mirror of
https://github.com/TheAlgorithms/Python.git
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328 lines
18 KiB
Plaintext
328 lines
18 KiB
Plaintext
{
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"cells": [
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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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"## Standard (Fully Connected) Neural Network"
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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": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"#Use in Markup cell type\n",
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"#![alt text](imagename.png \"Title\") "
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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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"### Implementing Fully connected Neural Net"
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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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"#### Loading Required packages and 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": 2,
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"metadata": {},
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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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"###1. Load Data and Splot Data\n",
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"from keras.datasets import mnist\n",
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"from keras.models import Sequential \n",
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"from keras.layers.core import Dense, Activation\n",
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"from keras.utils import np_utils\n",
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"(X_train, Y_train), (X_test, Y_test) = mnist.load_data()"
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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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"#### Preprocessing"
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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": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<Figure size 2000x400 with 10 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"n = 10 # how many digits we will display\n",
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"plt.figure(figsize=(20, 4))\n",
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"for i in range(n):\n",
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" # display original\n",
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" ax = plt.subplot(2, n, i + 1)\n",
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" plt.imshow(X_test[i].reshape(28, 28))\n",
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" plt.gray()\n",
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" ax.get_xaxis().set_visible(False)\n",
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" ax.get_yaxis().set_visible(False)\n",
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"plt.show()\n",
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"plt.close()"
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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": 4,
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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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"Previous X_train shape: (60000, 28, 28) \n",
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"Previous Y_train shape:(60000,)\n",
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"New X_train shape: (60000, 784) \n",
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"New Y_train shape:(60000, 10)\n"
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]
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}
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],
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"source": [
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"print(\"Previous X_train shape: {} \\nPrevious Y_train shape:{}\".format(X_train.shape, Y_train.shape))\n",
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"X_train = X_train.reshape(60000, 784) \n",
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"X_test = X_test.reshape(10000, 784)\n",
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"X_train = X_train.astype('float32') \n",
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"X_test = X_test.astype('float32') \n",
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"X_train /= 255 \n",
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"X_test /= 255\n",
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"classes = 10\n",
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"Y_train = np_utils.to_categorical(Y_train, classes) \n",
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"Y_test = np_utils.to_categorical(Y_test, classes)\n",
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"print(\"New X_train shape: {} \\nNew Y_train shape:{}\".format(X_train.shape, Y_train.shape))"
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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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"#### Setting up parameters"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"input_size = 784\n",
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"batch_size = 200 \n",
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"hidden1 = 400\n",
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"hidden2 = 20\n",
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"epochs = 2"
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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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"#### Building the FCN Model"
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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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"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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"_________________________________________________________________\n",
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"Layer (type) Output Shape Param # \n",
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"=================================================================\n",
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"dense_1 (Dense) (None, 400) 314000 \n",
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"_________________________________________________________________\n",
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"dense_2 (Dense) (None, 20) 8020 \n",
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"_________________________________________________________________\n",
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"dense_3 (Dense) (None, 10) 210 \n",
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"=================================================================\n",
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"Total params: 322,230\n",
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"Trainable params: 322,230\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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"###4.Build the model\n",
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"model = Sequential() \n",
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"model.add(Dense(hidden1, input_dim=input_size, activation='relu'))\n",
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"# output = relu (dot (W, input) + bias)\n",
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"model.add(Dense(hidden2, activation='relu'))\n",
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"model.add(Dense(classes, activation='softmax')) \n",
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"\n",
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"# Compilation\n",
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"model.compile(loss='categorical_crossentropy', \n",
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" metrics=['accuracy'], optimizer='sgd')\n",
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"model.summary()"
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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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"#### Training The Model"
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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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"Epoch 1/10\n",
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" - 12s - loss: 1.4482 - acc: 0.6251\n",
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"Epoch 2/10\n",
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" - 3s - loss: 0.6239 - acc: 0.8482\n",
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"Epoch 3/10\n",
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" - 3s - loss: 0.4582 - acc: 0.8798\n",
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"Epoch 4/10\n",
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" - 3s - loss: 0.3941 - acc: 0.8936\n",
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"Epoch 5/10\n",
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" - 3s - loss: 0.3579 - acc: 0.9011\n",
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"Epoch 6/10\n",
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" - 4s - loss: 0.3328 - acc: 0.9070\n",
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"Epoch 7/10\n",
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" - 3s - loss: 0.3138 - acc: 0.9118\n",
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"Epoch 8/10\n",
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" - 3s - loss: 0.2980 - acc: 0.9157\n",
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"Epoch 9/10\n",
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" - 3s - loss: 0.2849 - acc: 0.9191\n",
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"Epoch 10/10\n",
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" - 3s - loss: 0.2733 - acc: 0.9223\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<keras.callbacks.History at 0x272375a7240>"
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]
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},
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"execution_count": 7,
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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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"source": [
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"# Fitting on Data\n",
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"model.fit(X_train, Y_train, batch_size=batch_size, epochs=10, verbose=2)\n",
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"###5.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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"collapsed": true
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},
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"source": [
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"#### Testing The Model"
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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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"10000/10000 [==============================] - 1s 121us/step\n",
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"\n",
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"Test accuracy: 0.9257\n",
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"[0 6 9 0 1 5 9 7 3 4]\n"
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]
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},
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{
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"data": {
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"text/plain": [
|
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"<Figure size 1440x288 with 10 Axes>"
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|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"score = model.evaluate(X_test, Y_test, verbose=1)\n",
|
|
"print('\\n''Test accuracy:', score[1])\n",
|
|
"mask = range(10,20)\n",
|
|
"X_valid = X_test[mask]\n",
|
|
"y_pred = model.predict_classes(X_valid)\n",
|
|
"print(y_pred)\n",
|
|
"plt.figure(figsize=(20, 4))\n",
|
|
"for i in range(n):\n",
|
|
" # display original\n",
|
|
" ax = plt.subplot(2, n, i + 1)\n",
|
|
" plt.imshow(X_valid[i].reshape(28, 28))\n",
|
|
" plt.gray()\n",
|
|
" ax.get_xaxis().set_visible(False)\n",
|
|
" ax.get_yaxis().set_visible(False)\n",
|
|
"plt.show()\n",
|
|
"plt.close()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"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": "3.6.6"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|