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x1001000/week2.ipynb

Last active February 6, 2019 11:01
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week2.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "week2.ipynb",
"version": "0.3.2",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/x1001000/6b8305f39b48f4e71bb378c87461880d/week2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"metadata": {
"id": "DtxKXfrD6nnS",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# Data"
]
},
{
"metadata": {
"id": "i0Fc3j_p6nnT",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt"
],
"execution_count": 1,
"outputs": []
},
{
"metadata": {
"id": "_ro8Akjd6nnY",
"colab_type": "code",
"outputId": "73c4dfdb-18ce-464b-fe58-eebdec5a3306",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 136
}
},
"cell_type": "code",
"source": [
"from keras.datasets import mnist\n",
"(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
"print(x_train.shape)\n",
"print(y_train.shape)\n",
"print(x_test.shape)\n",
"print(y_test.shape)"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Downloading data from https://s3.amazonaws.com/img-datasets/mnist.npz\n",
"11493376/11490434 [==============================] - 2s 0us/step\n",
"(60000, 28, 28)\n",
"(60000,)\n",
"(10000, 28, 28)\n",
"(10000,)\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "wJTgo9LWKH4J",
"colab_type": "code",
"outputId": "02815b00-b889-401c-97cb-bc48dadb442d",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 364
}
},
"cell_type": "code",
"source": [
"plt.imshow(x_train[5566], cmap='Greys')"
],
"execution_count": 3,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7fe4dec1cf28>"
]
},
"metadata": {
"tags": []
},
"execution_count": 3
},
{
"output_type": "display_data",
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAUsAAAFKCAYAAACU6307AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAoDBfwEJhoM/zPq1ZQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe51d58fcc0>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"metadata": {
"id": "9c7RCtEE6nni",
"colab_type": "code",
"outputId": "b77fd9dc-eef7-41a9-84a9-5c318d1271da",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 85
}
},
"cell_type": "code",
"source": [
"from keras.utils import np_utils\n",
"x_train = x_train.reshape(60000, 784) / 255\n",
"x_test = x_test.reshape(10000, 784) / 255\n",
"y_train = np_utils.to_categorical(y_train, 10)\n",
"y_test = np_utils.to_categorical(y_test, 10)\n",
"print(x_train.shape)\n",
"print(y_train.shape)\n",
"print(x_test.shape)\n",
"print(y_test.shape)"
],
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"text": [
"(60000, 784)\n",
"(60000, 10)\n",
"(10000, 784)\n",
"(10000, 10)\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "l0qjXDda6nnl",
"colab_type": "code",
"outputId": "5cd6ce18-23ce-4e5c-87c7-ac2e9405a8e3",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"cell_type": "code",
"source": [
"y_train[5566]"
],
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([0., 0., 0., 0., 0., 0., 1., 0., 0., 0.], dtype=float32)"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
"metadata": {
"id": "nHkLCQAq6nnq",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# Model"
]
},
{
"metadata": {
"id": "9GpDbGkx6nnr",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"from keras.models import Sequential\n",
"model = Sequential()"
],
"execution_count": 6,
"outputs": []
},
{
"metadata": {
"id": "vXOYizQ56nnv",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"from keras.layers import Dense, Activation\n",
"model.add(Dense(500, input_dim=784))\n",
"model.add(Activation('relu'))\n",
"model.add(Dense(500))\n",
"model.add(Activation('relu'))\n",
"model.add(Dense(10))\n",
"model.add(Activation('softmax'))"
],
"execution_count": 7,
"outputs": []
},
{
"metadata": {
"id": "uypLOJ8D6nn1",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"from keras.optimizers import SGD\n",
"model.compile(loss='MSE', optimizer=SGD(lr=0.1), metrics=['accuracy'])"
],
"execution_count": 8,
"outputs": []
},
{
"metadata": {
"id": "wD6bTV_k6nn5",
"colab_type": "code",
"outputId": "e7e880b3-3582-42e7-dfe4-23528ac000ab",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 340
}
},
"cell_type": "code",
"source": [
"model.summary()"
],
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"dense_1 (Dense) (None, 500) 392500 \n",
"_________________________________________________________________\n",
"activation_1 (Activation) (None, 500) 0 \n",
"_________________________________________________________________\n",
"dense_2 (Dense) (None, 500) 250500 \n",
"_________________________________________________________________\n",
"activation_2 (Activation) (None, 500) 0 \n",
"_________________________________________________________________\n",
"dense_3 (Dense) (None, 10) 5010 \n",
"_________________________________________________________________\n",
"activation_3 (Activation) (None, 10) 0 \n",
"=================================================================\n",
"Total params: 648,010\n",
"Trainable params: 648,010\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "5sV1s2sL6nn9",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# Train"
]
},
{
"metadata": {
"id": "x6IpaIr76nn-",
"colab_type": "code",
"outputId": "64e440f2-99cd-4c82-d89f-8ac4de8fda77",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 374
}
},
"cell_type": "code",
"source": [
"from time import time\n",
"t = time()\n",
"model.fit(x_train, y_train, batch_size=100, epochs=10)"
],
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"text": [
"Epoch 1/10\n",
"60000/60000 [==============================] - 5s 82us/step - loss: 0.0717 - acc: 0.5565\n",
"Epoch 2/10\n",
"60000/60000 [==============================] - 3s 47us/step - loss: 0.0335 - acc: 0.8235\n",
"Epoch 3/10\n",
"60000/60000 [==============================] - 3s 46us/step - loss: 0.0213 - acc: 0.8804\n",
"Epoch 4/10\n",
"60000/60000 [==============================] - 3s 47us/step - loss: 0.0176 - acc: 0.8954\n",
"Epoch 5/10\n",
"60000/60000 [==============================] - 3s 46us/step - loss: 0.0157 - acc: 0.9036\n",
"Epoch 6/10\n",
"60000/60000 [==============================] - 3s 46us/step - loss: 0.0146 - acc: 0.9102\n",
"Epoch 7/10\n",
"60000/60000 [==============================] - 3s 46us/step - loss: 0.0137 - acc: 0.9151\n",
"Epoch 8/10\n",
"60000/60000 [==============================] - 3s 47us/step - loss: 0.0130 - acc: 0.9191\n",
"Epoch 9/10\n",
"60000/60000 [==============================] - 3s 47us/step - loss: 0.0124 - acc: 0.9222\n",
"Epoch 10/10\n",
"60000/60000 [==============================] - 3s 48us/step - loss: 0.0119 - acc: 0.9255\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7fe4dec5a860>"
]
},
"metadata": {
"tags": []
},
"execution_count": 10
}
]
},
{
"metadata": {
"id": "7tehj-Fl6noB",
"colab_type": "code",
"outputId": "d422a888-ede3-4fb0-9b26-42bcc95a540b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"cell_type": "code",
"source": [
"time() - t"
],
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"30.380422353744507"
]
},
"metadata": {
"tags": []
},
"execution_count": 11
}
]
}
]
}
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