diff --git a/doc/pub/week41/html/._week41-bs000.html b/doc/pub/week41/html/._week41-bs000.html
index dd11e88c9..54e48bba2 100644
--- a/doc/pub/week41/html/._week41-bs000.html
+++ b/doc/pub/week41/html/._week41-bs000.html
@@ -274,7 +274,7 @@ MathJax.Hub.Config({
[2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
-
Oct 9, 2020 Oct 10, 2020
diff --git a/doc/pub/week41/html/._week41-bs001.html b/doc/pub/week41/html/._week41-bs001.html
index ec264532d..c8ca5ac02 100644
--- a/doc/pub/week41/html/._week41-bs001.html
+++ b/doc/pub/week41/html/._week41-bs001.html
@@ -259,7 +259,7 @@ MathJax.Hub.Config({
Thursday: Building our own Feed-forward Neural Network. Video of Lecture
- Friday: Playing around with our own Feed-forward Neural Network and introduction to TensorFlow. Start convolutional Neural Networks (CNN).
+ Friday: Playing around with our own Feed-forward Neural Network and introduction to TensorFlow. Start convolutional Neural Networks (CNN). Video of Lecture
Reading suggestions for both days: Video of Lecture
-
Friday: Playing around with our own Feed-forward Neural Network and introduction to TensorFlow. Start convolutional Neural Networks (CNN).
+
Friday: Playing around with our own Feed-forward Neural Network and introduction to TensorFlow. Start convolutional Neural Networks (CNN). Video of Lecture
diff --git a/doc/pub/week41/html/week41-solarized.html b/doc/pub/week41/html/week41-solarized.html
index f0f1314e6..d50fe5c91 100644
--- a/doc/pub/week41/html/week41-solarized.html
+++ b/doc/pub/week41/html/week41-solarized.html
@@ -202,7 +202,7 @@ MathJax.Hub.Config({
[2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
-
Oct 9, 2020 Oct 10, 2020
Thursday: Building our own Feed-forward Neural Network. Video of Lecture
- Friday: Playing around with our own Feed-forward Neural Network and introduction to TensorFlow. Start convolutional Neural Networks (CNN).
+ Friday: Playing around with our own Feed-forward Neural Network and introduction to TensorFlow. Start convolutional Neural Networks (CNN). Video of Lecture
Reading suggestions for both days: Video of Lecture
- Friday: Playing around with our own Feed-forward Neural Network and introduction to TensorFlow. Start convolutional Neural Networks (CNN).
+ Friday: Playing around with our own Feed-forward Neural Network and introduction to TensorFlow. Start convolutional Neural Networks (CNN). Video of Lecture
Reading suggestions for both days: \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",
"\n",
- "Date: **Oct 9, 2020**\n",
+ "Date: **Oct 10, 2020**\n",
"\n",
"Copyright 1999-2020, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n",
"\n",
@@ -21,7 +21,7 @@
"\n",
"* Thursday: Building our own Feed-forward Neural Network. [Video of Lecture](https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureOctober8.mp4?vrtx=view-as-webpage)\n",
"\n",
- "* Friday: Playing around with our own Feed-forward Neural Network and introduction to TensorFlow. Start convolutional Neural Networks (CNN).\n",
+ "* Friday: Playing around with our own Feed-forward Neural Network and introduction to TensorFlow. Start convolutional Neural Networks (CNN). [Video of Lecture](https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureOcotber9.mp4?vrtx=view-as-webpage)\n",
"\n",
"Reading suggestions for both days: [Aurelien Geron's chapters 10-11](https://github.com/CompPhysics/MachineLearning/blob/master/doc/T\\\n",
"extbooks/TensorflowML.pdf) and Hastie et al chapter 11.\n",
@@ -491,7 +491,9 @@
{
"cell_type": "code",
"execution_count": 1,
- "metadata": {},
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"%matplotlib inline\n",
@@ -560,7 +562,9 @@
{
"cell_type": "code",
"execution_count": 2,
- "metadata": {},
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
@@ -682,7 +686,9 @@
{
"cell_type": "code",
"execution_count": 3,
- "metadata": {},
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"# building our neural network\n",
@@ -759,7 +765,9 @@
{
"cell_type": "code",
"execution_count": 4,
- "metadata": {},
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"# setup the feed-forward pass, subscript h = hidden layer\n",
@@ -924,7 +932,9 @@
{
"cell_type": "code",
"execution_count": 5,
- "metadata": {},
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"# to categorical turns our integer vector into a onehot representation\n",
@@ -1023,7 +1033,9 @@
{
"cell_type": "code",
"execution_count": 6,
- "metadata": {},
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"class NeuralNetwork:\n",
@@ -1145,7 +1157,9 @@
{
"cell_type": "code",
"execution_count": 7,
- "metadata": {},
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"epochs = 100\n",
@@ -1179,7 +1193,9 @@
{
"cell_type": "code",
"execution_count": 8,
- "metadata": {},
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"eta_vals = np.logspace(-5, 1, 7)\n",
@@ -1214,7 +1230,9 @@
{
"cell_type": "code",
"execution_count": 9,
- "metadata": {},
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"# visual representation of grid search\n",
@@ -1274,7 +1292,9 @@
{
"cell_type": "code",
"execution_count": 10,
- "metadata": {},
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"from sklearn.neural_network import MLPClassifier\n",
@@ -1305,7 +1325,9 @@
{
"cell_type": "code",
"execution_count": 11,
- "metadata": {},
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"# optional\n",
@@ -1388,7 +1410,9 @@
{
"cell_type": "code",
"execution_count": 12,
- "metadata": {},
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"pip3 install tensorflow"
@@ -1405,7 +1429,9 @@
{
"cell_type": "code",
"execution_count": 13,
- "metadata": {},
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"conda create -n tf tensorflow\n",
@@ -1422,7 +1448,9 @@
{
"cell_type": "code",
"execution_count": 14,
- "metadata": {},
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"conda create -n tf-gpu tensorflow-gpu\n",
@@ -1443,7 +1471,9 @@
{
"cell_type": "code",
"execution_count": 15,
- "metadata": {},
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"conda install keras"
@@ -1464,31 +1494,11 @@
},
{
"cell_type": "code",
- "execution_count": 2,
- "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": "iVBORw0KGgoAAAANSUhEUgAAAqwAAACRCAYAAAAGuepqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAALD0lEQVR4nO3dX2jl6VkH8OdpZ6HWP3O6KKJ2d6ItFbYXmxtRsTJnQBAESWBZEaw7GWnBK2eW6o0gyUi9EC82I17o1Wa6ghUtJKCLIDqJ1lW0sBnozYKUtGux2MqeuKsiWl8vTgbDMH+yz++cnDcznw8EMnvy/J73l/Oc93zPLyfZbK0FAAD06j2LXgAAADyIwAoAQNcEVgAAuiawAgDQNYEVAICuCawAAHTtkQ+smbmbmZ847VrOFnPCSZkVTsqscBLm5GTOTGDNzIPM/IlFr+N+curTmfnVzDw8GqKPLnpdj5szMCe/m5nvHPv4r8x8e9HrehyZFU7qDMzKz2bmG0fPPf+SmTcz8zsWva7HjTmZrzMTWM+A5yPiFyLixyPiyYj424h4ZaErojuttV9srX3bnY+I+IOI+KNFr4v+mBXehb+JiB9rrZ2PiB+IiHMR8enFLokOnek5OfOBNTM/kJl/kplfz8y3jj7/4F1f9qHM/PujVxU7mfnksfofyczXMnOSmbczc1xcyvdHxOdba19qrX0zIn4/Ip4pHosZ62hOjq/pWyPiuYi4OfRYzI5Z4aR6mZXW2puttW8c+0/fjIgPV47F7JmT2TjzgTWm5/ByRFyIiKcj4j8j4nfu+poXYnr183sj4n8i4rcjIjLz+yLiT2P6CuPJiPjliPhcZn7X3U0y8+mjYXn6Puv4bER8ODM/kplPRMTliPizgefG7PQyJ8c9FxFfj4i/qpwQc2NWOKluZiUzP5aZhxHxdkznZXPYqTFD5mQGznxgba39a2vtc621/2itvR0RvxERF+/6sldaa19srf17RPxaRPxMZr43Ij4eEa+21l5trf1va+3PI+ILEfFT9+jzldbaqLX2lfss5Z8j4q8j4o2YDuPzEfHiTE6SwTqak+MuR8RnWmtt0MkxU2aFk+ppVlprnz/6Ue8HI+K3IuJgJifJYOZkNs58YM3M92fm72XmlzPz32J6BWJ0dEff8eaxz78cEU9ExHfG9NXO80evSCaZOYmIj0XE9xSWsh4RPxQRT0XE+yLiekT8ZWa+v3AsZqyjObmznqdiumF9pnoM5sOscFK9zUpERGvtqzH96d5nhxyH2TEns3Fu0QuYgU9FxA9GxA+31r6WmcsR8XpE5LGveerY509HxH9HxDdiOiCvtNY+OYN1PBsRf9ha+6ejf29l5mZM38f6hRkcn2F6mZM7XoiI11prX5rhMZkNs8JJ9TYrd5yLiA/N4bjUmJMZOGtXWJ/IzPcd+zgXEd8e0x/BT47epLx+j7qPZ+YzR1c7fz0i/vjYL0b9dGb+ZGa+9+iY43u8Gfok/iGmr4K+OzPfk5k/H9NXSP9YOlOG6HlO7nghIrYG1DMbZoWT6nZWMvPnjt6/mJl5IaY/cv6L8pkyhDmZk7MWWF+N6Z1+52Mjpm8Y/paYvhL5u7j3Lzq9EtMN/2sx/XH9L0VMf2MuIlYi4ldj+gsNb0bEr8Q9vi9Hd/I7ef83M/9mRNyOiP2ImMT0/avPtdYm7/40GajnOYnM/NGYvn/InyhaPLPCSfU8K89ExGsR8U5M/3TRGxExjytyPJw5mZP0Hn4AAHp21q6wAgDwmBFYAQDomsAKAEDXBFYAALr2sL/Deuq/kbW1tVWq29jYKPccjUalus3N+v/RbDwel2sHyId/Sdmpz8ru7m6prjpjERHb29ulusPDw3LPW7duleoGzti8ZuXU52RnZ6dUd/Xq1Rmv5OGqMx0RsbS0NLN1vAvd7SkHBwflhtU9fcieUt0bzp8/X+65v79fqhs4Y13tKZNJ/Q/4LGJOquut3tcR/e0prrACANA1gRUAgK4JrAAAdE1gBQCgawIrAABdE1gBAOiawAoAQNcEVgAAuiawAgDQNYEVAICuCawAAHRNYAUAoGsCKwAAXTs3j4Pu7u6Wa69cuVKqW1lZKfccjUalutXV1XLPyWRSrmXq2rVrpboh3/u1tbVS3Y0bN8o9q/P5KDk4OCjXDnmcnrbt7e1ybfXx8KhZxPfh5s2b5dpbt26V6obsKZ5/hn0Pqo/TIXtRtefW1la558bGRrl2HlxhBQCgawIrAABdE1gBAOiawAoAQNcEVgAAuiawAgDQNYEVAICuCawAAHRNYAUAoGsCKwAAXRNYAQDomsAKAEDXBFYAALomsAIA0LVsrT3o9gfeeD/Xrl2rrSYiDg4OSnXb29vlnuPxuFQ3Go3KPYesd4Cc47FLszJEdVaG3G97e3ulusuXL5d7TiaTcu0A85qVU5+Tzc3NUt3y8nK556VLl0p1Fy9eLPfc3d0t1w7wSO0pi1B9vtzf3y/3fMRm5bGYk2pOGbKPVffOge47J66wAgDQNYEVAICuCawAAHRNYAUAoGsCKwAAXRNYAQDomsAKAEDXBFYAALomsAIA0DWBFQCArgmsAAB0TWAFAKBrAisAAF07N4+DLi0tlWsPDg5KdRsbG+Wee3t7pbrXX3+93JPhJpNJqa46YxER6+vrpbrRaFTuWV3vkMfho2Rtba1UN2RPqaruRRH19S7iPPl/y8vLpbqtra1yz+reOWQfe5RU9+TV1dXZLuQENjc3T73nvLjCCgBA1wRWAAC6JrACANA1gRUAgK4JrAAAdE1gBQCgawIrAABdE1gBAOiawAoAQNcEVgAAuiawAgDQNYEVAICuCawAAHRNYAUAoGvZWnvQ7Q+8cR6Wl5dLdbdv3y73vHz5cqlua2ur3HNBco7HLs3Kzs5OueHq6mq59ixZX18v1W1sbAxpO69ZKc3J/v5+ueF4PC7VHR4elntWVfeiiPr9vbS0VO4ZHe4pj4sh91t179zc3Cz3jM72lCGq+1F1L4qo70cvv/xyuefa2lq5doD7zokrrAAAdE1gBQCgawIrAABdE1gBAOiawAoAQNcEVgAAuiawAgDQNYEVAICuCawAAHRNYAUAoGsCKwAAXRNYAQDomsAKAEDXsrX2oNsfeOM8LC8vn3bLGI1Gpboha93c3CzXDpBzPHZpVnZ3d8sNt7e3S3X7+/vlngcHB6feszqfA81rVk59Ti5dulSurVpZWSnVVWd6gbrbUx4X4/H41HsOeRxGZ3vKZDKZ9ToeasheXr2/q89ZQ2sHuO+cuMIKAEDXBFYAALomsAIA0DWBFQCArgmsAAB0TWAFAKBrAisAAF0TWAEA6JrACgBA1wRWAAC6JrACANA1gRUAgK4JrAAAdE1gBQCga+cWvYC7jUajUt14PC733NjYKNVV17qonj0acr8dHh6W6ra2tso9V1dXS3WP2v122obMydWrV0t1N27cKPe8cuVKuZbF2dnZKdVduHCh3HN/f/9U6yLqzz+Pkr29vXLt+vp6qe769evlnmtra6W6IXvRZDIp1c3r+c4VVgAAuiawAgDQNYEVAICuCawAAHRNYAUAoGsCKwAAXRNYAQDomsAKAEDXBFYAALomsAIA0DWBFQCArgmsAAB0TWAFAKBr5xa9gLu9+OKLpbrV1dVyz+vXr5fqVlZWyj1Ho1G5lqm33nqrVHd4eFjuuba2Vq7lbHn22WfLtUP2BhbnpZdeKtXt7e2Ve54/f75UN2Qvso9FXLx4sVw7Ho9LddX5ioiYTCaluqtXr5Z79pZTXGEFAKBrAisAAF0TWAEA6JrACgBA1wRWAAC6JrACANA1gRUAgK4JrAAAdE1gBQCgawIrAABdE1gBAOiawAoAQNcEVgAAuiawAgDQtWytLXoNAABwX66wAgDQNYEVAICuCawAAHRNYAUAoGsCKwAAXRNYAQDo2v8BlXpliBTuHMEAAAAASUVORK5CYII=\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "execution_count": 16,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
"source": [
"# import necessary packages\n",
"import numpy as np\n",
@@ -1537,8 +1547,10 @@
},
{
"cell_type": "code",
- "execution_count": 3,
- "metadata": {},
+ "execution_count": 17,
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"from tensorflow.keras.layers import Input\n",
@@ -1562,8 +1574,10 @@
},
{
"cell_type": "code",
- "execution_count": 4,
- "metadata": {},
+ "execution_count": 18,
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"\n",
@@ -1578,7 +1592,7 @@
" model = Sequential()\n",
" model.add(Dense(n_neurons_layer1, activation='sigmoid', kernel_regularizer=regularizers.l2(lmbd)))\n",
" model.add(Dense(n_neurons_layer2, activation='sigmoid', kernel_regularizer=regularizers.l2(lmbd)))\n",
- " model.add(Dense(n_categories, activation='softmax')) # output layer\n",
+ " model.add(Dense(n_categories, activation='softmax'))\n",
" \n",
" sgd = optimizers.SGD(lr=eta)\n",
" model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])\n",
@@ -1588,261 +1602,11 @@
},
{
"cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "360/360 [==============================] - 0s 686us/sample - loss: 2.3550 - accuracy: 0.1000\n",
- "Learning rate = 1e-05\n",
- "Lambda = 1e-05\n",
- "Test accuracy: 0.100\n",
- "\n",
- "360/360 [==============================] - 0s 435us/sample - loss: 2.4565 - accuracy: 0.1056\n",
- "Learning rate = 1e-05\n",
- "Lambda = 0.0001\n",
- "Test accuracy: 0.106\n",
- "\n",
- "360/360 [==============================] - 0s 278us/sample - loss: 2.5534 - accuracy: 0.1056\n",
- "Learning rate = 1e-05\n",
- "Lambda = 0.001\n",
- "Test accuracy: 0.106\n",
- "\n",
- "360/360 [==============================] - 0s 270us/sample - loss: 3.9470 - accuracy: 0.1139\n",
- "Learning rate = 1e-05\n",
- "Lambda = 0.01\n",
- "Test accuracy: 0.114\n",
- "\n",
- "360/360 [==============================] - 0s 284us/sample - loss: 16.9267 - accuracy: 0.1111\n",
- "Learning rate = 1e-05\n",
- "Lambda = 0.1\n",
- "Test accuracy: 0.111\n",
- "\n",
- "360/360 [==============================] - 0s 300us/sample - loss: 137.9984 - accuracy: 0.1167\n",
- "Learning rate = 1e-05\n",
- "Lambda = 1.0\n",
- "Test accuracy: 0.117\n",
- "\n",
- "360/360 [==============================] - 0s 259us/sample - loss: 801.0018 - accuracy: 0.1083\n",
- "Learning rate = 1e-05\n",
- "Lambda = 10.0\n",
- "Test accuracy: 0.108\n",
- "\n",
- "360/360 [==============================] - 0s 278us/sample - loss: 2.3425 - accuracy: 0.1278\n",
- "Learning rate = 0.0001\n",
- "Lambda = 1e-05\n",
- "Test accuracy: 0.128\n",
- "\n",
- "360/360 [==============================] - 0s 279us/sample - loss: 2.3908 - accuracy: 0.0861\n",
- "Learning rate = 0.0001\n",
- "Lambda = 0.0001\n",
- "Test accuracy: 0.086\n",
- "\n",
- "360/360 [==============================] - 0s 289us/sample - loss: 2.5874 - accuracy: 0.0806\n",
- "Learning rate = 0.0001\n",
- "Lambda = 0.001\n",
- "Test accuracy: 0.081\n",
- "\n",
- "360/360 [==============================] - 0s 275us/sample - loss: 3.8508 - accuracy: 0.0889\n",
- "Learning rate = 0.0001\n",
- "Lambda = 0.01\n",
- "Test accuracy: 0.089\n",
- "\n",
- "360/360 [==============================] - 0s 266us/sample - loss: 16.0708 - accuracy: 0.1389\n",
- "Learning rate = 0.0001\n",
- "Lambda = 0.1\n",
- "Test accuracy: 0.139\n",
- "\n",
- "360/360 [==============================] - 0s 261us/sample - loss: 81.1070 - accuracy: 0.1167\n",
- "Learning rate = 0.0001\n",
- "Lambda = 1.0\n",
- "Test accuracy: 0.117\n",
- "\n",
- "360/360 [==============================] - 0s 274us/sample - loss: 5.9006 - accuracy: 0.0861\n",
- "Learning rate = 0.0001\n",
- "Lambda = 10.0\n",
- "Test accuracy: 0.086\n",
- "\n",
- "360/360 [==============================] - 0s 270us/sample - loss: 2.1962 - accuracy: 0.3000\n",
- "Learning rate = 0.001\n",
- "Lambda = 1e-05\n",
- "Test accuracy: 0.300\n",
- "\n",
- "360/360 [==============================] - 0s 280us/sample - loss: 2.1921 - accuracy: 0.3750\n",
- "Learning rate = 0.001\n",
- "Lambda = 0.0001\n",
- "Test accuracy: 0.375\n",
- "\n",
- "360/360 [==============================] - 0s 293us/sample - loss: 2.3488 - accuracy: 0.2917\n",
- "Learning rate = 0.001\n",
- "Lambda = 0.001\n",
- "Test accuracy: 0.292\n",
- "\n",
- "360/360 [==============================] - 0s 275us/sample - loss: 3.5324 - accuracy: 0.4389\n",
- "Learning rate = 0.001\n",
- "Lambda = 0.01\n",
- "Test accuracy: 0.439\n",
- "\n",
- "360/360 [==============================] - 0s 279us/sample - loss: 10.1736 - accuracy: 0.3667\n",
- "Learning rate = 0.001\n",
- "Lambda = 0.1\n",
- "Test accuracy: 0.367\n",
- "\n",
- "360/360 [==============================] - 0s 269us/sample - loss: 2.6613 - accuracy: 0.0917\n",
- "Learning rate = 0.001\n",
- "Lambda = 1.0\n",
- "Test accuracy: 0.092\n",
- "\n",
- "360/360 [==============================] - 0s 294us/sample - loss: 2.3171 - accuracy: 0.0778\n",
- "Learning rate = 0.001\n",
- "Lambda = 10.0\n",
- "Test accuracy: 0.078\n",
- "\n",
- "360/360 [==============================] - 0s 271us/sample - loss: 1.0447 - accuracy: 0.8778\n",
- "Learning rate = 0.01\n",
- "Lambda = 1e-05\n",
- "Test accuracy: 0.878\n",
- "\n",
- "360/360 [==============================] - 0s 275us/sample - loss: 1.2041 - accuracy: 0.8778\n",
- "Learning rate = 0.01\n",
- "Lambda = 0.0001\n",
- "Test accuracy: 0.878\n",
- "\n",
- "360/360 [==============================] - 0s 286us/sample - loss: 1.2453 - accuracy: 0.8694\n",
- "Learning rate = 0.01\n",
- "Lambda = 0.001\n",
- "Test accuracy: 0.869\n",
- "\n",
- "360/360 [==============================] - 0s 276us/sample - loss: 2.1735 - accuracy: 0.8806\n",
- "Learning rate = 0.01\n",
- "Lambda = 0.01\n",
- "Test accuracy: 0.881\n",
- "\n",
- "360/360 [==============================] - 0s 285us/sample - loss: 2.2429 - accuracy: 0.4694\n",
- "Learning rate = 0.01\n",
- "Lambda = 0.1\n",
- "Test accuracy: 0.469\n",
- "\n",
- "360/360 [==============================] - 0s 267us/sample - loss: 2.3081 - accuracy: 0.0778\n",
- "Learning rate = 0.01\n",
- "Lambda = 1.0\n",
- "Test accuracy: 0.078\n",
- "\n",
- "360/360 [==============================] - 0s 267us/sample - loss: 2.3074 - accuracy: 0.0889\n",
- "Learning rate = 0.01\n",
- "Lambda = 10.0\n",
- "Test accuracy: 0.089\n",
- "\n",
- "360/360 [==============================] - 0s 269us/sample - loss: 0.1042 - accuracy: 0.9750\n",
- "Learning rate = 0.1\n",
- "Lambda = 1e-05\n",
- "Test accuracy: 0.975\n",
- "\n",
- "360/360 [==============================] - 0s 271us/sample - loss: 0.1380 - accuracy: 0.9778\n",
- "Learning rate = 0.1\n",
- "Lambda = 0.0001\n",
- "Test accuracy: 0.978\n",
- "\n",
- "360/360 [==============================] - 0s 589us/sample - loss: 0.2649 - accuracy: 0.9778\n",
- "Learning rate = 0.1\n",
- "Lambda = 0.001\n",
- "Test accuracy: 0.978\n",
- "\n",
- "360/360 [==============================] - 0s 388us/sample - loss: 0.4903 - accuracy: 0.9667\n",
- "Learning rate = 0.1\n",
- "Lambda = 0.01\n",
- "Test accuracy: 0.967\n",
- "\n",
- "360/360 [==============================] - 0s 286us/sample - loss: 1.7649 - accuracy: 0.5278\n",
- "Learning rate = 0.1\n",
- "Lambda = 0.1\n",
- "Test accuracy: 0.528\n",
- "\n",
- "360/360 [==============================] - 0s 298us/sample - loss: 2.3120 - accuracy: 0.0889\n",
- "Learning rate = 0.1\n",
- "Lambda = 1.0\n",
- "Test accuracy: 0.089\n",
- "\n",
- "360/360 [==============================] - 0s 297us/sample - loss: 1584.1322 - accuracy: 0.0972\n",
- "Learning rate = 0.1\n",
- "Lambda = 10.0\n",
- "Test accuracy: 0.097\n",
- "\n",
- "360/360 [==============================] - 0s 282us/sample - loss: 0.0681 - accuracy: 0.9778\n",
- "Learning rate = 1.0\n",
- "Lambda = 1e-05\n",
- "Test accuracy: 0.978\n",
- "\n",
- "360/360 [==============================] - 0s 272us/sample - loss: 0.0806 - accuracy: 0.9833\n",
- "Learning rate = 1.0\n",
- "Lambda = 0.0001\n",
- "Test accuracy: 0.983\n",
- "\n",
- "360/360 [==============================] - 0s 280us/sample - loss: 0.5444 - accuracy: 0.8889\n",
- "Learning rate = 1.0\n",
- "Lambda = 0.001\n",
- "Test accuracy: 0.889\n",
- "\n",
- "360/360 [==============================] - 0s 271us/sample - loss: 1.5942 - accuracy: 0.6972\n",
- "Learning rate = 1.0\n",
- "Lambda = 0.01\n",
- "Test accuracy: 0.697\n",
- "\n",
- "360/360 [==============================] - 0s 272us/sample - loss: 2.3020 - accuracy: 0.2528\n",
- "Learning rate = 1.0\n",
- "Lambda = 0.1\n",
- "Test accuracy: 0.253\n",
- "\n",
- "360/360 [==============================] - 0s 273us/sample - loss: 369.8135 - accuracy: 0.0889\n",
- "Learning rate = 1.0\n",
- "Lambda = 1.0\n",
- "Test accuracy: 0.089\n",
- "\n",
- "360/360 [==============================] - 0s 327us/sample - loss: nan - accuracy: 0.0778\n",
- "Learning rate = 1.0\n",
- "Lambda = 10.0\n",
- "Test accuracy: 0.078\n",
- "\n",
- "360/360 [==============================] - 0s 276us/sample - loss: 2.4197 - accuracy: 0.0889\n",
- "Learning rate = 10.0\n",
- "Lambda = 1e-05\n",
- "Test accuracy: 0.089\n",
- "\n",
- "360/360 [==============================] - 0s 272us/sample - loss: 5.5606 - accuracy: 0.1056\n",
- "Learning rate = 10.0\n",
- "Lambda = 0.0001\n",
- "Test accuracy: 0.106\n",
- "\n",
- "360/360 [==============================] - 0s 268us/sample - loss: 2.5543 - accuracy: 0.0889\n",
- "Learning rate = 10.0\n",
- "Lambda = 0.001\n",
- "Test accuracy: 0.089\n",
- "\n",
- "360/360 [==============================] - 0s 451us/sample - loss: 2.3939 - accuracy: 0.1250\n",
- "Learning rate = 10.0\n",
- "Lambda = 0.01\n",
- "Test accuracy: 0.125\n",
- "\n",
- "360/360 [==============================] - 0s 274us/sample - loss: 753.5854 - accuracy: 0.0889\n",
- "Learning rate = 10.0\n",
- "Lambda = 0.1\n",
- "Test accuracy: 0.089\n",
- "\n",
- "360/360 [==============================] - 0s 278us/sample - loss: nan - accuracy: 0.0778\n",
- "Learning rate = 10.0\n",
- "Lambda = 1.0\n",
- "Test accuracy: 0.078\n",
- "\n",
- "360/360 [==============================] - 0s 283us/sample - loss: nan - accuracy: 0.0778\n",
- "Learning rate = 10.0\n",
- "Lambda = 10.0\n",
- "Test accuracy: 0.078\n",
- "\n"
- ]
- }
- ],
+ "execution_count": 19,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
"source": [
"DNN_keras = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)\n",
" \n",
@@ -1863,140 +1627,11 @@
},
{
"cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
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- ]
- },
- {
- "data": {
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J8r9/mzYDmjDl1VmJ2jz9TFF8/TKydcmuZInXWWx251dqHMUhFSE3NzdiY2MTrY+MjMTd3d0RISQScuYSWXNmil/OljszEZevE3Uz6qHaeGX0ZO47y3i14lsMa/IhFgucPxkc37ZG2yr8sPinJL9MnnQhZy+RNeedeR3Zcv2b95P9m31yMEPuKXXsXzp/FYDNC3YCcP5kCEf2HKN4xcIOyuzRhJ65RNYcCXLNlZmIK2lrnycUt28THOO5s9xj/yfdxiujJ3PfXsqrFYYxrPG4uP1/Ipi6L1TDK6MnU7aPYsbeD8iaMzNDv+xN1SYBDs3vQUJOhyXK7drl60QmzP8BbQqXK4CrmyuHdvyRaPu12j/H9wt3pMrvfrNySEeoV69etGzZkuHDhzN16lSmTZvG8OHDadeuHb169XJECIns3/I7xSs/Ra7C2QFo0r0Oe9YfeOg2TbsH8tLINgBk8s9Iw6612LZ8d/xry1Qrzm/bjzgilWS3f8thilcunCDvQPZsOPCAV6UNZsg9pY794H9COX7gFPVerBb/3NNVn+LYgZOOSu2R7N96hOKVCpGrkD8ATbrVYs+G35wbVAra/8Nh477tUYc96/c/dJumPerw0si4yeRx+78225bv5rMhi3il9Bv0rvI2vau8zaULV/jw5Rn87wk7f/ZvPkiJqkXir/xq2qs+e9b8/J/alKn5NL9t+z3J7Zep8TS/bkn6OXkyOWRorFmzZlSuXJk9e/YQEhKCzWajYsWK9OvXj+zZszsihETCQ68x6dU5jFjSHzcPVy6cDGFi91kUCSjIwBmv0Lvq8Hu2AVg2cR1vzuvFrF/ifiNaMPprju2/c5lo7qdyEPxPqFNye1zhoRFM6jWHEYv74ebuxoVTIUzsMYsi5QsycEY3ej8zwtkhphgz5J6Sx/57HabSd0oXmvaog8XFwuIPVhvOiydReFgEk3vPY/iCPnG5ngplYq/PKVK+AAOmvUyf6qOcHWKyCg+9xqSesxix9HXcPNzi9u0rM+P2/8we9K7y9j3bACybsJY3573GrP3jsVgsLBj9Fcf2P9md3YSuhl7jo24zGLFyMO4ebpw/EcyELtMpWqEQg+a8Rq+AIfds8688RXJyMSgkye3nKpKD4Hs8l5o8CZOYHcViT4X1uwaenZ0dgvO4mOfglCSY6Xavd7F4OGcY/Ulhj4p6cKM0zBadNocqH9b3tpUOfb8Dp/M57L0C8p122Hsl5cm80YOIiIg4jdVEtxk0T6YiIiIid1FFSERERAx0+byIiIiICagiJCIiIgZmumpMFSERERExLVWERERExMBqN0+dxDyZioiIiNxFFSERERExsJmoTmKeTEVERETuooqQiIiIGOiqMRERERETUEVIREREDHTVmIiIiIgJqCMkIiIipqWhMRERETGwabK0iIiISNqnipCIiIgYWE1UJzFPpiIiIiJ3UUVIREREDHT5vIiIiIgJqCIkIiIiBvqjqyIiIiImoIqQiIiIGFjtuo+QiIiISJqnipCIiIgY6D5CIiIiIiagipCIiIgY2HQfIREREZG0TxUhERERMdAcIRERERETUEdIRERETEtDYyIiImKgGyqKiIiImECqrAhZPNM7OwSnsUdFOTsEp7K4pcpDNtnYY2OdHYLTWLw8nR2CU9mjY5wdglO5eJp7/zua/uiqiIiIiAmY+9drERERScSqGyqKiIiIpH2qCImIiIiBDV01JiIiIpLmqSIkIiIiBpojJCIiImICqgiJiIiIgf7oqoiIiIgJqCIkIiIiBjb9rTERERGRtE8VIRERETHQHCERERERE1BHSERERExLQ2MiIiJiYNMNFUVERETSPlWERERExMCqP7oqIiIikvapIiQiIiIGmiMkIiIiYgKqCImIiIiB5giJiIiImIAqQiIiImKgOUIiIiIiJqCKkIiIiBhYVRESERERSftUERIREREDm64aExEREUn7VBESERERA80REhERETEBVYRERETEwGbXHCERERGRNE8dIRERETEtDY2JiIiIgdVEdRJ1hG6rXK80XUe2xt3DjVN/nGVK//ncjIhMsu3gT7sS9Oc5vp6+GQCP9O70mdiJYgEFsVgsHN1/kk+HLCE6MsaRKSSryg3K0nV0O9w93Dn1+xmm9P78np/HG7N7EnTkDF9N/dbBUSafyvXL0PXdNrinc+fUkTNM6fvFvff/zFcI+uMsX3/yXfw6b19PPto4jMl9v+D4r0EOijplmG3fA1SqU5Kub7eIO///PMfHgxZz8/o99v/UzgT9eZ6vP9sCgJdPegZOfpE8T2XHxcXCDyv2svLT7x0Z/n9WuWFZuo5+Pu54//0MU3rNSbSP79XGJ7M3/aa9TKEy+Ym8EcXmhTtZOzMu3yqNyzNkTk9CzlyK387gumO5dY/P8kljxmNfNDQGgG/WDAya/jJjusyke5URXAgKo+vI1ona5S2ag/GrB1O9eQXD+o6DmuDq6spr1d7jtWqjSJfeg/YDGzkq/GTnm82HwbN6MKbTJ3QvP5SLQSF0G90+Ubu8xXLx4cZhVG9ZyQlRJh/frD4MmtGNMZ0/pXvFt7kQFErXUW0TtctbNCfj1w2heouKhvWV6pVm6pYR5C6Sw1Ehpxiz7Xu4ff5/3Jmx3efQo/poLv4TRtd3WiRql7dIdsat7E+1puUN6196sxlhF67wWu336d9wAk26VKd4hYKOCv8/i9vHPRnTcRrdy77JxVMhdBvT/qHbvDrhBW5dj6Jn+aEMqDmKSvXLUqVROQCerlqErz7eSO+qw+MfqaUTZMZj/35sdovDHs6mjhAQULskx34N4vzJEAA2zNtOYLsqido1e6U2mxb+yI9r9hvWH95zjKWTNmC327HZ7Px96DT+ebI6JPaUEFCnFH/tP8n5E8EArJ+zlcD2zyRq17xnHTZ9uYOdq/Y5OsRkFRBYkmMHTt3Z/3O3EdiuaqJ2zXoEsmn+Tn5c/bNhfYtedZnQczZXLoY7JN6UZLZ9DxBQswTHfvuH86dCAVg//0dqt078Q67pyzX5bsluflz3q2H9ZyNWMue9VQBkye6Lu4cbNyNupXzgjyigbmnjPp69hcAOzz50myLlC7JlyS5sNjuxMVb2bfqNaq0qA3EdoXK1nmbm3veZ9MNwSj1XzIGZPR4zHvup0bp162jcuDH169dn8eLFiZ4/cuQIbdq0oXnz5rz66qtcu3btgdtURwjwy52Z0HNX4pdDz1/BO6MXXj7pDe1mDF3K9q8TH/wHtv3Budsnj3+eLLTqVTdRZyk18cuTlbCzl+OXQ89dxts38efx6eCFbFuxx9HhJTu/PFkIPZcw3ytJ5jtjyGK2f7U30euHt5nCsQNBKR2mQ5ht3wNky5WJ0PN3zv+wC1fxzuiJVwZjzjPfWcH2Vb8kuQ2b1caQ6V34bNs7HNp9nLN/B6dozI/DL08Wws7eGbpKah/fr83Rn09Qp1M1XN1cSe+djmotK5Elhy8A1y5HsP7zrbxW5R3mjVzBu8tfJ1vuzI5L7jGY8di/HxsuDns8rODgYKZMmcKSJUtYvXo1y5cv5++//za0ef/99+nfvz9r166lYMGCzJ0794HbdUhH6Pz58/d9OJvFxQW73Z5ovdVq+0/beapsPj7a+CZrP9/Gvs2Hkis8h3OxWJLl80gtLC4Wkkg3zeZ7P2bb9wAuLi6QDPt/Yt/5tC85FJ/MXnQa1DiZokt+cfs48Xqr1f5QbWYPW4LdbmfG/8YyasUADmz5ndhoKwBjOkxj16q4iumR3cf4439/ExBYKkXySG5mPPafFNeuXePs2bOJHndXc3bv3k3VqlXJlCkTXl5eNGjQgE2bNhna2Gw2bty4AcCtW7dIn97YkU2KQyZLv/rqqwQFBeHv75/oQLNYLGzZssURYdxT6NlLhjH9bDkzEXHlBlE3ox96GzVbV6LvxBf49M0lSVaNUpOQs5coXqlw/HK2XJmJuHz9P30eqUnomUsUr1AofjlbrsxEXEm7+d6P2fY9QMi5yxQrXyB+Of78v/VwOQfUKkHQn+e5HBxO5M0otq/aT7Um5VIo2scXcuaufZz7330c9VBtMubNytx3lhFxJe6HTYchzTh/MhhvXy+a9azDsonr4l9nsUBsjNUBWT0+Mx7792N14Nyd+fPnM3369ETr+/btS79+/eKXQ0JC8PPzi1/29/fn0CFj0WHYsGF069aNDz74AE9PT1asWPHA93dIRWjp0qUULFiQCRMmsHXrVsPD2Z0ggP3b/qB4xULkKuQPQJOuNdnz7W8P/foqDcrw2rgOvN1mSqrvBAHs33KY4pULk6twdgCadA9kz4YDTo4q5ezfeoTilRLs/2612LPh4fd/WmK2fQ9wYPufFK9QgFwF475gG79UjT3fPXxFt0azAF4YHFcBcvdwo0bzAH776a8UiTU57N/yO8UrP5VgH9dhz/oDD92mafdAXhrZBoBM/hlp2LUW25bv5lbELZr1qku1lnEXExQum59iFQvzy/epozpuxmP/SdGlSxe2bNmS6NGlSxdDO5vNhsVyp4Nmt9sNy5GRkbzzzjt8+eWX7Nq1i06dOjF06NAHvr9DKkIZMmRg7NixrFy5kgoVKjz4BQ4WHhbB5L5fMPzLXrh5uHHhVCgTX5tLkXL5GTC1C31qjr7v63uMbgcWCwOm3tlpf+z9m0/fXJLSoaeI8NAIJvWaw4jF/XBzd+PCqRAm9phFkfIFGTijG72fGeHsEJNVeFgEk3vPY/iCPrh5uMbt/16fU6R8AQZMe5k+1Uc5O0SHMdu+Bwi/dJ0pAxbxzpzuced/UCgf9V9AkbL5eP2jF+hbb9x9Xz/nvW/o92EHZm57B4Dd3x5kzZztDoj80YSHXmPSq3MYsaR/3PF+MoSJ3WdRJKAgA2e8Qu+qw+/ZBmDZxHW8Oa8Xs34Zh8UCC0Z/zbH9pwAY1e5jek/uTOfhbbDGWvmg83SuXbruzHQfmhmP/ftx5NVcGTNmJGPGjA9slyNHDn755c48vdDQUPz9/eOXjx07Rrp06ShTpgwA7du3Z+rUqQ/crsWe1KDoE65hlh7ODsFp7FFRD26UhlnczH3rK3tsrLNDcBqXjD7ODsGpbFcffPVLmubi/Musnem7Gwsc+n6v/9rRYe81tfzSh2oXHBxMx44d+eqrr/D09KRDhw6MGTMmvuMTHh5Oo0aNWLRoEYUKFWLdunWsWLGChQsX3ne75v6pIiIiIonY7E/eReXZs2dn4MCBvPTSS8TExNC2bVvKlClDjx496N+/P6VLl2bcuHEMGDAAu91O1qxZ+eCDDx64XVWEUhlVhMzdd1dFyLxUEVJFyJH6HXjBYe/1SUDi+wE5krl/qoiIiEgiVszT8Xzyal8iIiIiDqKKkIiIiBg8CX8DzFFUERIRERHTUkdIRERETEtDYyIiImLwJF4+n1LMk6mIiIjIXVQREhEREQObLp8XERERSftUERIREREDqy6fFxEREUn7VBESERERA101JiIiImICqgiJiIiIgf7EhoiIiIgJqCIkIiIiBrqPkIiIiIgJqCIkIiIiBpojJCIiImICqgiJiIiIge4jJCIiImIC6giJiIiIaWloTERERAw0WVpERETEBFQREhEREQPdUFFERETEBFQREhEREQPNERIRERExAVWERERExEAVIRERERETUEVIREREDFQREhERETGBVFkRCmn3tLNDcBqLzdkROJdbpN3ZITjV1SLm+S3tbt4XnB2Bc/kt+NXZITiXxbzHvjOoIiQiIiJiAqmyIiQiIiIpR3eWFhERETEBVYRERETEQHOERERERExAHSERERExLQ2NiYiIiIGGxkRERERMQBUhERERMVBFSERERMQEVBESERERA1WERERERExAFSERERExsKsiJCIiIpL2qSIkIiIiBvqjqyIiIiImoIqQiIiIGOiqMRERERETUEVIREREDHTVmIiIiIgJqCIkIiIiBpojJCIiImIC6giJiIiIaWloTERERAw0WVpERETEBFQREhEREQNNlhYRERExAVWERERExMBud3YEjqOKkIiIiJiWKkIiIiJiYENzhERERETSPFWERERExED3ERIRERExAVWEbqtWqiD9Wj2Hu5srx8+FMXrB99yIjEqbZy0AACAASURBVDa0aV+rLG1rlsVut3M2NJwxi77nSsQtXCwWhnasTYUieQDY9fspPv76R2ek8ciqlS5I39Zx+f99NozR8xPn/3ztsrStdSf/sQvi8v+wV1Py+vvGt8ud1Zf9x84y6NO1jk7jkT1XtiCvPV8ND3dX/j4TxvtzNifKv23dcrSpUwY7cC74Kh/M+54r124Z2ozv34ywqzf4aMFWB0b/+GoWKcjgwOfwcHXlr5Aw3l77PTeio5NsW6dYYSa2bEjAh58C4GKxMLJRbSrljzv+d/x9ignfp7Ljv1RB+rV4Dnd3V46fDWP0oiTO/5plaVujLHZun/+LE5z/He46/79JXfknVLlBWbqOboe7hzunfj/DlN6fczMiMsm2b8zuSdCRM3w19VsHR5kyKjcoS9f32uHu4capI2eY0nvuvXOf1YOgI2f5alrayP1uuo+QyWTK4MmoLvV5Y9Z6Wr87n3Nh4fRrVc3QpkQ+fzrXq0DXD5fx/OiFnA65Qu/mzwLQpGoJCmTPzPOjF9JhzCIqFM1D3YAizkjlkWTK4Mm7L9dnyMz1tBkxn7Nh4fRrbcy/eD5/OtevQLfxy2g/aiFngq/wWou4/Id+tp5OoxfTafRixi74gYhbUXy4JPV0BDL5eDK8ZwPemraO59/8knMh4fRuf1f+Bfx5oXEFuo9eRqe3FnAm+CqvtnnO0ObFJhUpVyy3I0NPFpm9PBnXvD79Vq6n4Yz5nLkSzht1qiXZNn+WTAytV4OE8yhblClBwayZafbZQlrMWkTlfHloWCJ1Hf+jXqrPG7PX03rU7fO/5T3O/4nLeH7M7fO/2e3zv8rt83/MQjqMXUSFIqnr/E/IN5sPg2f1YEynT+hefigXg0LoNrp9onZ5i+Xiw43DqN6ykhOiTBm+2XwY/Fl3xrzwCd0DhnHxVCjdRj+fqF3eYjn5cMPQNJW72TmsI/TDDz+wcOFCTp8+bVi/fPlyR4VwT888nZ8j/1zkTMhVAFbuOESjKsUNbf48HULLEV9yPTIaDzdX/DNlIPxG3G8KLi4W0qdzx8PNFXd3V9xdXYmOtTo8j0f1TMn8/BF0J/+vtifO/+jpEFoN/5Lrt+Ly98t8J/9/ubm68F7XBkxavp3gK9cdFv/jqlI6P3+evMiZ4Lj8v9lykIbPljC0ORoUQtshX3DjVjQe7rfzv36nGhRQPA/PlCnAqq2HHBp7cqhWKD+Hz1/kn8tx+S/95RDNShdP1C69mxsTWzVk/OYdhvWuFgueHu54uLri4Rp3/EdZU9HxXyI/R4Iucib09vm/8xCNKidx/o+8z/nvkeD8d3MlOib15J9QQJ1S/LX/JOdPBAOwfs5WAts/k6hd85512PTlDnau2ufoEFNMQOBduX++lcDnk8q9bprLPSl2u+MezuaQjtBHH33EokWLCAoKomPHjqxZsyb+uWXLljkihPvKntmH4Mt3fnCHXInAxzMd3uk9DO1ibTZqlS3Mtx/2IKBIHtbsPgLAut1/EHEzik0f9mDzhJ6cCb3KzkMnHZrD48ie2YeLV4z5Z/BKIn+rjVrlCvPthLj81/50xPB8y2qlCL16g22/nnBI3MklexYfgi9FxC+HXE46f6vVRo0KhVk3tSfliuVh/c64/LNl8mZQ59qMnPEtVtsTcFb/Rzl8fbgYfmf/X7wWgU/6dHh7GPMf3bQuy/cf5q/gMMP6bw7+wbVbUewc2INdg3ryz5WrbDuWuo7/hB33kKsPOP/H3XX+77l9/o/vwebxt8//w6kn/4T88mQl7Ozl+OXQc5fx9vXCyye9od2ngxeybcUeR4eXovzyZCHs3EPmvvJ/jg5PUpBDOkI7duzg888/Z8SIESxevJipU6fy7bdx46r2J6A76OICdhLHYbXZEq3bfvAEdQZ/xqz1e/i0f2ssFujZtCpXIm5Rd8gsGg2dQ0bv9LxYN8ARoScLiwtJdsuTzP+3E9QZ9Bmz1+1h+oC4/P/VqV4AczfsTcFIU4bFYkli74PVnjj/nftP0KD3TD5ftYepb7bG3c2VMX2aMGXxdi6F30j5YFOAiyXp49+WIP9OFcsQa7Px9W9HErXrW7Mql2/e4rlJs6jx8Rwyeaana9XUc/z/5/N/yF3nf5OqXLl+i7pvzqLRW3PI6JWeF+uknvwTcrFYkvxOtloTfxZpjYuLJcnqhBlyT4rdbnHYw9kc0hGy2+1Ybv/ELFCgALNmzeL9999n79698eud6eLlCPx8M8Qv/1v2joyOjV+X18+XcoVzxS+v+ekIObP6kNErPYHln2LNT78Ta7VxPTKa9Xv+oFKxvA7N4XFcvBRBtkx38vdLIv88fr6UeypB/rvu5A9QLK8fri4u7D921nGBJ5PgSxH4ZfKOX44b9ookMipB/v6ZKFv0Tv7rdvxOjmwZKVEwO7n9fBnQqSYLx75I68Ay1K1SlLdfqefQHB7HhfAI/H3u7P/sGTNw9VYkt2Lu5N+qbElK58rO6p4vMLtTS9K7ubG65wv4Z/CmXvGn+Pq334mx2bgeFc2qg39QpUAqOv4f5fzffYScWRKc/7sTnP//S13nf0IhZy+RNWfm+OVsuTITcfk6UTeTnjifloScuUzWHJnil82Uu9k5pCPUsGFDOnfuzKFDcfMnihQpwtSpUxkwYECiOUPOsOePfyhdKAd5/eNOgjY1yrDjoHF4J5tvBsb1aEwm77gf/I2qFOfEuUuE34jk6OkQ6lUsCoCbiws1yxbi0KkLjk3iMfzvrvzb1izDjt/uyj9TBj7o2ZhMGW7nX/VO/gABRfPwy1Hn78tHsff3IEo9lZO82ePyb12nLD8e+NvQJlsmb8b2aYLv7fwbPFuck2cvcej4eZoPmEPn4YvoPHwR32w9xA97j/HB3O8dnsej2nXiH8rmzkH+LHH5d6hQhi1/Gfd/u7lLafbZQlrOXkzPJauJjI2l5ezFhFy/wR8XQmj09J3jP7BoIQ6eSz3H/54//6F0wRzk9bt9/le/x/n/SoLzv3JxTpxPcP5XSHD+l0ld539C+7ccpnjlwuQqnB2AJt0D2bPhgJOjcoz9W+/K/ZVA9mz41clRiSM45PL5vn37UqFCBby97/zWXaFCBb755hvmzZvniBDu60rELUbN38zEnk1xd3PhbGg4I77YRIn82RnZuS4dxy7m17/PMXfjPmYPbofVZiP06g0GzYy7PHzSyh0M7VCbr9/rgs1mZ9/R08z/7hcnZ/XwrkTc4r0vNjOh1538R86Ny39El7p0Gr2Y346fY96Gfcx6407+g2fcuTw+X/ZMnL90zYlZPLor124xZs5mxvVvhpurC+dCwnlv1iaKF8zOO6/Uo/PwRfx27BxfrN3LzHeex2q1EXb1BkM+XvPgjacCl2/e4q21m5nWtinuri6cvhLO0NWbKJUzO2Ob1aXl7MX3ff24zTsY0ag23/bugtVmZ8+p03z+U+o6/kctuH3+u7pwNiycEV9uokS+7Ix8sS4dP7h9/m/ax+xB7bBabYSG32DQZ7fP/692MLR9bb5+9/b5/9dp5m9OPfknFB4awaRecxixuB9u7m5cOBXCxB6zKFK+IANndKP3MyOcHWKKicv9c0Ys6oubhxsXToYwsedsipQvwMBPu9H72ZHODtGhnoQhK0ex2J+ESTr/UcCrU5wdgtNYzDlcHc8tMtUdrsnqahHzfDndzTt1FlmSjd8Ck1cnnoBpFM703fX5Dn2/0mvfddh7HW7+nsPeKym6oaKIiIgY6IaKIiIiIiagipCIiIgYpL5JM49OFSERERExLVWERERExMBMV42pIiQiIiKmpYqQiIiIGKgiJCIiImICqgiJiIiIgYkuGlNFSERERMxLFSEREREx0BwhERERERNQRUhERESMTDRJSBUhERERMS11hERERMS0NDQmIiIiBposLSIiImICqgiJiIiIgV2TpUVERETSPlWERERExEBzhERERERMQBUhERERMVJFSERERCTtU0VIREREDHTVmIiIiIgJqCIkIiIiRqoIiYiIiKR9qgiJiIiIge4jJCIiImICqgiJiIiIkeYIiYiIiKR96giJiIiIaWloTERERAw0WVpERETEBFJlRWjKWzOdHYLTWDFPLz0pMXZXZ4fgVNdsns4OwWmGbO7o7BCcys9i7nNfHOwJnSy9bt06Zs6cSWxsLF26dOGFF14wPH/y5EneffddwsPD8fPzY/Lkyfj6+t53m6oIiYiIyBMvODiYKVOmsGTJElavXs3y5cv5+++/45+32+289tpr9OjRg7Vr11KiRAlmz579wO2myoqQiIiIpCTHVSCvXbvGtWvXEq3PmDEjGTNmjF/evXs3VatWJVOmTAA0aNCATZs20bdvXwCOHDmCl5cXNWrUAKBXr15Jbvdu6giJiIiI08yfP5/p06cnWt+3b1/69esXvxwSEoKfn1/8sr+/P4cOHYpfPn36NNmyZePtt9/mzz//pFChQowYMeKB76+OkIiIiBg5cI5Qly5daNWqVaL1CatBADabDUuCuXJ2u92wHBsby759+1i0aBGlS5fm448/Zvz48YwfP/6+76+OkIiIiDjN3UNg95IjRw5++eWX+OXQ0FD8/f3jl/38/MifPz+lS5cGoGnTpvTv3/+B29VkaRERETGyO/DxkJ599ln27NnD5cuXuXXrFps3b46fDwRQvnx5Ll++zNGjRwHYunUrJUuWfOB2VRESERGRJ1727NkZOHAgL730EjExMbRt25YyZcrQo0cP+vfvT+nSpfn0008ZPnw4t27dIkeOHEyYMOGB21VHSERERIye0DtLN2vWjGbNmhnWzZkzJ/7/ZcuW5auvvvpP29TQmIiIiJiWKkIiIiJiYH9C7yydElQREhEREdNSRUhERESMVBESERERSfvUERIRERHT0tCYiIiIGD2hl8+nBFWERERExLRUERIREREDiyZLi4iIiKR9qgiJiIiIkSpCIiIiImmfKkIiIiJipKvGRERERNI+VYRERETESHOERERERNI+VYRERETESBUhERERkbRPFSERERExUkVIREREJO1TRUhERESMdB8hERERkbRPHSERERExLQ2NiYiIiIFFk6VFRERE0j5VhERERMTIRBUhdYRuO7QXVn0BsTGQuyB0GQie3sY2W9fAtrXg7gE580GnPuCd8c7zl0Ng/AAYMRN8fB0b/+M6vBfWfAExMZCnILyYRP7b1sD2teDhATnyQYck8p8wAIbPhAypLP+Ejuy1s+4LG7ExkKsgdBzogqe38QqKHWts/LjWjns6yJ7XQru+Frx9Uu9VFn/ti2Xzl9FYY+xkL+hCqwHpSe9lzOeP3bFsWRSNxQU8M1ho+Xo6suZ0wWa1s35mFKcOWwEoWsmNhq94YLGkns+jdr5CvFm1Oh6urhy9FMrQbd9xPSba0OadZ2rRuHBRwqMiATh59TJ9v18PwIsly9GhRGnSu7lxODSYodu+I9pmdXgeyaFyg7J0fa8d7h5unDpyhim953IzIjLJtm/M6kHQkbN8Ne1bB0eZMsycu5lpaAyIuArzJ0GvETBmLvjlgG/mGdsc/Q02rYBB42HkTChVCRZOvfP8nu9h4htw9VLq+fL/V8RVWDAJeo6A9+ZCthyw+q78//oNNq+AAePhndv5L06Q//++h8lvQPglx8ae3CKu2lk82Ua3ES4Mn+tK1pwW1n1h/NXo2EE7P6y002e8C0NnuPJ0JVg21eakiB/fjXA730yJouM76Rkwx5ssOVzY/EWUoU1MlJ2VEyPpNDw9fad7UbyKKxs+i2vz29ZYQs/a6TfDi76fehF02MqRXamnE5AlvScTAxvy2ndrqLN0HmeuhTO0ao1E7SrkyEW/79fTeOUCGq9cEN8JalCwCC+XLs8L61ZSb9kXpHdz45WyFRydRrLwzebD4M+6M+aFT+geMIyLp0LpNvr5RO3yFsvJhxuGUr1lJSdEmTLMnLvZOawjFBQURHBwMAArV65k7NixbNy40VFvf19/HID8xSB77rjlmk1h71awJ/j5d/o4lCgPmf3ilgOqxVWRYmPg6iX4bQ8M+MDxsSeHPw9AgWLgfzv/Gk1hXxL5F0+Qf7lqcVWkf/M/uAf6pdL8Ezp6wE6+ouCfO65DW62JhV+22rEn+DDOHLdTrJyFzH5xbcpWs/D7XoiNSZ215OMHYsld1IVsueO+Dio3cefgtlhDzrbb/bzIG3Hrom+Bm/ud52Ii7cTGxB0P1lhw83BoCo+let4CHAq5SFD4VQAWHfmNFkVKGNp4uLhSMps/vcpXYtPzXZjZoDm5MvgA0KbY08w5+AvhUZHYgXd2fM83x/5wdBrJIiCwFH/tP8n5E3Hf1es/30rg888kate8Z102fbmDnav2OTrEFGPm3M3ugUNj8+bNY/r06VitVnLnzk3RokUpVqwYxYoVo2jRouTJk+eBb/Lll1+ycOFCbDYbVatW5cKFC9SrV4+vv/6aU6dO0adPn2RJ5lFdDoUs2e4sZ/aDyJsWIm/a44eHChaHLWvgUjBkzQ4/fQexMRauX7OTKSu8NtI5sSeHK6GQOUH+mfwg8mbc49/8CxSPGxr7N/8938X90LtxDTJlhVdTcf4JXQ0lvoMD9/osLOxcY+NysJ0s2S3s3WzHevuz8M3qpMAfQ3ioHd9sd3LOmM1C1E2IugXpveLWpfO00LxvOmYPvoVXRgs2G/T8yBOAgLpuHNkVy4SXbmCzwlPlXSleJfWMuufK4MOF69fily9cjyBjunRkcPeIHx7z987A7nOn+WjfTxy7HEbPcpWY07AlTb5aSEHfLGT1vMj8Jm3w987AzxfOMm7PTmel81j88mQh7Nzl+OXQc5fx9vXCyye9YYjo08ELAQioU8rhMaYUM+eeFDNdNfbAb6tZs2YxYcIEypQpw5kzZzh27Bh//fUXO3fu5Pjx4wAUKVKEpUuX3nMbX3/9NRs3biQsLIymTZvyv//9j3Tp0tGuXTvatm3r9I6Q3QZJTWdwcb3z/yKlodmLMGM0uFjguQbg7WOP/604NbPZgIfIv8mLMGt03Gf1bAPw9gHXNJB/QnY7D/wsCpey0PAFC5+PtmFxgar1LXj5kGqPBbv9Hsd/gnrxxVNWti2Jpv8sL7LmdGHPmmiWvh9Jn+mebF0SjVdGC8MWexMbDYvHRLLrm2iqtU4dZSGLxZLkvFBrgorY2Yhwum78Jn559m8/069CVfL4+OLm4kL1PPnp8e1qoqyxTApsxJAq1Rj90zYHRJ+8XFwshkrwv6zW1Dv0+7DMnLvZPbAjlCFDBmrVqoWbmxv+/v5UqGAc+z579mx8h+hebDYbHh4e5M6dm27dupEuXbr456xW588lyOIPp47eWb4aBl4Z7KRLf2dd5E0oWhqqNYxbvhIGa+bHdQZSuyz+EJQofxLlX6Q0PNfwTpt1aST/hDL7QdDRO9+G4fGfxZ2eQuRNO0+VsfBMw7iewtUwOxsW2PFKpZ9FJj8LZ/+6k/O1MDueGcAjQc5/H7CS72lXsuaMy7lKU3c2zonm5jX4Y7eVpr08cHO34OYO5evEVYiqtXZ4Ko/kfMQ1yvvnjF/O4e3D1chb3IqNiV9XPEs2SmTzZ1WCIS8LFmJtVkJuXmfTyePx1aNVx/7k9YqJh1RSg5AzlylesXD8crZcmYm4fJ2om9H3eVXaYObck6Q/sXFHz549Wbly5T2fz5MnD7Vr177vNurXr8+LL76I1WqlX79+ABw9epROnTrRqFGj/xhy8nu6Apw8CsHn4pZ3bIByd32PXb0EH70Jt27ELW9cCpVqJf2bdGpTokJcRzDkdv4/boCySeQ/JUH+3y6FirXSRv4JFa9g4Z+jEHIurmOwa4Od0s8Ykwy/BNPetHHr9nyZzcvsVKhlSVVXSSX0VIArZ47aCDsX95vvzxtjKF7V+DtSzsIuBB22cv1KXJs/91jJnN2Ct6+FXIVd+P3HWACssXaO7o0lT3FXUosfz/5Duew5KeCbCYAXSpbl+6AThjY2YFS1QPLcvhz0xZLlOHoplIs3rrPxxDGaPFWMdK5xn1n9gk9xMOSiQ3NILvu3HqZ45cLkKpwdgCavBLJnw69OjsoxzJy72T2wIjR+/HhiYmL48ccfqV69OiVKlKBYsWJ4eno+9Ju8/vrr/Pzzz7i63vly9PDwoF+/ftSsWfPRIk9GGTPBy4Nh1hiIjQW/nNBtCAQdgwVT4q4Sy5EXGj4P416PG0p4qiR0dO6IXrLJmAleGgyzx8RNdM2WE14eAv8cg0VT4q4Sy5EXGjwPE14H2+3826eR/BPyyWSh0yAX5o21xX8WLw5x4fQxO0s/tjF0hivZ81qo97yFyQNs2G1QqKSFtn1SZycIIEMmF1oPTMeyDyKxxtrJksOFNm+k59wxK6umRdF3uheFy7lRrY2NucNu4epmwdPHwgsj40qGjXumY93MKD7ueQMXFwuFyrlSvW3qGSe8dOsmQ7ZtYmb95ri7uvJP+FUGbf2W0n7Z+bBWAxqvXMCxy2G8++MW5jZuhavFwoXrEfT/Ie6qsYVHfiNT+vSsb9sZFxcLR0KDeX/3dqfm9KjCQyOY1OtzRizqi5uHGxdOhjCx52yKlC/AwE+70fvZNDIZMAlmzj1JJpojZLHbkxoVveP06dP89ddf8Y+jR49y/vx58uTJw3fffeeoOA12BBV1yvs+CaxJTWAxkRh76qk0pIRrtof/BSStGbK5o7NDcKpiQw47OwRxou+uz3fo+xX6eLLD3uvkgEEOe6+kPLAilC9fPvLly0e9evXi1928eZNjx46laGAiIiLiJCaqCD3SfYS8vLwoV65ccsciIiIi4lCp52YfIiIi4hBmuo+Q/sSGiIiImJYqQiIiImKkipCIiIhI2qeOkIiIiJiWhsZERETESENjIiIiImmfKkIiIiJioMvnRURERExAFSERERExspvn71qqIiQiIiKmpYqQiIiIGGmOkIiIiEjap4qQiIiIGOiqMRERERETUEVIREREjFQREhEREUn7VBESERERA80REhERETEBVYRERETESBUhERERkbRPHSERERExLQ2NiYiIiJGGxkRERETSPlWERERExECXz4uIiIiYgDpCIiIiYlrqCImIiIhpaY6QiIiIGGmOkIiIiEjap4qQiIiIGOiqMRERERETSJUVoerpnR2BM5mom56kWGcH4FTXbaHODsFphsZYnB2COJE9KsrZIZiLiX7UqCIkIiIippUqK0IiIiKSglQREhEREUn7VBESERERA101JiIiImIC6giJiIiIaWloTERERIw0NCYiIiKS9qkiJCIiIgaaLC0iIiJiAqoIiYiIiJEqQiIiIiJpnypCIiIiYqSKkIiIiEjap4qQiIiIGOiqMRERERETUEVIREREjFQREhEREUn7VBESERERI1WERERERNI+VYRERETEQFeNiYiIiJiAOkIiIiJiWhoaExERESMNjYmIiIikfaoIiYiIiIEmS4uIiIiYgCpCIiIiYqSKkIiIiEjap4qQiIiIGKkiJCIiIpL2qSIkIiIiBhZnB+BAqgiJiIiIaakiJCIiIkYmmiOkjtB/ZLfDW+OgaCHo1sHZ0TiWmXOHtJ3/rj0ufPq5O9ExUKSQneFDosngbWyz/BtXVqx2I50HFMxv483XY/DNCFYrTJzmzoGDcQXmZ6tYeb1XLJZUVFuvXaAgQ56tjoerK0fDQhm2ZTPXo6MNbd6uVpPGRYpyNTISgJNXrtB/03pDm5mNmxN84zqjdmx1WOzJrXKDsnR9rx3uHm6cOnKGKb3ncjMiMsm2b8zqQdCRs3w17VsHR/l4KjcqT7f3O+Du4c6pw6eZ3HMWNyNuPVQbFxcLfaZ1o0z1EgDs2/Qbc4YuAqBqkwDemNeb0DNh8dsZVGsUt64n/fnJk0FDY//BiSDoOhA273B2JI5n5twhbed/5SqMnuDBh+9F8/WCKHLntDF9truhzS+/urBgqTszJkWx5PMonqti44NJHgBs/N6Vf85YWDo37rkDB13ZsiP1fLVk8fTkw7oN6b1hLXUXfsGZ8HDefLZ6onYBOXPR/9sNNF26kKZLFybqBPUMqETF3LkdFXaK8M3mw+DPujPmhU/oHjCMi6dC6Tb6+UTt8hbLyYcbhlK9ZSUnRPl4fLP58MbnvRj9/BReKTWIC6dCeOWDjg/dps6LNchbNCevlh9CrwpDKVOjBNXbVAHg6WeK8tXk9bxWcVj8I7V2gix2xz2czSnfVuPHj3fG2z62JauhbRNoUMvZkTiemXOHtJ3//3525eliNvLliftGatPCyqYtrtgTfEH9ecyFShWsZPeLW65d3cqPe1yIiQGbFW7dshATA9ExEBsDHh5OSOQRVc+Xn8PBFwkKvwrAosMHaVGshKGNh6srJf386VmhEt92eokZjZuRK4NP/PNVcuehRv4CLDl80KGxJ7eAwFL8tf8k508EA7D+860EPv9MonbNe9Zl05c72Llqn6NDfGwV6pXhr19OcP7viwCsn/U9gR2rPXQbV1cX0nunxz2dO+7p3HDzcCMmMgaI6wiVq12Smfs/ZNK2UZSuVtyBmcmjSvGhsbfeeivRuq1btxIeHg7AuHHjUjqEZDNiQNy/P/3i3Dicwcy5Q9rOPzjUQnb/O70efz87N25YuHGT+OGxUiVsLP/GnQsXLeTMYWfdJldiYiyEX4OmDa38sMOVxu3SY7VClYo2ajxrc1I2/13ODBm5cD0ifvni9Qh80qUjg4dH/PCYv7c3e86eZvKeXRy7fIkeARWZ1awlzZYuxN/bm5E1avPymm/oVLqMs9JIFn55shB27nL8cui5y3j7euHlk94wPPbp4IUABNQp5fAYH5dfnqyEnr0Uvxx69tLtHD3jh8fu12bz/O1Ub1OFJf/MwNXNlf3fH+J/Gw4AcO3SdbYt+4kfv9lLyeeK8d7Xb9CrwlDDZ5pqPAGVGkdJ8YpQpkyZ2L59O8WLur08PgAAIABJREFUF6dy5cpUrlwZLy+v+P+LiHPZbSQ5n8c1wbdD+TI2erwUy5CRHrz0ajosFvDNaMfNDebMdyNzJjvffRPJhhWRXIuARStSz/RDFwvY7Ym/9a22O525s9eu0W3tKo5djvvhOOfAL+Tz9aWAbyamNmzC2B+3E3rzhsNiTikuLhaS+CiwWlNPx/ZBLC4uSeZoS5Dj/dq8OKIt4WERtM/9Kp0K9MYnSwbaDGgCwOjnJ/PjN3sBOPLTX/yx5xgBdUunSB5mtW7dOho3bkz9+vVZvHjxPdtt376dwMDAh9pmineEhg4dyuTJk9m4cSO5cuWiVatW+Pr60qpVK1q1apXSby8iD5A9u53QsDs9odBQCxl97Hh63mlz4yYElLOxaHYUC2ZFUfM5KwC+GWHbj640b2TF3R0yZIAmDazs/zX1zBE6FxGBf4YM/2/vvsOjKvM2jt+TTAKEFkoKxRWlRRYJRekrEBGEUFdUZF9CL9JWFgvL0qRJ3UhxFVBRBBRQQUQJ1RdxEwuoRCyAFKUEEoiQRFJn5v0j+0bPJkDQ5Bwy5/u5rrkuzsmTOb+HQ8Jv7vOcmbztkHLldCkjXek5OXn7wqpUVa8w4+UyhxwKKltWt1QI1D/+1F5bH+mvfg3DFVmvvp65t5Np9RelxFPJqhIamLddtXolpSanKfNK1jW+q2RJOnVBVapVytuuWqOyUpLTlHEls1Bj2vZqru2rPlBOtktXUtK187UPFd7+jypbMUB9n+plPJjDIVe2q9jnZBfnz59XdHS01q1bp82bN2v9+vX6/vvv8427cOGC5s2bV+jnNeW3VatWrbR8+XKtW7dO8+bNk8vFPwzgZtHyLpcOfeujH0/nNkNvveure9oYf0aTLjg08rFSSvtP6PHyGj91inDJ4ZDC6rq16399JUk5OdKHsb5q2KDkJAgf/XhSTUKrqVbF3AbgL3eGa9fxY4Yxbo9H0+6JUM0KFSRJ/3NnuL67kKTPzp5R21Ur8hZQrzt0UO8dOay/795h+jyKwoE9XymseW1Vrx0iSYocEqG4976wuKqidWBnvO5oUUfV64RKkroN76i4d/cXeszRL0/ongdz1035On3VqlszfffJUaWnpqvHo53UtnfulY7ajWsp7O7a+mx7CV035jHxUUixsbFq2bKlAgMDFRAQoM6dOysmJibfuMmTJ2vMmDGFfl7T8uvAwEAtXrxYGzdu1OHDh806LIDrqFxJmvpkliZO81d2jlSzukfT/56lbw47NGuBv9a9mKlaf/BoQL9sDRpVSm6P1LihW0/8NXeB6PjR2VqwxE99okrJx0dq3tStqL451znqzeNierqe3Lldz3XtLj9fX/14+ZIm7IjRncEheubeTur2+ms6knxR0/fu0cruveXrcOhcWpoei3nP6tKL3OWkVC0a+aKmrBkjp79TCccTtWD4CtVtUkvjnxusUa2nWl3i73YpKUULh76gKevHy8/PqbPHz2vBoOdUt9nt+tvy4Xr0rolXHSNJL0xYrTGLB+mlrxbJ5XLryw8OacPCLXK7PZr2wEKNfnagoqY+KJfLpdn9FivlYup1KkJKSopSUlLy7a9QoYIq/OfFhyQlJiYqKCgobzs4OFjx8fGG71m9erUaNGig8PDwQh/f4Sno4vhNzn2untUlAJZIc5fMW3GLQuM3/2p1CZaqOyn++oO8mCcz8/qDvNiO7DdMPV7jsdGmHWtIPaeWLVuWb/+YMWM0duzYvO3nn39emZmZeuyx3LtXNmzYoEOHDmnGjBmSpCNHjmjGjBl65ZVXdO7cOUVFRWnPnuu/p1fJWdEIAAC8zoABAwpcM/zrNEiSQkNDtX//L5cxk5KSFBwcnLcdExOjpKQkPfDAA8rOzlZiYqL69eundevWXfP4NEIAAMDIxGtF/30J7Gpat26tpUuXKjk5WWXKlNGOHTs0c+bMvK+PGzdO48aNkySdPn1aUVFR122CJN5ZGgAAlAAhISEaP368oqKi1KtXL3Xr1k2NGjXSsGHD9NVXX/3m5yURAgAABjfDR18UpHv37urevbth38qVK/ONq1mzZqHWB0kkQgAAwMZIhAAAgNFNmggVBxIhAABgWyRCAADA4GZdI1QcSIQAAIBtkQgBAAAjEiEAAADvRyIEAACMSIQAAAC8H40QAACwLS6NAQAAA26fBwAAsAESIQAAYEQiBAAA4P1IhAAAgIHDY59IiEQIAADYFokQAAAwsk8gRCIEAADsi0QIAAAY8D5CAAAANkAiBAAAjEiEAAAAvB+JEAAAMGCNEAAAgA2QCAEAACMSIQAAAO9HIwQAAGyLS2MAAMCAxdIAAAA2QCKEEuWrrHSrS7DU3449aHUJlrntzjPyzAq2ugxYxFGqlNUl2AuJEADcXGiCABQHEiEAAGDAGiEAAAAbIBECAABGHvtEQiRCAADAtkiEAACAAWuEAAAAbIBECAAAGJEIAQAAeD8SIQAAYOBwW12BeUiEAACAbZEIAQAAI9YIAQAAeD8aIQAAYFtcGgMAAAa8oSIAAIANkAgBAAAjPnQVAADA+5EIAQAAA9YIAQAA2ACJEAAAMCIRAgAA8H4kQgAAwIA1QgAAADZAIgQAAIx4HyEAAADvRyIEAAAMWCMEAABgAyRCAADAiEQIAADA+9EIAQAA2+LSGAAAMGCxNAAAgA2QCAEAACO3fSIhGqEb5PFIf39Gqne7NLiv1dWYy5vn/vknPlr3kr+ys6Vbb3Nr5IQsBZQ1jtm22amYd5zy95dq/MGtoWOzVK6CdOVn6flF/jp7ykdut9Tuvhz16ptjzUR+oxZV7tDQ2yPl5+PU8bSzWvjdel1xZeZ9/b6Qu9TnlnZ522WdpRVUKlB9Y5/Wz65Mjav3Z4WV/4McDoe+TflBS468rSx3thVT+U1atKitoUPbyd/PV8ePJ2nBwvd15UqWYUzHjn/Uww+1kMfjUWZmtpYu26UjR85Jknr0aKLIruHy93fqyNFzWrhwm7KzXVZM5Xdr3jlcg55+UH7+Tp34+pSiR72kK6kZBY59fPkwnfz6tN5css3kKouHneduZ1wauwHHTkqDxks79lpdifm8ee4pl6R/LSylCVMztXhVhoKrebTuJT/DmENf+uid9U5NnZ+pBcsz1LS5S8uf9ZckvfGKn6pU9WjRygw9syxDO7c6deSbkvOjVdGvrJ4I66vph17RwE/mKiE9WUNrdzOM2Xl+v0bsX6QR+xdp1IFo/ZSVqqVH39ZP2Wn6y60d5evw0bDPFmrYpwtUysdP/W6916LZ3LiKFcvoySe6avr0TRowcKXOJlzSsKHtDWNuqVlZI4Z30FMT12v4iFVasyZWT0/vLUn6U9t66t2rmR5/4g0NHvKiSvn7qc8Dd1swk9+vYtXymvDCUM38y1INbTpR504kafCMh/KNu6V+Nc177yn9qVfJnGdB7Dz3AnlMfFjMlN/W8fHxeX+Oi4vT3LlztXDhQh08eNCMwxeZdZulPpFS5/ZWV2I+b577wQO+ql3PrWo1c38iO3XP0b7dTsNH7Rw/6qM7m7hVJSh3Z/O2Lh342Fc52dKgUdnqPyI3/biU7FB2tkMBZW+Cn+5CuqtyfR1OPaUz6RckSVvO/lv3hjS96vi+f4jQT1lp2no2TpIUf+mY1p7cJY88csuj79POKKRUJVNqLwp33XWbDh9O0JkzP0mStmz5Qvfe28AwJivbpYWLtik5+WdJ0uEj51S5cjk5nT66r1NDbXzzU6WmZsjjkaKfjdHOXYdMn0dRaBrRUIcPHNfZY+clSVtf3KOIh1rlG9djeEfFvLJXH2761OwSi42d5253pjRC06ZNkyStXbtWc+bMUWhoqKpWraqpU6dqzZo1ZpRQJKY8JnW7z+oqrOHNc7+Y5FCVIHfedpUgj9KvOJR+5ZcxdcPcOvSlj5LOOyRJ/7vdqZxsh1JTJIdD8vWVlsz114RhpdWgkUvVa5acRiioVKCSMi7lbSdlXlY5ZxkF+JbKN7aCX1k9eEt7Pf/95rx9B346otPpSZKk4FKV9Oea92hvUsl5kRMcVEGJSal520lJKSpXrrQCAvzz9p0/f1mffHIsb/vRRyMUG3dUOTlu1axZWYGBZTX3mYe0cuVgDRjQVmlpmSqJgmpW1oUzyXnbSWeSVbZigALKlzaMe27Ca/pg48dml1es7Dz3gjg85j2sZmp+v2HDBq1evVoDBw7UwIEDtXbt2hLVCME7ud25zcx/8/nVT8cdd7r1YP9sLZxeShNHlZLDRypX3iPnr66gjZuYpZfeStfPqQ69ucYv/xPepHwcDnkKyKfdBXz6dLfqLRV74ZASMpLzfa1uuZp6tukYvXPmI3188ZtiqbU4OHwcBX7StruAxaKlS/tp2tReqlG9khYuzF0b4vT1UbNmtTRj5mY9+ugrqlC+jIYMvqfY6y4OPj6OAj903OVy59/pZew8d7szpRHKycmR2+1WYGCg/P1/eZXl7+8vH5+Ss5YC3qlqsEc/XfylE0q+4FDZ8h6VLvPLmPQrUoNGbs17PkNz/5Wpu1vnLoQtV1768jMfJV/I/f7SZaQ2HXJ0/PuS8+86MeOSqvhXzNuu6l9RKdlXlOHOyje2fXATxZz7LN/+DsGNNb/xSL147D2t+2F3sdZb1BITU1SlSvm87aCq5ZWSkq6MDONi7+DgClq6pL9cbrf+NuF1/fxzbupz8WKaPtp3RFeuZCknx62du75WgwY1TJ1DUUk8lawqoYF521WrV1Jqcpoyr+T/t+Bt7Dz3Ank85j0sZspv68DAQLVv314nTpzQzJkzJeWuFerbt6/uv/9+M0oAriq8mUtHv/VVwuncZmbnVqfubmW84+eniw5Nf7yUruQuEdHb65xq0yFHDocU96FTb67JXVOUnSXF7XWqYeOSc8fQ/uTDalDxVtUoU1WS1L1Ga8VeyL/GpZyzjKqXqaKvL58w7G9VpYFG1+2tpw6+oD2Jn5tSc1Hav/+E7mhQXTVq5K5r6t69iWJjjxrGlCnjr38u6qd9+w5r1qwtysr65a7ADz88rHbtw+Tvn3sTbts2dXX4cIJ5EyhCB/Z8pbDmtVW9dogkKXJIhOLe+8Liqsxh57nbnSm3z7/22muSpOPHjyslJUVSbho0btw4tW/f3owSgKuqWEl69PFM/XNmKeVkSyHVPRrzZKaOHfbRC//014LlGap+i0e9Hs7RP8aWltsjhTV0a8iY3FeKUSOytHKxvx4fnruW4O42LnXtXXJun7+Unab5376haQ0HyunwVUL6Bc399nXVK19TE+o/rBH7F0mSapSpquTMVLk8xksFI+r0kEMOTaj/cN6+ry+f0JKjb5s6j9/q0qUrWjD/PU2f1ltOp4/OJlzS3LlbVa9eqB6f0EXDR6xSr15NFRJSQW3b1lPbtvXyvvfxJ17XO1s+V/nypfXCCwPl6+PQ0aPn9fwLMRbO6Le7nJSqRSNf1JQ1Y+T0dyrheKIWDF+huk1qafxzgzWq9VSrSyw2dp57QW6GtTtmcXg8N0EudYPc5+pdfxC80ldZ6VaXYKm/HXvQ6hIs45kVbHUJlvL75FurS4CFtqe9aurxOnSeZ9qxPtj+lGnHKghvqAgAAIxKXETy25WcFZ0AAABFjEQIAAAYOEreqpnfjEQIAADYFo0QAACwLS6NAQAAIxu9oTaJEAAAsC0SIQAAYMBiaQAAABsgEQIAAEb2CYRIhAAAgH2RCAEAACPWCAEAAHg/EiEAAGDgsE8gRCIEAADsi0QIAAAYsUYIAADA+5EIAQAAAwefNQYAAOD9SIQAAIARa4QAAAC8H4kQAAAwsk8gRCIEAADsi0YIAADYFpfGAACAgYPF0gAAAN6PRAgAABiRCAEAAHg/EiEAAGDER2wAAAB4PxIhAABgwF1jAAAANkAiBAAAjEiEAAAAvF+JTIS61GlldQnWsVGXXiCHw+oKLOV0JVldgnV8L1pdAWAfNvq/hkQIAADYFo0QAAAwcpv4uAHvvvuuunbtqk6dOmnt2rX5vr5r1y717NlTPXr00KhRo3T58uXrPieNEAAAuOmdP39e0dHRWrdunTZv3qz169fr+++/z/t6Wlqapk+frhUrVmjLli2qX7++li5det3npRECAAAGDo/HtEdhxcbGqmXLlgoMDFRAQIA6d+6smJiYvK9nZ2dr2rRpCgkJkSTVr19fCQkJ133eErlYGgAAeIeUlBSlpKTk21+hQgVVqFAhbzsxMVFBQUF528HBwYqPj8/brlSpku677z5JUkZGhlasWKH+/ftf9/g0QgAAwDKvvvqqli1blm//mDFjNHbs2Lxtt9stx6/uHPZ4PIbt/5eamqrRo0crLCxMvXv3vu7xaYQAAICRibfPDxgwoMCG5ddpkCSFhoZq//79edtJSUkKDg42jElMTNSQIUPUsmVLTZo0qVDHpxECAACW+e9LYFfTunVrLV26VMnJySpTpox27NihmTNn5n3d5XJp5MiR6tKli0aNGlXo49MIAQAAo5vwDRVDQkI0fvx4RUVFKTs7W3369FGjRo00bNgwjRs3TufOndM333wjl8ul7du3S5IaNmyo2bNnX/N5HR7PTTjb6+hcboDVJVin5J2uomXzd5aW6wbfdMOb+HKTK+xre9qrph7v/kaTTTtWTPws045VEBIhAABgZKMX3bzEAgAAtkUiBAAAjGx0FZ5ECAAA2BaJEAAAMLiRj74o6UiEAACAbZEIAQAAIxIhAAAA70ciBAAAjNwkQgAAAF6PRAgAABixRggAAMD70QgBAADb4tIYAAAw4tIYAACA9yMRAgAARiRCAAAA3o9ECAAAGPGGigAAAN6PRAgAABh53FZXYBoSIQAAYFskQgAAwIi7xgAAALwfiRAAADCy0V1jNEJX0bxzuAY9/aD8/J068fUpRY96SVdSMwoc+/jyYTr59Wm9uWSbyVUWn+adwzVoxoPy8/fTiUOnFD3qxavPf8Vwnfz6lN5c7B3zt8O5b35/uAbNeEh+pf5zfkeuzDfHq40pX6msxi4ZqNsb3aqMnzO147UPteX5nZKkFl2b6ImVw5V46mLe80zoOEvpaQX//d2M7HD+r8XO87fz3O2MS2MFqFi1vCa8MFQz/7JUQ5tO1LkTSRo846F8426pX03z3ntKf+p1twVVFp+KVctrwvJhmtlvqYY2eUrnTiZq8IyH8427pX51zXt/olfN3w7nPvf8DtfMR5ZoaPiTOnciUYNnPlzoMSPm/0XpaZka3uQpPdZuuu7uFK4WXRpLkhq0rKs3n31fo1pOznuUpCbIDuf/Wuw8fzvPvUAej3kPi5nWCO3bt08pKSmSpM2bN2vGjBl66623zDr8DWka0VCHDxzX2WPnJUlbX9yjiIda5RvXY3hHxbyyVx9u+tTsEotV03v/a/4r9yji4YLmf6/Xzd8O575pxzuNc1yxWxF9Wxd6TN0mt2n3uo/kdnuUk+3SpzFfqm3v5pJyG6HG7Rvo+U9ma9GuyWrYpr6JM/v97HD+r8XO87fz3O3OlEZo9uzZWr58uTIzM/Xss89qy5YtqlOnjnbu3KlZs2aZUcINCapZWRfOJOdtJ51JVtmKAQooX9ow7rkJr+mDjR+bXV6xC6pZRRdOF3L+G+LMLq9Y2eHcB9WsrAunf7l0VdAcrzXmu8+O6d5+beXr9FXpsqXUttfdqhxaUZKUkpyqrS/u0aMt/qGXp27QtPV/VdUalcyb3O9kh/N/LXaev53nXiAbJUKmrBGKjY3Vli1b5Ovrq71792r9+vXy9/fXww8/rG7duplRwg3x8XEUeG5cLnu8wZSPwyFPAX8Bdpi/Hc597vnNv9/l8hRqzIqJ6zTsmUf0r49n6afzl/T57kNq0LKuJGlm3yV5Y7+OPaJvPv5eTSMaasdr+4p8HsXBDuf/Wuw8fzvP3e5MSYRKly6tixdzX12GhobqypUrkqT09HQ5nTffeu3EU8mqEhqYt121eiWlJqcp80qWhVWZJ/H0RVWp9sureDvN3w7nPvHURVWp9qs51vj/OWYWakxAhTJ66R9vaMRdf9fEyHlyOKSzx8+rbMUA9X2iu+FYDoeUk+0q/kkVETuc/2ux8/ztPHe7M6URGj16tPr06aN58+apZs2a6t+/v+bMmaOHHnpIgwYNMqOEG3Jgz1cKa15b1WuHSJIih0Qo7r0vLK7KPAd2/9f8h0Yo7r3PLa7KHHY49wd2H1JY8zq/Or/3Km7r54Ue021ohKKmPiBJCgyuoPsHtdcH62OVnpqu7iM7qm2vuyRJtcNvVf27amv/znizpva72eH8X4ud52/nuReIS2NFKyIiQnXr1tWuXbv0ww8/qHHjxipbtqzmzp2rRo0amVHCDbmclKpFI1/UlDVj5PR3KuF4ohYMX6G6TWpp/HODNar1VKtLLFa581+pKWvHyunnVMKJRC0Ytlx1m9ym8f8arFGtplhdYrGxw7m/nJSiRSNWasq6cXL6++bOcehy1W16m8b/a4hGtZx81TGS9MaCd/XkyyO1fP8zcjik1TPe0pEDJyRJ0x98VqP+2V/9Jz8gV45Lc/ovU8rFNCune0PscP6vxc7zt/Pc7c7hKWgxyE2uc7kBVpdgnZJ3uoqWw2F1Bday83oFX97tA/a1Pe1VU4/Xpdpo0461LeE5045VEH6zAAAA27r5VioDAABr2ejqA4kQAACwLRIhAABgRCIEAADg/UiEAACAkZtECAAAwOuRCAEAAAOPxz7vWUYiBAAAbItECAAAGLFGCAAAwPuRCAEAACPeRwgAAMD70QgBAADb4tIYAAAwcnP7PAAAgNcjEQIAAEYslgYAAPB+JEIAAMDAwxohAAAA70ciBAAAjFgjBAAA4P1IhAAAgBEfugoAAOD9SIQAAICRh7vGAAAAvB6JEAAAMPCwRggAAMD7kQgBAAAj1ggBAAB4PxohAABgW1waAwAABiyWBgAAsAESIQAAYGSjxdIOj8dGHzELAADwK1waAwAAtkUjBAAAbItGCAAA2BaNEAAAsC0aIQAAYFs0QgAAwLZohAAAgG3RCAEAANuiEQIAALZFI3QD3n33XXXt2lWdOnXS2rVrrS7HdGlpaerWrZtOnz5tdSmmW7ZsmSIjIxUZGan58+dbXY7pFi9erK5duyoyMlKrVq2yuhzLzJs3TxMnTrS6DNP1799fkZGR6tmzp3r27KmDBw9aXZKp9uzZoz//+c/q0qWLZs2aZXU5KGJ81lghnT9/XtHR0Xr77bfl7++vvn37qkWLFqpTp47VpZni4MGDmjx5sk6ePGl1KaaLjY3VRx99pE2bNsnhcGjo0KHauXOn7rvvPqtLM8Wnn36qjz/+WFu2bFFOTo66du2qdu3a6fbbb7e6NFPFxcVp06ZNat++vdWlmMrj8ejkyZP64IMP5HTa77+MU6dOadq0adq4caOqVKmiAQMGaO/evWrXrp3VpaGIkAgVUmxsrFq2bKnAwEAFBASoc+fOiomJsbos02zYsEHTpk1TcHCw1aWYLigoSBMnTpS/v7/8/PxUu3ZtnT171uqyTNO8eXOtXr1aTqdTFy9elMvlUkBAgNVlmerSpUuKjo7WyJEjrS7FdMePH5ckDR48WD169NCaNWssrshcO3fuVNeuXRUaGio/Pz9FR0crPDzc6rJQhOzX3v9GiYmJCgoKytsODg5WfHy8hRWZa/bs2VaXYJm6devm/fnkyZPatm2bXn/9dQsrMp+fn5+WLFmil19+Wffff79CQkKsLslUU6dO1fjx45WQkGB1KaZLSUlRq1atNGXKFGVnZysqKkq33Xab2rRpY3Vppvjhhx/k5+enkSNHKiEhQe3bt9djjz1mdVkoQiRCheR2u+VwOPK2PR6PYRve7+jRoxo8eLCefPJJ1apVy+pyTDdu3DjFxcUpISFBGzZssLoc02zcuFHVqlVTq1atrC7FEk2aNNH8+fNVvnx5Va5cWX369NHevXutLss0LpdLcXFxmjNnjtavX6/4+Hht2rTJ6rJQhGiECik0NFRJSUl520lJSba8TGRXBw4c0MCBAzVhwgT17t3b6nJMdezYMX377beSpDJlyqhTp046fPiwxVWZ5/3339e///1v9ezZU0uWLNGePXs0Z84cq8syzf79+xUXF5e37fF4bLVWqGrVqmrVqpUqV66s0qVLq2PHjra6GmAHNEKF1Lp1a8XFxSk5OVnp6enasWOH7rnnHqvLggkSEhI0evRoLVy4UJGRkVaXY7rTp09r8uTJysrKUlZWlnbv3q1mzZpZXZZpVq1apa1bt+qdd97RuHHjFBERoUmTJlldlmlSU1M1f/58ZWZmKi0tTZs2bbLNjQKS1KFDB3300UdKSUmRy+XSvn379Mc//tHqslCE7NPW/04hISEaP368oqKilJ2drT59+qhRo0ZWlwUTvPTSS8rMzNTcuXPz9vXt21ePPPKIhVWZp127doqPj1evXr3k6+urTp062bIhtKsOHTro4MGD6tWrl9xut/r166cmTZpYXZZpwsPDNXToUPXr10/Z2dlq06aNHnjgAavLQhFyeDwej9VFAAAAWIFLYwAAwLZohAAAgG3RCAEAANuiEQIAALZFIwQAAGyLRggAANgWjRAAALAtGiEAhdKlSxfdc889Onr0qNWlAECRoRECUChbt25VrVq1tH37dqtLAYAiQyMEoFB8fX3VrFkzW33gKgDvx2eNASiUjIwMvf/+++JTeQB4ExIhAIUSHR2t4OBg/fjjj/r555+tLgcAigSNEIDr+uKLL7Rt2zYtXbpU5cuXZ8E0AK9BIwTgmjIzMzVp0iQ9/fTTCgwMVFhYmL777jurywKAIkEjBOCaFi9erMaNG6tDhw6SpLCwMBZMA/AaNEIArio+Pl4xMTGaNGlS3r477riDRAiA13B4uAUEAADYFIkQAACwLRohAABgWzRCAADAtmiEAACAbdEIAQAA26IRAgAAtkXmDH3CAAAAEklEQVQjBAAAbItGCAAA2Nb/AQa+WWHyXw/6AAAAAElFTkSuQmCC\n",
- "text/plain": [
- ""
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- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "execution_count": 20,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
"source": [
"# optional\n",
"# visual representation of grid search\n",
@@ -2041,7 +1676,9 @@
{
"cell_type": "code",
"execution_count": 21,
- "metadata": {},
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"\n",
@@ -2760,30 +2397,11 @@
},
{
"cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "inputs = (n_inputs, pixel_width, pixel_height, depth) = (1797, 8, 8, 1)\n",
- "labels = (n_inputs) = (1797,)\n"
- ]
- },
- {
- "data": {
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- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "execution_count": 22,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
"source": [
"# import necessary packages\n",
"import numpy as np\n",
@@ -2836,8 +2454,10 @@
},
{
"cell_type": "code",
- "execution_count": 7,
- "metadata": {},
+ "execution_count": 23,
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"from tensorflow.keras import datasets, layers, models\n",
@@ -2874,8 +2494,10 @@
},
{
"cell_type": "code",
- "execution_count": 8,
- "metadata": {},
+ "execution_count": 24,
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"def create_convolutional_neural_network_keras(input_shape, receptive_field,\n",
@@ -2915,261 +2537,11 @@
},
{
"cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "360/360 [==============================] - 0s 285us/sample - loss: 2.6906 - accuracy: 0.1250\n",
- "Learning rate = 1e-05\n",
- "Lambda = 1e-05\n",
- "Test accuracy: 0.125\n",
- "\n",
- "360/360 [==============================] - 0s 286us/sample - loss: 2.8650 - accuracy: 0.1000\n",
- "Learning rate = 1e-05\n",
- "Lambda = 0.0001\n",
- "Test accuracy: 0.100\n",
- "\n",
- "360/360 [==============================] - 0s 283us/sample - loss: 2.9532 - accuracy: 0.1111\n",
- "Learning rate = 1e-05\n",
- "Lambda = 0.001\n",
- "Test accuracy: 0.111\n",
- "\n",
- "360/360 [==============================] - 0s 271us/sample - loss: 4.0025 - accuracy: 0.1194\n",
- "Learning rate = 1e-05\n",
- "Lambda = 0.01\n",
- "Test accuracy: 0.119\n",
- "\n",
- "360/360 [==============================] - 0s 272us/sample - loss: 13.7496 - accuracy: 0.0694\n",
- "Learning rate = 1e-05\n",
- "Lambda = 0.1\n",
- "Test accuracy: 0.069\n",
- "\n",
- "360/360 [==============================] - 0s 272us/sample - loss: 91.1848 - accuracy: 0.1222\n",
- "Learning rate = 1e-05\n",
- "Lambda = 1.0\n",
- "Test accuracy: 0.122\n",
- "\n",
- "360/360 [==============================] - 0s 274us/sample - loss: 525.6322 - accuracy: 0.0500\n",
- "Learning rate = 1e-05\n",
- "Lambda = 10.0\n",
- "Test accuracy: 0.050\n",
- "\n",
- "360/360 [==============================] - 0s 275us/sample - loss: 1.8135 - accuracy: 0.3722\n",
- "Learning rate = 0.0001\n",
- "Lambda = 1e-05\n",
- "Test accuracy: 0.372\n",
- "\n",
- "360/360 [==============================] - 0s 270us/sample - loss: 1.5110 - accuracy: 0.5139\n",
- "Learning rate = 0.0001\n",
- "Lambda = 0.0001\n",
- "Test accuracy: 0.514\n",
- "\n",
- "360/360 [==============================] - 0s 270us/sample - loss: 1.3332 - accuracy: 0.6083\n",
- "Learning rate = 0.0001\n",
- "Lambda = 0.001\n",
- "Test accuracy: 0.608\n",
- "\n",
- "360/360 [==============================] - 0s 270us/sample - loss: 2.1509 - accuracy: 0.6306\n",
- "Learning rate = 0.0001\n",
- "Lambda = 0.01\n",
- "Test accuracy: 0.631\n",
- "\n",
- "360/360 [==============================] - 0s 273us/sample - loss: 10.4007 - accuracy: 0.4444\n",
- "Learning rate = 0.0001\n",
- "Lambda = 0.1\n",
- "Test accuracy: 0.444\n",
- "\n",
- "360/360 [==============================] - 0s 265us/sample - loss: 54.8139 - accuracy: 0.4222\n",
- "Learning rate = 0.0001\n",
- "Lambda = 1.0\n",
- "Test accuracy: 0.422\n",
- "\n",
- "360/360 [==============================] - 0s 272us/sample - loss: 4.6303 - accuracy: 0.1389\n",
- "Learning rate = 0.0001\n",
- "Lambda = 10.0\n",
- "Test accuracy: 0.139\n",
- "\n",
- "360/360 [==============================] - 0s 269us/sample - loss: 0.3255 - accuracy: 0.9056\n",
- "Learning rate = 0.001\n",
- "Lambda = 1e-05\n",
- "Test accuracy: 0.906\n",
- "\n",
- "360/360 [==============================] - 0s 758us/sample - loss: 0.2759 - accuracy: 0.9222\n",
- "Learning rate = 0.001\n",
- "Lambda = 0.0001\n",
- "Test accuracy: 0.922\n",
- "\n",
- "360/360 [==============================] - 0s 274us/sample - loss: 0.3967 - accuracy: 0.9000\n",
- "Learning rate = 0.001\n",
- "Lambda = 0.001\n",
- "Test accuracy: 0.900\n",
- "\n",
- "360/360 [==============================] - 0s 271us/sample - loss: 1.1899 - accuracy: 0.9139\n",
- "Learning rate = 0.001\n",
- "Lambda = 0.01\n",
- "Test accuracy: 0.914\n",
- "\n",
- "360/360 [==============================] - 0s 272us/sample - loss: 5.7756 - accuracy: 0.9556\n",
- "Learning rate = 0.001\n",
- "Lambda = 0.1\n",
- "Test accuracy: 0.956\n",
- "\n",
- "360/360 [==============================] - 0s 266us/sample - loss: 2.6194 - accuracy: 0.5250\n",
- "Learning rate = 0.001\n",
- "Lambda = 1.0\n",
- "Test accuracy: 0.525\n",
- "\n",
- "360/360 [==============================] - 0s 274us/sample - loss: 2.3033 - accuracy: 0.0889\n",
- "Learning rate = 0.001\n",
- "Lambda = 10.0\n",
- "Test accuracy: 0.089\n",
- "\n",
- "360/360 [==============================] - 0s 271us/sample - loss: 0.0663 - accuracy: 0.9833\n",
- "Learning rate = 0.01\n",
- "Lambda = 1e-05\n",
- "Test accuracy: 0.983\n",
- "\n",
- "360/360 [==============================] - 0s 273us/sample - loss: 0.0886 - accuracy: 0.9778\n",
- "Learning rate = 0.01\n",
- "Lambda = 0.0001\n",
- "Test accuracy: 0.978\n",
- "\n",
- "360/360 [==============================] - 0s 272us/sample - loss: 0.1853 - accuracy: 0.9694\n",
- "Learning rate = 0.01\n",
- "Lambda = 0.001\n",
- "Test accuracy: 0.969\n",
- "\n",
- "360/360 [==============================] - 0s 270us/sample - loss: 0.6787 - accuracy: 0.9806\n",
- "Learning rate = 0.01\n",
- "Lambda = 0.01\n",
- "Test accuracy: 0.981\n",
- "\n",
- "360/360 [==============================] - 0s 270us/sample - loss: 0.9998 - accuracy: 0.9056\n",
- "Learning rate = 0.01\n",
- "Lambda = 0.1\n",
- "Test accuracy: 0.906\n",
- "\n",
- "360/360 [==============================] - 0s 265us/sample - loss: 2.3065 - accuracy: 0.0778\n",
- "Learning rate = 0.01\n",
- "Lambda = 1.0\n",
- "Test accuracy: 0.078\n",
- "\n",
- "360/360 [==============================] - 0s 269us/sample - loss: 2.3067 - accuracy: 0.0778\n",
- "Learning rate = 0.01\n",
- "Lambda = 10.0\n",
- "Test accuracy: 0.078\n",
- "\n",
- "360/360 [==============================] - 0s 271us/sample - loss: 0.1100 - accuracy: 0.9722\n",
- "Learning rate = 0.1\n",
- "Lambda = 1e-05\n",
- "Test accuracy: 0.972\n",
- "\n",
- "360/360 [==============================] - 0s 269us/sample - loss: 0.0998 - accuracy: 0.9806\n",
- "Learning rate = 0.1\n",
- "Lambda = 0.0001\n",
- "Test accuracy: 0.981\n",
- "\n",
- "360/360 [==============================] - 0s 272us/sample - loss: 0.2629 - accuracy: 0.9556\n",
- "Learning rate = 0.1\n",
- "Lambda = 0.001\n",
- "Test accuracy: 0.956\n",
- "\n",
- "360/360 [==============================] - 0s 272us/sample - loss: 0.3039 - accuracy: 0.9639\n",
- "Learning rate = 0.1\n",
- "Lambda = 0.01\n",
- "Test accuracy: 0.964\n",
- "\n",
- "360/360 [==============================] - 0s 269us/sample - loss: 1.1311 - accuracy: 0.9083\n",
- "Learning rate = 0.1\n",
- "Lambda = 0.1\n",
- "Test accuracy: 0.908\n",
- "\n",
- "360/360 [==============================] - 0s 270us/sample - loss: 2.3080 - accuracy: 0.0889\n",
- "Learning rate = 0.1\n",
- "Lambda = 1.0\n",
- "Test accuracy: 0.089\n",
- "\n",
- "360/360 [==============================] - 0s 273us/sample - loss: nan - accuracy: 0.0778\n",
- "Learning rate = 0.1\n",
- "Lambda = 10.0\n",
- "Test accuracy: 0.078\n",
- "\n",
- "360/360 [==============================] - 0s 281us/sample - loss: 3.2809 - accuracy: 0.0778\n",
- "Learning rate = 1.0\n",
- "Lambda = 1e-05\n",
- "Test accuracy: 0.078\n",
- "\n",
- "360/360 [==============================] - 0s 277us/sample - loss: 108.7478 - accuracy: 0.0889\n",
- "Learning rate = 1.0\n",
- "Lambda = 0.0001\n",
- "Test accuracy: 0.089\n",
- "\n",
- "360/360 [==============================] - 0s 278us/sample - loss: 2.3687 - accuracy: 0.0917\n",
- "Learning rate = 1.0\n",
- "Lambda = 0.001\n",
- "Test accuracy: 0.092\n",
- "\n",
- "360/360 [==============================] - 0s 281us/sample - loss: 2.3136 - accuracy: 0.0889\n",
- "Learning rate = 1.0\n",
- "Lambda = 0.01\n",
- "Test accuracy: 0.089\n",
- "\n",
- "360/360 [==============================] - 0s 269us/sample - loss: 2.3084 - accuracy: 0.0917\n",
- "Learning rate = 1.0\n",
- "Lambda = 0.1\n",
- "Test accuracy: 0.092\n",
- "\n",
- "360/360 [==============================] - 0s 270us/sample - loss: nan - accuracy: 0.0778\n",
- "Learning rate = 1.0\n",
- "Lambda = 1.0\n",
- "Test accuracy: 0.078\n",
- "\n",
- "360/360 [==============================] - 0s 270us/sample - loss: nan - accuracy: 0.0778\n",
- "Learning rate = 1.0\n",
- "Lambda = 10.0\n",
- "Test accuracy: 0.078\n",
- "\n",
- "360/360 [==============================] - 0s 274us/sample - loss: 224833.6556 - accuracy: 0.0917\n",
- "Learning rate = 10.0\n",
- "Lambda = 1e-05\n",
- "Test accuracy: 0.092\n",
- "\n",
- "360/360 [==============================] - 0s 269us/sample - loss: 5281993.4889 - accuracy: 0.1250\n",
- "Learning rate = 10.0\n",
- "Lambda = 0.0001\n",
- "Test accuracy: 0.125\n",
- "\n",
- "360/360 [==============================] - 0s 270us/sample - loss: 2.4257 - accuracy: 0.1056\n",
- "Learning rate = 10.0\n",
- "Lambda = 0.001\n",
- "Test accuracy: 0.106\n",
- "\n",
- "360/360 [==============================] - 0s 270us/sample - loss: 2.3729 - accuracy: 0.0889\n",
- "Learning rate = 10.0\n",
- "Lambda = 0.01\n",
- "Test accuracy: 0.089\n",
- "\n",
- "360/360 [==============================] - 0s 268us/sample - loss: nan - accuracy: 0.0778\n",
- "Learning rate = 10.0\n",
- "Lambda = 0.1\n",
- "Test accuracy: 0.078\n",
- "\n",
- "360/360 [==============================] - 0s 267us/sample - loss: nan - accuracy: 0.0778\n",
- "Learning rate = 10.0\n",
- "Lambda = 1.0\n",
- "Test accuracy: 0.078\n",
- "\n",
- "360/360 [==============================] - 0s 268us/sample - loss: nan - accuracy: 0.0778\n",
- "Learning rate = 10.0\n",
- "Lambda = 10.0\n",
- "Test accuracy: 0.078\n",
- "\n"
- ]
- }
- ],
+ "execution_count": 25,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
"source": [
"CNN_keras = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)\n",
" \n",
@@ -3198,134 +2570,11 @@
},
{
"cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1437/1437 [==============================] - 0s 28us/sample - loss: 2.7008 - accuracy: 0.1482\n",
- "360/360 [==============================] - 0s 36us/sample - loss: 2.6906 - accuracy: 0.1250\n",
- "1437/1437 [==============================] - 0s 27us/sample - loss: 3.0786 - accuracy: 0.1030\n",
- "360/360 [==============================] - 0s 56us/sample - loss: 2.8650 - accuracy: 0.1000\n",
- "1437/1437 [==============================] - 0s 32us/sample - loss: 2.9438 - accuracy: 0.1120\n",
- "360/360 [==============================] - 0s 33us/sample - loss: 2.9532 - accuracy: 0.1111\n",
- "1437/1437 [==============================] - 0s 31us/sample - loss: 3.9211 - accuracy: 0.1273\n",
- "360/360 [==============================] - 0s 31us/sample - loss: 4.0025 - accuracy: 0.1194\n",
- "1437/1437 [==============================] - 0s 31us/sample - loss: 13.3698 - accuracy: 0.0939\n",
- "360/360 [==============================] - 0s 37us/sample - loss: 13.7496 - accuracy: 0.0694\n",
- "1437/1437 [==============================] - 0s 26us/sample - loss: 91.1213 - accuracy: 0.1441\n",
- "360/360 [==============================] - 0s 37us/sample - loss: 91.1848 - accuracy: 0.1222\n",
- "1437/1437 [==============================] - 0s 28us/sample - loss: 525.5854 - accuracy: 0.0828\n",
- "360/360 [==============================] - 0s 44us/sample - loss: 525.6322 - accuracy: 0.0500\n",
- "1437/1437 [==============================] - 0s 30us/sample - loss: 1.8382 - accuracy: 0.3688\n",
- "360/360 [==============================] - 0s 31us/sample - loss: 1.8135 - accuracy: 0.3722\n",
- "1437/1437 [==============================] - 0s 28us/sample - loss: 1.4643 - accuracy: 0.5498\n",
- "360/360 [==============================] - 0s 44us/sample - loss: 1.5110 - accuracy: 0.5139\n",
- "1437/1437 [==============================] - 0s 28us/sample - loss: 1.2890 - accuracy: 0.6381\n",
- "360/360 [==============================] - 0s 40us/sample - loss: 1.3332 - accuracy: 0.6083\n",
- "1437/1437 [==============================] - 0s 34us/sample - loss: 2.1119 - accuracy: 0.6458\n",
- "360/360 [==============================] - 0s 33us/sample - loss: 2.1509 - accuracy: 0.6306\n",
- "1437/1437 [==============================] - 0s 28us/sample - loss: 10.3785 - accuracy: 0.4711\n",
- "360/360 [==============================] - 0s 49us/sample - loss: 10.4007 - accuracy: 0.4444\n",
- "1437/1437 [==============================] - 0s 32us/sample - loss: 54.7472 - accuracy: 0.4850\n",
- "360/360 [==============================] - 0s 32us/sample - loss: 54.8139 - accuracy: 0.4222\n",
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\n",
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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "execution_count": 26,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
"source": [
"# visual representation of grid search\n",
"# uses seaborn heatmap, could probably do this in matplotlib\n",
@@ -3371,25 +2620,7 @@
]
}
],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "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": "3.6.8"
- }
- },
+ "metadata": {},
"nbformat": 4,
"nbformat_minor": 4
}
diff --git a/doc/src/week41/week41.do.txt b/doc/src/week41/week41.do.txt
index 9eb94a41a..6d3f70ab0 100644
--- a/doc/src/week41/week41.do.txt
+++ b/doc/src/week41/week41.do.txt
@@ -7,7 +7,7 @@ DATE: today
===== Plan for week 41 =====
* Thursday: Building our own Feed-forward Neural Network. "Video of Lecture":"https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureOctober8.mp4?vrtx=view-as-webpage"
-* Friday: Playing around with our own Feed-forward Neural Network and introduction to TensorFlow. Start convolutional Neural Networks (CNN).
+* Friday: Playing around with our own Feed-forward Neural Network and introduction to TensorFlow. Start convolutional Neural Networks (CNN). "Video of Lecture":"https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureOcotber9.mp4?vrtx=view-as-webpage"
Reading suggestions for both days: "Aurelien Geron's chapters 10-11":"https://github.com/CompPhysics/MachineLearning/blob/master/doc/T\
extbooks/TensorflowML.pdf" and Hastie et al chapter 11.