRJkiT1rMKpiwAoad1Da0tz4JruczjFge1rf83kth00R5lMXvbF0DmSJElSjxs9rpQ6+pETa6V8e+L9dj2HUxxoeOkBADYMuoohhUWBayRJkqSel5aeTkXWJABqdrweuKb7gg+nhx56iHHjxpGTk8O8efN49dVX3/fzX375ZebNm0dOTg7jx4/nu9/9bh+V9o6D5TuZXf8KAEOvvDNsjCRJktSL6gefOhBxwHecuuXJJ5/kzjvv5J577mHdunUsWbKEa6+9lvLy8rN+/t69e7nuuutYsmQJ69at46tf/Sp33HEHP/3pT/u4vOfse/Y+MmIdbMqew/gZl4TOkSRJknpN5qkDEYOObwlc0n2xKIqiUH/xSy65hIsuuoiHH3743Y9NmzaNj370o3z9619/z+f/3//7f/nFL37B1q1b3/3YbbfdxoYNG1i9evU5/TXr6uooKCjg+PHj5OfnX/gv4gI0NtTS9q1S8jnB+g98lzlX/lnQHkmSJKk37d+zmdE/WExzlEnsq5VkZecE7enONgj2jlNLSwtr165l6dKlZ3x86dKlrFq16qw/Z/Xq1e/5/Kuvvpo1a9bQ2tp61p/T3NxMXV3dGT/ixcZf/jP5nGB/bASzLv9E6BxJkiSpV40aO406+pMda6V829rQOd0SbDhVV1fT3t5OYWHhGR8vLCzk4MGDZ/05Bw8ePOvnt7W1UV1dfdaf8/Wvf52CgoJ3fxQXF/fML6AHZA8Zw670CVROuZm09PTQOZIkSVKviqWlsXPAxbyTM5+ooz10TrdkhA6IxWJn/HkURe/5WFeff7aPn/bXf/3X3H333e/+eV1dXdyMpzlX/hnRFTcwrqMjdIokSZLUJ+Z96b9CJ5yXYMNp6NChpKenv+fdpcOHD7/nXaXTRowYcdbPz8jIYMiQIWf9OdnZ2WRnZ/dMdC+IpaWRnhb8uKEkSZKk9xHsFXtWVhbz5s1j5cqVZ3x85cqVLF68+Kw/Z9GiRe/5/Oeff5758+eTmZnZa62SJEmSUlvQtzruvvtuvve97/HYY4+xdetW7rrrLsrLy7ntttuAzt9md9NNN737+bfddhtlZWXcfffdbN26lccee4xHH32UL33pS6F+CZIkSZJSQNCvcbrhhhuoqanh7//+76mqqmLGjBmsWLGCkpISAKqqqs74nk7jxo1jxYoV3HXXXTz44IOMGjWK+++/nz/+4z8O9UuQJEmSlAKCfh+nEOLp+zhJkiRJCichvo+TJEmSJCUKh5MkSZIkdcHhJEmSJEldcDhJkiRJUhccTpIkSZLUBYeTJEmSJHXB4SRJkiRJXXA4SZIkSVIXHE6SJEmS1AWHkyRJkiR1weEkSZIkSV1wOEmSJElSFxxOkiRJktQFh5MkSZIkdcHhJEmSJEldcDhJkiRJUhccTpIkSZLUBYeTJEmSJHXB4SRJkiRJXXA4SZIkSVIXHE6SJEmS1AWHkyRJkiR1weEkSZIkSV1wOEmSJElSFxxOkiRJktQFh5MkSZIkdcHhJEmSJEldyAgd0NeiKAKgrq4ucIkkSZKkkE5vgtMb4f2k3HCqr68HoLi4OHCJJEmSpHhQX19PQUHB+35OLDqXeZVEOjo6OHDgAHl5ecRisdA51NXVUVxcTEVFBfn5+aFzlAB8ZtQdPi/qLp8ZdZfPjLornp6ZKIqor69n1KhRpKW9/1cxpdw7TmlpaRQVFYXOeI/8/PzgD44Si8+MusPnRd3lM6Pu8plRd8XLM9PVO02neRxCkiRJkrrgcJIkSZKkLjicAsvOzuZrX/sa2dnZoVOUIHxm1B0+L+ounxl1l8+MuitRn5mUOw4hSZIkSd3lO06SJEmS1AWHkyRJkiR1weEkSZIkSV1wOEmSJElSFxxOveyhhx5i3Lhx5OTkMG/ePF599dX3/fyXX36ZefPmkZOTw/jx4/nud7/bR6WKF915Zn72s59x1VVXMWzYMPLz81m0aBG/+tWv+rBW8aC7/5457be//S0ZGRnMmTOndwMVd7r7zDQ3N3PPPfdQUlJCdnY2EyZM4LHHHuujWsWD7j4zP/zhD5k9ezb9+vVj5MiRfPrTn6ampqaPahXaK6+8wvXXX8+oUaOIxWL853/+Z5c/JxFeAzucetGTTz7JnXfeyT333MO6detYsmQJ1157LeXl5Wf9/L1793LdddexZMkS1q1bx1e/+lXuuOMOfvrTn/ZxuULp7jPzyiuvcNVVV7FixQrWrl3L5ZdfzvXXX8+6dev6uFyhdPeZOe348ePcdNNNfOhDH+qjUsWL83lmPvGJT/DrX/+aRx99lO3bt/OjH/2IqVOn9mG1QuruM/Paa69x0003ccstt7B582Z+8pOf8NZbb3Hrrbf2cblCOXHiBLNnz+Y73/nOOX1+wrwGjtRrFixYEN12221nfGzq1KnRV77ylbN+/v/5P/8nmjp16hkf+9znPhctXLiw1xoVX7r7zJxNaWlp9Hd/93c9naY4db7PzA033BD9v//3/6Kvfe1r0ezZs3uxUPGmu8/Ms88+GxUUFEQ1NTV9kac41N1n5pvf/GY0fvz4Mz52//33R0VFRb3WqPgFRD//+c/f93MS5TWw7zj1kpaWFtauXcvSpUvP+PjSpUtZtWrVWX/O6tWr3/P5V199NWvWrKG1tbXXWhUfzueZ+X0dHR3U19czePDg3khUnDnfZ+b73/8+u3fv5mtf+1pvJyrOnM8z84tf/IL58+fzjW98g9GjRzN58mS+9KUvcfLkyb5IVmDn88wsXryYyspKVqxYQRRFHDp0iKeeeoply5b1RbISUKK8Bs4IHZCsqquraW9vp7Cw8IyPFxYWcvDgwbP+nIMHD57189va2qiurmbkyJG91qvwzueZ+X3/9E//xIkTJ/jEJz7RG4mKM+fzzOzcuZOvfOUrvPrqq2Rk+D8BqeZ8npk9e/bw2muvkZOTw89//nOqq6v5/Oc/z9GjR/06pxRwPs/M4sWL+eEPf8gNN9xAU1MTbW1tfOQjH+GBBx7oi2QloER5Dew7Tr0sFoud8edRFL3nY119/tk+ruTV3WfmtB/96Ef87d/+LU8++STDhw/vrTzFoXN9Ztrb2/nkJz/J3/3d3zF58uS+ylMc6s6/Zzo6OojFYvzwhz9kwYIFXHfdddx7773867/+q+86pZDuPDNbtmzhjjvu4G/+5m9Yu3Ytzz33HHv37uW2227ri1QlqER4Dez/3dhLhg4dSnp6+nv+35jDhw+/Z1GfNmLEiLN+fkZGBkOGDOm1VsWH83lmTnvyySe55ZZb+MlPfsKVV17Zm5mKI919Zurr61mzZg3r1q3jC1/4AtD5ojiKIjIyMnj++ee54oor+qRdYZzPv2dGjhzJ6NGjKSgoePdj06ZNI4oiKisrmTRpUq82K6zzeWa+/vWvc+mll/LlL38ZgFmzZtG/f3+WLFnCP/zDP8TNuweKH4nyGth3nHpJVlYW8+bNY+XKlWd8fOXKlSxevPisP2fRokXv+fznn3+e+fPnk5mZ2Wutig/n88xA5ztNn/rUp3jiiSf8/eMpprvPTH5+Phs3bmT9+vXv/rjtttuYMmUK69ev55JLLumrdAVyPv+eufTSSzlw4AANDQ3vfmzHjh2kpaVRVFTUq70K73yemcbGRtLSznyJmZ6eDvz3uwjS70qY18CBjlKkhB//+MdRZmZm9Oijj0ZbtmyJ7rzzzqh///7Rvn37oiiKoq985SvRjTfe+O7n79mzJ+rXr1901113RVu2bIkeffTRKDMzM3rqqadC/RLUx7r7zDzxxBNRRkZG9OCDD0ZVVVXv/qitrQ31S1Af6+4z8/u8qpd6uvvM1NfXR0VFRdHHP/7xaPPmzdHLL78cTZo0Kbr11ltD/RLUx7r7zHz/+9+PMjIyooceeijavXt39Nprr0Xz58+PFixYEOqXoD5WX18frVu3Llq3bl0ERPfee2+0bt26qKysLIqixH0N7HDqZQ8++GBUUlISZWVlRRdddFH08ssvv/uf3XzzzdFll112xue/9NJL0dy5c6OsrKxo7Nix0cMPP9zHxQqtO8/MZZddFgHv+XHzzTf3fbiC6e6/Z36Xwyk1dfeZ2bp1a3TllVdGubm5UVFRUXT33XdHjY2NfVytkLr7zNx///1RaWlplJubG40cOTL68z//86iysrKPqxXKiy+++L6vTxL1NXAsinzPVJIkSZLej1/jJEmSJEldcDhJkiRJUhccTpIkSZLUBYeTJEmSJHXB4SRJkiRJXXA4SZIkSVIXHE6SJEmS1AWHkyRJkiR1weEkSZIkSV1wOEmSJElSFxxOkiRJktQFh5MkKWUcOXKEESNG8I//+I/vfuyNN94gKyuL559/PmCZJCnexaIoikJHSJLUV1asWMFHP/pRVq1axdSpU5k7dy7Lli1j+fLlodMkSXHM4SRJSjm33347L7zwAhdffDEbNmzgrbfeIicnJ3SWJCmOOZwkSSnn5MmTzJgxg4qKCtasWcOsWbNCJ0mS4pxf4yRJSjl79uzhwIEDdHR0UFZWFjpHkpQAfMdJkpRSWlpaWLBgAXPmzGHq1Knce++9bNy4kcLCwtBpkqQ45nCSJKWUL3/5yzz11FNs2LCBAQMGcPnll5OXl8czzzwTOk2SFMf8rXqSpJTx0ksvsXz5ch5//HHy8/NJS0vj8ccf57XXXuPhhx8OnSdJimO+4yRJkiRJXfAdJ0mSJEnqgsNJkiRJkrrgcJIkSZKkLjicJEmSJKkLDidJkiRJ6oLDSZIkSZK64HCSJEmSpC44nCRJkiSpCw4nSZIkSeqCw0mSJEmSuuBwkiRJkqQu/H+t1JkdxcjH1gAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import autograd.numpy as np\n", "from autograd import grad, elementwise_grad\n", @@ -2173,9 +2107,7 @@ { "cell_type": "markdown", "id": "f0abbbd3", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Comparing with a numerical scheme\n", "\n", @@ -2195,9 +2127,7 @@ { "cell_type": "markdown", "id": "5972f4a4", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -2212,9 +2142,7 @@ { "cell_type": "markdown", "id": "66486037", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "If $x_i = i \\Delta x = x_{i-1} + \\Delta x$ and $g_i = g(x_i)$ for $i = 1,\\dots N_x - 2$ with $N_x$ being the number of values for $x$, ([15](#approx)) becomes" ] @@ -2222,9 +2150,7 @@ { "cell_type": "markdown", "id": "b95db726", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{aligned}\n", @@ -2237,9 +2163,7 @@ { "cell_type": "markdown", "id": "56dfacfe", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "Since we know from our problem that" ] @@ -2247,9 +2171,7 @@ { "cell_type": "markdown", "id": "d6ea9a88", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{aligned}\n", @@ -2262,9 +2184,7 @@ { "cell_type": "markdown", "id": "0cff1a42", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "along with the conditions $g(0) = g(1) = 0$,\n", "the following scheme can be used to find an approximate solution for $g(x)$ numerically:" @@ -2273,9 +2193,7 @@ { "cell_type": "markdown", "id": "44cd6a7b", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -2293,9 +2211,7 @@ { "cell_type": "markdown", "id": "c85a0279", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "for $i = 1, \\dots, N_x - 2$ where $g_0 = g_{N_x - 1} = 0$ and $f(x_i) = (3x_i + x_i^2)\\exp(x_i)$, which is given for our specific problem.\n", "\n", @@ -2305,9 +2221,7 @@ { "cell_type": "markdown", "id": "27958d4b", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{aligned}\n", @@ -2342,9 +2256,7 @@ { "cell_type": "markdown", "id": "ad6836c2", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "which makes it possible to solve for the vector $\\boldsymbol{g}$." ] @@ -2352,9 +2264,7 @@ { "cell_type": "markdown", "id": "0260be46", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Setting up the code\n", "\n", @@ -2365,11 +2275,47 @@ "cell_type": "code", "execution_count": 6, "id": "01fe4413", - "metadata": { - "collapsed": false, - "editable": true - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3208: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n", + " return asarray(a).size\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial cost: 457.256\n", + "Final cost: 0.00310113\n", + "The max absolute difference between the analytical solution and DNN Autograd: 0.000464088\n", + "The max absolute difference between the analytical solution and numerical scheme: 0.00266858\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import autograd.numpy as np\n", "from autograd import grad, elementwise_grad\n", @@ -2566,9 +2512,7 @@ { "cell_type": "markdown", "id": "87ecdb6d", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Partial Differential Equations\n", "\n", @@ -2583,9 +2527,7 @@ { "cell_type": "markdown", "id": "6420f00b", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -2600,9 +2542,7 @@ { "cell_type": "markdown", "id": "ea80f33b", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where $f$ is an expression involving all kinds of possible mixed derivatives of $g(x_1,\\dots,x_N)$ up to an order $n$. In order for the solution to be unique, some additional conditions must also be given." ] @@ -2610,9 +2550,7 @@ { "cell_type": "markdown", "id": "2701592c", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Type of problem\n", "\n", @@ -2625,9 +2563,7 @@ { "cell_type": "markdown", "id": "d5e81572", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{align*}\n", @@ -2639,9 +2575,7 @@ { "cell_type": "markdown", "id": "5fad32fb", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where $h_1(x_1,\\dots,x_N)$ is a function that ensures $g_t(x_1,\\dots,x_N)$ satisfies some given conditions.\n", "The neural network $N(x_1,\\dots,x_N,P)$ has weights and biases described by $P$ and $h_2(x_1,\\dots,x_N,N(x_1,\\dots,x_N,P))$ is an expression using the output from the neural network in some way.\n", @@ -2652,9 +2586,7 @@ { "cell_type": "markdown", "id": "88dfb36f", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Network requirements\n", "\n", @@ -2672,9 +2604,7 @@ { "cell_type": "markdown", "id": "ebaba66a", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "C\\left(x_1, \\dots, x_N, P\\right) = \\left( f\\left(x_1, \\, \\dots \\, , x_N, \\frac{\\partial g(x_1,\\dots,x_N) }{\\partial x_1}, \\dots , \\frac{\\partial g(x_1,\\dots,x_N) }{\\partial x_N}, \\frac{\\partial g(x_1,\\dots,x_N) }{\\partial x_1\\partial x_2}, \\, \\dots \\, , \\frac{\\partial^n g(x_1,\\dots,x_N) }{\\partial x_N^n} \\right) \\right)^2\n", @@ -2684,9 +2614,7 @@ { "cell_type": "markdown", "id": "49e28269", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## More details\n", "\n", @@ -2696,9 +2624,7 @@ { "cell_type": "markdown", "id": "e76c1ce2", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "C\\left(\\boldsymbol{x}, P\\right) = f\\left( \\left( \\boldsymbol{x}, \\frac{\\partial g(\\boldsymbol{x}) }{\\partial x_1}, \\dots , \\frac{\\partial g(\\boldsymbol{x}) }{\\partial x_N}, \\frac{\\partial g(\\boldsymbol{x}) }{\\partial x_1\\partial x_2}, \\, \\dots \\, , \\frac{\\partial^n g(\\boldsymbol{x}) }{\\partial x_N^n} \\right) \\right)^2\n", @@ -2708,9 +2634,7 @@ { "cell_type": "markdown", "id": "1549d32f", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "If we also have $M$ different sets of values for $x_1, \\dots, x_N$, that is $\\boldsymbol{x}_i = \\big(x_1^{(i)}, \\dots, x_N^{(i)}\\big)$ for $i = 1,\\dots,M$ being the rows in matrix $X$, the cost function can be generalized into" ] @@ -2718,9 +2642,7 @@ { "cell_type": "markdown", "id": "da6fb7be", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "C\\left(X, P \\right) = \\sum_{i=1}^M f\\left( \\left( \\boldsymbol{x}_i, \\frac{\\partial g(\\boldsymbol{x}_i) }{\\partial x_1}, \\dots , \\frac{\\partial g(\\boldsymbol{x}_i) }{\\partial x_N}, \\frac{\\partial g(\\boldsymbol{x}_i) }{\\partial x_1\\partial x_2}, \\, \\dots \\, , \\frac{\\partial^n g(\\boldsymbol{x}_i) }{\\partial x_N^n} \\right) \\right)^2.\n", @@ -2730,9 +2652,7 @@ { "cell_type": "markdown", "id": "b1e74a19", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Example: The diffusion equation\n", "\n", @@ -2742,9 +2662,7 @@ { "cell_type": "markdown", "id": "48df8601", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\frac{\\partial g(x,t)}{\\partial t} = \\frac{\\partial^2 g(x,t)}{\\partial x^2}\n", @@ -2754,9 +2672,7 @@ { "cell_type": "markdown", "id": "e23e991d", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where a possible choice of conditions are" ] @@ -2764,9 +2680,7 @@ { "cell_type": "markdown", "id": "eef4c245", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{align*}\n", @@ -2780,9 +2694,7 @@ { "cell_type": "markdown", "id": "00e49269", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "with $u(x)$ being some given function." ] @@ -2790,9 +2702,7 @@ { "cell_type": "markdown", "id": "4c8c0864", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Defining the problem\n", "\n", @@ -2802,9 +2712,7 @@ { "cell_type": "markdown", "id": "cfd29570", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -2819,9 +2727,7 @@ { "cell_type": "markdown", "id": "85cc21dd", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "and" ] @@ -2829,9 +2735,7 @@ { "cell_type": "markdown", "id": "52446c6c", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{align*}\n", @@ -2845,9 +2749,7 @@ { "cell_type": "markdown", "id": "7a6a3a79", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "with $u(x) = \\sin(\\pi x)$.\n", "\n", @@ -2859,9 +2761,7 @@ { "cell_type": "markdown", "id": "51920eea", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Setting up the network using Autograd\n", "\n", @@ -2878,10 +2778,7 @@ "cell_type": "code", "execution_count": 7, "id": "e79edf06", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "def sigmoid(z):\n", @@ -2933,9 +2830,7 @@ { "cell_type": "markdown", "id": "92235aff", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Setting up the network using Autograd; The trial solution\n", "\n", @@ -2963,9 +2858,7 @@ { "cell_type": "markdown", "id": "ed32d0c3", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Why the jacobian?\n", "\n", @@ -2992,10 +2885,7 @@ "cell_type": "code", "execution_count": 8, "id": "df3d43b2", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# Set up the trial function:\n", @@ -3039,9 +2929,7 @@ { "cell_type": "markdown", "id": "dfd5e194", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Setting up the network using Autograd; The full program\n", "\n", @@ -3065,10 +2953,7 @@ "cell_type": "code", "execution_count": 9, "id": "53d722cd", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import autograd.numpy as np\n", @@ -3299,9 +3184,7 @@ { "cell_type": "markdown", "id": "6e6643b1", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Example: Solving the wave equation with Neural Networks\n", "\n", @@ -3311,9 +3194,7 @@ { "cell_type": "markdown", "id": "16cd3520", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\frac{\\partial^2 g(x,t)}{\\partial t^2} = c^2\\frac{\\partial^2 g(x,t)}{\\partial x^2}\n", @@ -3323,9 +3204,7 @@ { "cell_type": "markdown", "id": "754d5669", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "with $c$ being the specified wave speed.\n", "\n", @@ -3335,9 +3214,7 @@ { "cell_type": "markdown", "id": "85b2c747", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{align*}\n", @@ -3352,9 +3229,7 @@ { "cell_type": "markdown", "id": "bcbf83a9", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where $\\frac{\\partial g(x,t)}{\\partial t} \\Big |_{t = 0}$ means the derivative of $g(x,t)$ with respect to $t$ is evaluated at $t = 0$, and $u(x)$ and $v(x)$ being given functions." ] @@ -3362,9 +3237,7 @@ { "cell_type": "markdown", "id": "1659ec1a", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The problem to solve for\n", "\n", @@ -3374,9 +3247,7 @@ { "cell_type": "markdown", "id": "7c0fca91", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -3391,9 +3262,7 @@ { "cell_type": "markdown", "id": "6e6aba88", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where $c$ is the given wave speed.\n", "The chosen conditions for this equation are" @@ -3402,9 +3271,7 @@ { "cell_type": "markdown", "id": "4e881998", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -3422,9 +3289,7 @@ { "cell_type": "markdown", "id": "1cafacfe", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "In this example, let $c = 1$ and $u(x) = \\sin(\\pi x)$ and $v(x) = -\\pi\\sin(\\pi x)$." ] @@ -3432,9 +3297,7 @@ { "cell_type": "markdown", "id": "cb906aba", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The trial solution\n", "Setting up the network is done in similar matter as for the example of solving the diffusion equation.\n", @@ -3458,9 +3321,7 @@ { "cell_type": "markdown", "id": "32ccd5c7", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The analytical solution\n", "\n", @@ -3474,9 +3335,7 @@ { "cell_type": "markdown", "id": "eae08701", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Solving the wave equation - the full program using Autograd" ] @@ -3485,10 +3344,7 @@ "cell_type": "code", "execution_count": 10, "id": "4c31a212", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import autograd.numpy as np\n", @@ -3716,9 +3572,7 @@ { "cell_type": "markdown", "id": "01330e68", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Resources on differential equations and deep learning\n", "\n", @@ -3734,9 +3588,7 @@ { "cell_type": "markdown", "id": "4f7a9803", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Convolutional Neural Networks (recognizing images)\n", "\n", @@ -3761,9 +3613,7 @@ { "cell_type": "markdown", "id": "828e6019", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## What is the Difference\n", "\n", @@ -3783,9 +3633,7 @@ { "cell_type": "markdown", "id": "92fd4d62", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Neural Networks vs CNNs\n", "\n", @@ -3801,9 +3649,7 @@ { "cell_type": "markdown", "id": "ac6ce257", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Why CNNS for images, sound files, medical images from CT scans etc?\n", "\n", @@ -3831,9 +3677,7 @@ { "cell_type": "markdown", "id": "ff9ebb73", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Regular NNs don’t scale well to full images\n", "\n", @@ -3861,9 +3705,7 @@ { "cell_type": "markdown", "id": "db515315", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## 3D volumes of neurons\n", "\n", @@ -3901,9 +3743,7 @@ { "cell_type": "markdown", "id": "cdd402e7", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Layers used to build CNNs\n", "\n", @@ -3930,9 +3770,7 @@ { "cell_type": "markdown", "id": "49acc82f", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Transforming images\n", "\n", @@ -3952,9 +3790,7 @@ { "cell_type": "markdown", "id": "56d06b86", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## CNNs in brief\n", "\n", @@ -3981,9 +3817,7 @@ { "cell_type": "markdown", "id": "94c5721c", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Key Idea\n", "\n", @@ -3998,9 +3832,7 @@ { "cell_type": "markdown", "id": "44c8b5a8", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Mathematics of CNNs\n", "\n", @@ -4018,9 +3850,7 @@ { "cell_type": "markdown", "id": "3b32c3b2", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "y(t) = \\int x(a) w(t-a) da,\n", @@ -4030,9 +3860,7 @@ { "cell_type": "markdown", "id": "f9534bc9", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where $x(a)$ represents a so-called input and $w(t-a)$ is normally called the weight function or kernel.\n", "\n", @@ -4042,9 +3870,7 @@ { "cell_type": "markdown", "id": "0165e5ff", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "y(t) = \\left(x * w\\right)(t).\n", @@ -4054,9 +3880,7 @@ { "cell_type": "markdown", "id": "81d5bee2", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "The discretized version reads" ] @@ -4064,9 +3888,7 @@ { "cell_type": "markdown", "id": "596229fb", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "y(t) = \\sum_{a=-\\infty}^{a=\\infty}x(a)w(t-a).\n", @@ -4076,9 +3898,7 @@ { "cell_type": "markdown", "id": "82a4b22e", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "Computing the inverse of the above convolution operations is known as deconvolution.\n", "\n", @@ -4088,9 +3908,7 @@ { "cell_type": "markdown", "id": "b3429713", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Convolution Examples: Polynomial multiplication\n", "\n", @@ -4103,9 +3921,7 @@ { "cell_type": "markdown", "id": "d5817d7d", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "p(t) = \\alpha_0+\\alpha_1 t+\\alpha_2 t^2,\n", @@ -4115,9 +3931,7 @@ { "cell_type": "markdown", "id": "49b85586", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "and" ] @@ -4125,9 +3939,7 @@ { "cell_type": "markdown", "id": "97f1c627", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "s(t) = \\beta_0+\\beta_1 t+\\beta_2 t^2+\\beta_3 t^3.\n", @@ -4137,9 +3949,7 @@ { "cell_type": "markdown", "id": "47abe7ce", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "The polynomial multiplication gives us a new polynomial of degree $5$" ] @@ -4147,9 +3957,7 @@ { "cell_type": "markdown", "id": "f4baf465", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "z(t) = \\delta_0+\\delta_1 t+\\delta_2 t^2+\\delta_3 t^3+\\delta_4 t^4+\\delta_5 t^5.\n", @@ -4159,9 +3967,7 @@ { "cell_type": "markdown", "id": "bf512f61", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Efficient Polynomial Multiplication\n", "\n", @@ -4172,9 +3978,7 @@ { "cell_type": "markdown", "id": "8ee1612b", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{split}\n", @@ -4191,9 +3995,7 @@ { "cell_type": "markdown", "id": "527003b3", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "We note that $\\alpha_i=0$ except for $i\\in \\left\\{0,1,2\\right\\}$ and $\\beta_i=0$ except for $i\\in\\left\\{0,1,2,3\\right\\}$.\n", "\n", @@ -4203,9 +4005,7 @@ { "cell_type": "markdown", "id": "ee0d7901", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\delta_j = \\sum_{i=-\\infty}^{i=\\infty}\\alpha_i\\beta_{j-i}=(\\alpha * \\beta)_j,\n", @@ -4215,9 +4015,7 @@ { "cell_type": "markdown", "id": "8eb6fdfb", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "or as a double sum with restriction $l=i+j$" ] @@ -4225,9 +4023,7 @@ { "cell_type": "markdown", "id": "96c164e0", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\delta_l = \\sum_{ij}\\alpha_i\\beta_{j}.\n", @@ -4237,9 +4033,7 @@ { "cell_type": "markdown", "id": "412fb91f", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "Do you see a potential drawback with these equations?" ] @@ -4247,9 +4041,7 @@ { "cell_type": "markdown", "id": "70a30687", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## A more efficient way of coding the above Convolution\n", "\n", @@ -4261,9 +4053,7 @@ { "cell_type": "markdown", "id": "0fced6a8", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\boldsymbol{\\delta}=\\begin{bmatrix}\\alpha_0 & 0 & 0 & 0 \\\\\n", @@ -4279,9 +4069,7 @@ { "cell_type": "markdown", "id": "7ce7e477", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "The process is commutative and we can easily see that we can rewrite the multiplication in terms of a matrix holding $\\beta$ and a vector holding $\\alpha$.\n", "In this case we have" @@ -4290,9 +4078,7 @@ { "cell_type": "markdown", "id": "4c6024fb", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\boldsymbol{\\delta}=\\begin{bmatrix}\\beta_0 & 0 & 0 \\\\\n", @@ -4308,9 +4094,7 @@ { "cell_type": "markdown", "id": "25715e96", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "Note that the use of these matrices is for mathematical purposes only and not implementation purposes.\n", "When implementing the above equation we do not encode (and allocate memory) the matrices explicitely.\n", @@ -4322,9 +4106,7 @@ { "cell_type": "markdown", "id": "48e86964", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Convolution Examples: Principle of Superposition and Periodic Forces (Fourier Transforms)\n", "\n", @@ -4334,9 +4116,7 @@ { "cell_type": "markdown", "id": "dd8b183e", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "m\\frac{d^2x}{dt^2}+\\eta\\frac{dx}{dt}+x(t)=F(t),\n", @@ -4346,9 +4126,7 @@ { "cell_type": "markdown", "id": "acd194a6", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where $F(t)$ is an applied external force acting on the system (often called a driving force), one can use the theory of Fourier transformations to find the solutions of this type of equations.\n", "\n", @@ -4360,9 +4138,7 @@ { "cell_type": "markdown", "id": "9cdc871b", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -4378,9 +4154,7 @@ { "cell_type": "markdown", "id": "963c1109", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Principle of Superposition\n", "\n", @@ -4399,9 +4173,7 @@ { "cell_type": "markdown", "id": "a21aea89", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{eqnarray}\n", @@ -4413,9 +4185,7 @@ { "cell_type": "markdown", "id": "2da231a6", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "One example of a non-sinusoidal periodic force is a square wave. Many\n", "components in electric circuits are non-linear, e.g. diodes, which\n", @@ -4426,9 +4196,7 @@ { "cell_type": "markdown", "id": "401164a3", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Simple Code Example\n", "\n", @@ -4439,10 +4207,7 @@ "cell_type": "code", "execution_count": 11, "id": "e3701802", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -4467,9 +4232,7 @@ { "cell_type": "markdown", "id": "586d9ba6", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "For the sinusoidal example the\n", "period is $\\tau=2\\pi/\\omega$. However, higher harmonics can also\n", @@ -4481,9 +4244,7 @@ { "cell_type": "markdown", "id": "6f38255c", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -4499,9 +4260,7 @@ { "cell_type": "markdown", "id": "ced5a425", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Wrapping up Fourier transforms\n", "\n", @@ -4514,9 +4273,7 @@ { "cell_type": "markdown", "id": "d65e6b1a", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -4532,9 +4289,7 @@ { "cell_type": "markdown", "id": "4780a0b5", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "The solutions for $x(t)$ then come from replacing $\\omega$ with\n", "$n\\omega$ for each term in the particular solution," @@ -4543,9 +4298,7 @@ { "cell_type": "markdown", "id": "fdcb608d", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{eqnarray}\n", @@ -4563,9 +4316,7 @@ { "cell_type": "markdown", "id": "5edd527a", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Finding the Coefficients\n", "\n", @@ -4582,9 +4333,7 @@ { "cell_type": "markdown", "id": "2a07ac4a", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -4602,9 +4351,7 @@ { "cell_type": "markdown", "id": "08ddec1e", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "To check the consistency of these expressions and to verify\n", "Eq. ([24](#eq:fourierdef2)), one can insert the expansion of $F(t)$ in\n", @@ -4615,9 +4362,7 @@ { "cell_type": "markdown", "id": "ebb221b1", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{eqnarray}\n", @@ -4631,9 +4376,7 @@ { "cell_type": "markdown", "id": "fb8cf771", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "Immediately, one can throw away all the terms with $g_m$ because they\n", "convolute an even and an odd function. The term with $f_0/2$\n", @@ -4648,9 +4391,7 @@ { "cell_type": "markdown", "id": "2379d8ce", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -4666,9 +4407,7 @@ { "cell_type": "markdown", "id": "36efca82", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "and" ] @@ -4676,9 +4415,7 @@ { "cell_type": "markdown", "id": "4b7c953d", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{eqnarray}\n", @@ -4692,9 +4429,7 @@ { "cell_type": "markdown", "id": "882e7174", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "The same method can be used to check for the consistency of $g_n$." ] @@ -4702,9 +4437,7 @@ { "cell_type": "markdown", "id": "9a1bfdd0", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Final words on Fourier Transforms\n", "\n", @@ -4721,10 +4454,7 @@ "cell_type": "code", "execution_count": 12, "id": "37f3dc9f", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -4761,9 +4491,7 @@ { "cell_type": "markdown", "id": "d6d7dbaf", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Two-dimensional Objects\n", "\n", @@ -4775,9 +4503,7 @@ { "cell_type": "markdown", "id": "b75ff7c6", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "S_(i,j)=(I * K)(i,j) = \\sum_m\\sum_n I(m,n)K(i-m,j-n).\n", @@ -4787,9 +4513,7 @@ { "cell_type": "markdown", "id": "ab3c1523", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "Convolution is a commutatitave process, which means we can rewrite this equation as" ] @@ -4797,9 +4521,7 @@ { "cell_type": "markdown", "id": "29af51e8", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "S_(i,j)=(I * K)(i,j) = \\sum_m\\sum_n I(i-m,j-n)K(m,n).\n", @@ -4809,9 +4531,7 @@ { "cell_type": "markdown", "id": "87064aee", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "Normally the latter is more straightforward to implement in a machine elarning library since there is less variation in the range of values of $m$ and $n$." ] @@ -4819,9 +4539,7 @@ { "cell_type": "markdown", "id": "794e45d6", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Cross-Correlation\n", "\n", @@ -4831,9 +4549,7 @@ { "cell_type": "markdown", "id": "fa43e53c", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "S_(i,j)=(I * K)(i,j) = \\sum_m\\sum_n I(i+m,j-+)K(m,n).\n", @@ -4843,9 +4559,7 @@ { "cell_type": "markdown", "id": "5c05185c", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## More on Dimensionalities\n", "\n", @@ -4870,9 +4584,7 @@ { "cell_type": "markdown", "id": "c4abb269", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\mathrm{NumberParameters}=10^{10}+10^4+10^4+1 \\approx 10^{10},\n", @@ -4882,9 +4594,7 @@ { "cell_type": "markdown", "id": "89ce0cd0", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "that is ten billion parameters to determine." ] @@ -4892,9 +4602,7 @@ { "cell_type": "markdown", "id": "b6093d7d", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Further Dimensionality Remarks\n", "\n", @@ -4918,9 +4626,7 @@ { "cell_type": "markdown", "id": "1c2f9d07", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## CNNs in more detail, Lecture from IN5400\n", "\n", @@ -4930,9 +4636,7 @@ { "cell_type": "markdown", "id": "d25141a7", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## CNNs in more detail, building convolutional neural networks in Tensorflow and Keras\n", "\n", @@ -4949,9 +4653,7 @@ { "cell_type": "markdown", "id": "97f37684", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Setting it up\n", "\n", @@ -4962,9 +4664,7 @@ { "cell_type": "markdown", "id": "06ca30fb", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "(n_{inputs},\\, n_{pixels, width},\\, n_{pixels, height},\\, depth) .\n", @@ -4974,9 +4674,7 @@ { "cell_type": "markdown", "id": "945d9820", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The MNIST dataset again\n", "\n", @@ -4995,9 +4693,7 @@ { "cell_type": "markdown", "id": "78260f1c", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Strong correlations\n", "\n", @@ -5016,9 +4712,7 @@ { "cell_type": "markdown", "id": "aed50975", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Layers of a CNN\n", "The layers of a convolutional neural network arrange neurons in 3D: width, height and depth. \n", @@ -5041,9 +4735,7 @@ { "cell_type": "markdown", "id": "dbb1f3d9", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Systematic reduction\n", "\n", @@ -5060,9 +4752,7 @@ { "cell_type": "markdown", "id": "21f7a203", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Prerequisites: Collect and pre-process data" ] @@ -5071,10 +4761,7 @@ "cell_type": "code", "execution_count": 13, "id": "599d4c52", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# import necessary packages\n", @@ -5122,9 +4809,7 @@ { "cell_type": "markdown", "id": "a762279b", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Importing Keras and Tensorflow" ] @@ -5133,10 +4818,7 @@ "cell_type": "code", "execution_count": 14, "id": "3d283ac2", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "from tensorflow.keras import datasets, layers, models\n", @@ -5166,9 +4848,7 @@ { "cell_type": "markdown", "id": "f554cf0e", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Running with Keras" ] @@ -5177,10 +4857,7 @@ "cell_type": "code", "execution_count": 15, "id": "395b2e91", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "def create_convolutional_neural_network_keras(input_shape, receptive_field,\n", @@ -5214,9 +4891,7 @@ { "cell_type": "markdown", "id": "8688bef2", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Final part" ] @@ -5225,10 +4900,7 @@ "cell_type": "code", "execution_count": 16, "id": "ece60043", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "CNN_keras = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)\n", @@ -5252,9 +4924,7 @@ { "cell_type": "markdown", "id": "61a2ff97", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Final visualization" ] @@ -5263,10 +4933,7 @@ "cell_type": "code", "execution_count": 17, "id": "56482d32", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# visual representation of grid search\n", @@ -5304,9 +4971,7 @@ { "cell_type": "markdown", "id": "6bf45693", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The CIFAR01 data set\n", "\n", @@ -5320,10 +4985,7 @@ "cell_type": "code", "execution_count": 18, "id": "cd9c8313", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import tensorflow as tf\n", @@ -5341,9 +5003,7 @@ { "cell_type": "markdown", "id": "ae2cd771", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Verifying the data set\n", "\n", @@ -5354,10 +5014,7 @@ "cell_type": "code", "execution_count": 19, "id": "fd0eb790", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n", @@ -5379,9 +5036,7 @@ { "cell_type": "markdown", "id": "622519b8", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Set up the model\n", "\n", @@ -5394,10 +5049,7 @@ "cell_type": "code", "execution_count": 20, "id": "6d573492", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "model = models.Sequential()\n", @@ -5415,9 +5067,7 @@ { "cell_type": "markdown", "id": "7d8938d9", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "You can see that the output of every Conv2D and MaxPooling2D layer is a 3D tensor of shape (height, width, channels). The width and height dimensions tend to shrink as you go deeper in the network. The number of output channels for each Conv2D layer is controlled by the first argument (e.g., 32 or 64). Typically, as the width and height shrink, you can afford (computationally) to add more output channels in each Conv2D layer." ] @@ -5425,9 +5075,7 @@ { "cell_type": "markdown", "id": "7e377497", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Add Dense layers on top\n", "\n", @@ -5444,10 +5092,7 @@ "cell_type": "code", "execution_count": 21, "id": "addb2ceb", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "model.add(layers.Flatten())\n", @@ -5461,9 +5106,7 @@ { "cell_type": "markdown", "id": "938b7da0", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "As you can see, our (4, 4, 64) outputs were flattened into vectors of shape (1024) before going through two Dense layers." ] @@ -5471,9 +5114,7 @@ { "cell_type": "markdown", "id": "2c3de061", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Compile and train the model" ] @@ -5482,10 +5123,7 @@ "cell_type": "code", "execution_count": 22, "id": "f01dce7e", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "model.compile(optimizer='adam',\n", @@ -5499,9 +5137,7 @@ { "cell_type": "markdown", "id": "55c40e2c", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Finally, evaluate the model" ] @@ -5510,10 +5146,7 @@ "cell_type": "code", "execution_count": 23, "id": "418f2f9d", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "plt.plot(history.history['accuracy'], label='accuracy')\n", @@ -5529,7 +5162,25 @@ ] } ], - "metadata": {}, + "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" + } + }, "nbformat": 4, "nbformat_minor": 5 } diff --git a/doc/src/week41/programs/testtf.py b/doc/src/week41/programs/testtf.py new file mode 100644 index 000000000..e47235c8a --- /dev/null +++ b/doc/src/week41/programs/testtf.py @@ -0,0 +1,49 @@ +import tensorflow as tf +import tensorflow_datasets as tfds +print("TensorFlow version:", tf.__version__) +print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU'))) +tf.config.list_physical_devices('GPU') +(ds_train, ds_test), ds_info = tfds.load( + 'mnist', + split=['train', 'test'], + shuffle_files=True, + as_supervised=True, + with_info=True, +) +def normalize_img(image, label): + """Normalizes images: `uint8` -> `float32`.""" + return tf.cast(image, tf.float32) / 255., label +batch_size = 128 +ds_train = ds_train.map( + normalize_img, num_parallel_calls=tf.data.experimental.AUTOTUNE) +ds_train = ds_train.cache() +ds_train = ds_train.shuffle(ds_info.splits['train'].num_examples) +ds_train = ds_train.batch(batch_size) +ds_train = ds_train.prefetch(tf.data.experimental.AUTOTUNE) +ds_test = ds_test.map( + normalize_img, num_parallel_calls=tf.data.experimental.AUTOTUNE) +ds_test = ds_test.batch(batch_size) +ds_test = ds_test.cache() +ds_test = ds_test.prefetch(tf.data.experimental.AUTOTUNE) +model = tf.keras.models.Sequential([ + tf.keras.layers.Conv2D(32, kernel_size=(3, 3), + activation='relu'), + tf.keras.layers.Conv2D(64, kernel_size=(3, 3), + activation='relu'), + tf.keras.layers.MaxPooling2D(pool_size=(2, 2)), +# tf.keras.layers.Dropout(0.25), + tf.keras.layers.Flatten(), + tf.keras.layers.Dense(128, activation='relu'), +# tf.keras.layers.Dropout(0.5), + tf.keras.layers.Dense(10, activation='softmax') +]) +model.compile( + loss='sparse_categorical_crossentropy', + optimizer=tf.keras.optimizers.Adam(0.001), + metrics=['accuracy'], +) +model.fit( + ds_train, + epochs=12, + validation_data=ds_test, +)