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Add comparision model
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‎Chapter 7/Exercise 3.ipynb‎

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"cells": [
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Using TensorFlow backend.\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
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"170500096/170498071 [==============================] - 263s 2us/step\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"(x_train, y_train), (x_test, y_test) = cifar10.load_data()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"(50000, 32, 32, 3)"
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]
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},
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"metadata": {},
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"output_type": "execute_result"
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}
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},
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"cell_type": "code",
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"execution_count": 4,
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"outputs": [
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{
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"(50000, 1)"
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]
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},
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"execution_count": 7,
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"y_test = label_binarizer.transform(y_test)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Model without regularization"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"WARNING:tensorflow:From C:\\Users\\Maedr3\\AppData\\Roaming\\Python\\Python36\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
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"Instructions for updating:\n",
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"Colocations handled automatically by placer.\n",
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"_________________________________________________________________\n",
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"Layer (type) Output Shape Param # \n",
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"=================================================================\n",
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"conv2d_1 (Conv2D) (None, 32, 32, 32) 896 \n",
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"_________________________________________________________________\n",
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"conv2d_2 (Conv2D) (None, 30, 30, 32) 9248 \n",
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"_________________________________________________________________\n",
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"max_pooling2d_1 (MaxPooling2 (None, 15, 15, 32) 0 \n",
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"_________________________________________________________________\n",
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"flatten_1 (Flatten) (None, 7200) 0 \n",
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"_________________________________________________________________\n",
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"dense_1 (Dense) (None, 512) 3686912 \n",
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"_________________________________________________________________\n",
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"dense_2 (Dense) (None, 10) 5130 \n",
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"=================================================================\n",
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"Total params: 3,702,186\n",
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"Trainable params: 3,702,186\n",
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"Non-trainable params: 0\n",
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"_________________________________________________________________\n"
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]
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}
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],
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"source": [
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"model = Sequential()\n",
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" \n",
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"model.add(Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=(32,32,3))) \n",
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"model.add(Conv2D(32, (3, 3), activation='relu')) \n",
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"model.add(MaxPool2D(pool_size=(2, 2)))\n",
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"\n",
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"model.add(Flatten())\n",
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"\n",
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"model.add(Dense(512, activation='relu'))\n",
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"\n",
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"model.add(Dense(10, activation='softmax'))\n",
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"\n",
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"model.summary()\n",
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"\n",
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"model.compile(loss='categorical_crossentropy', \n",
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" optimizer='adam',\n",
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" metrics = ['accuracy'])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"WARNING:tensorflow:From C:\\Users\\Maedr3\\AppData\\Roaming\\Python\\Python36\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
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"Instructions for updating:\n",
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"Use tf.cast instead.\n",
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"Train on 50000 samples, validate on 10000 samples\n",
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"Epoch 1/10\n",
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"50000/50000 [==============================] - 318s 6ms/step - loss: 14.4860 - acc: 0.1003 - val_loss: 14.5063 - val_acc: 0.1000\n",
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"Epoch 2/10\n",
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"50000/50000 [==============================] - 329s 7ms/step - loss: 14.5063 - acc: 0.1000 - val_loss: 14.5063 - val_acc: 0.1000\n",
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"Epoch 3/10\n",
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"50000/50000 [==============================] - 344s 7ms/step - loss: 14.5063 - acc: 0.1000 - val_loss: 14.5063 - val_acc: 0.1000\n",
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"Epoch 4/10\n",
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"50000/50000 [==============================] - 320s 6ms/step - loss: 14.5063 - acc: 0.1000 - val_loss: 14.5063 - val_acc: 0.1000\n",
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"Epoch 5/10\n",
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"50000/50000 [==============================] - 304s 6ms/step - loss: 14.5063 - acc: 0.1000 - val_loss: 14.5063 - val_acc: 0.1000\n",
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"Epoch 6/10\n",
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"50000/50000 [==============================] - 306s 6ms/step - loss: 14.5063 - acc: 0.1000 - val_loss: 14.5063 - val_acc: 0.1000\n",
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"Epoch 7/10\n",
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"50000/50000 [==============================] - 312s 6ms/step - loss: 14.5063 - acc: 0.1000 - val_loss: 14.5063 - val_acc: 0.1000\n",
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"Epoch 8/10\n",
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"50000/50000 [==============================] - 317s 6ms/step - loss: 14.5063 - acc: 0.1000 - val_loss: 14.5063 - val_acc: 0.1000\n",
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"Epoch 9/10\n",
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"50000/50000 [==============================] - 311s 6ms/step - loss: 14.5063 - acc: 0.1000 - val_loss: 14.5063 - val_acc: 0.1000\n",
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"Epoch 10/10\n",
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"50000/50000 [==============================] - 306s 6ms/step - loss: 14.5063 - acc: 0.1000 - val_loss: 14.5063 - val_acc: 0.1000\n"
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]
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}
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],
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"source": [
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"model_details = model.fit(x_train, y_train,\n",
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" batch_size = 512, \n",
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" epochs = 10, \n",
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" validation_data= (x_test, y_test),\n",
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" verbose=1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"10000/10000 [==============================] - 21s 2ms/step\n",
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"Accuracy: 10.00%\n"
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]
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}
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],
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"source": [
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"score = model.evaluate(x_test, y_test)\n",
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"print(\"Accuracy: {0:.2f}%\".format(score[1]*100))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Model with regularization"
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]
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},
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"cell_type": "code",
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"execution_count": 19,
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{
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"cell_type": "code",
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"execution_count": 33,
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"metadata": {},
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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"plt.show()\n",
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"print(\"Prediction: {0}\".format(labels[np.argmax(y_pred[correct_indices[image]])]))"
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]
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},
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {

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