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JironBach/MNIST.ipynb

Created February 14, 2017 20:06
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MNIST.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## load_mnist"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" \n",
" \n",
" \n",
" \n",
" \n",
" ++ +** \n",
" ++******+**+ \n",
" ********** \n",
" ******++** \n",
" + *** + \n",
" +* \n",
" +*+ \n",
" +* \n",
" **+ \n",
" *** \n",
" +**+ \n",
" **+ \n",
" *** \n",
" ++*** \n",
" +*****+ \n",
" *****+ \n",
" *****+ \n",
" +*****+ \n",
" +******+ \n",
" +****++ \n",
" \n",
" \n",
" \n",
"correct = 5\n",
"--------------------------------\n",
" \n",
" \n",
" \n",
" \n",
" +*+ \n",
" ***** \n",
" ****** \n",
" ****+ ** \n",
" +****** +*+ \n",
" ***+ ** *+ \n",
" ***+ ** \n",
" +*** **+ \n",
" +** **+ \n",
" ** **+ \n",
" +*+ **+ \n",
" ** **+ \n",
" ** +*+ \n",
" ** +** \n",
" *+ +*+ \n",
" ** **+ \n",
" **+ +***+ \n",
" ********++ \n",
" +******+ \n",
" +***+ \n",
" \n",
" \n",
" \n",
" \n",
"correct = 0\n",
"--------------------------------\n",
" \n",
" \n",
" \n",
" \n",
" \n",
" * \n",
" + \n",
" + +* \n",
" *+ *+ \n",
" *+ +* \n",
" *+ +* \n",
" *+ ** \n",
" +* +** \n",
" +* +** \n",
" +* ++***+** \n",
" +*********++ ** \n",
" +++++ ** \n",
" +*+ \n",
" +* \n",
" +* \n",
" +* \n",
" +* \n",
" +*+ \n",
" +*+ \n",
" *+ \n",
" \n",
" \n",
" \n",
"correct = 4\n",
"--------------------------------\n",
" \n",
" \n",
" \n",
" \n",
" \n",
" ** \n",
" *** \n",
" *** \n",
" *** \n",
" *** \n",
" +**+ \n",
" *** \n",
" *** \n",
" **+ \n",
" **+ \n",
" ***+ \n",
" +*** \n",
" ***+ \n",
" ***+ \n",
" *** \n",
" +*** \n",
" ***+ \n",
" *** \n",
" *** \n",
" +** \n",
" \n",
" \n",
" \n",
"correct = 1\n",
"--------------------------------\n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" +*** + \n",
" ***+****+ \n",
" **+ +** \n",
" **+ *** \n",
" +**+ *** \n",
" **+ **+ \n",
" **+ +**+ \n",
" ** +**** \n",
" ********** \n",
" ***+ +*+ \n",
" ** \n",
" ** \n",
" ** \n",
" ** \n",
" ** \n",
" ** \n",
" ** \n",
" *+ \n",
" +*+ \n",
" +* \n",
" \n",
"correct = 9\n",
"--------------------------------\n"
]
}
],
"source": [
"#!/usr/bin/env python\n",
"# -*- coding: utf-8 -*-\n",
"\n",
"import cPickle, gzip, numpy, sys\n",
"\n",
"start = 0\n",
"end = 4\n",
"\n",
"for index in range(start, end + 1):\n",
" data_index=index\n",
" f=gzip.open('./neural-networks-and-deep-learning/data/mnist.pkl.gz','rb')\n",
" train_set, valid_set, test_set=cPickle.load(f)\n",
" train_set_x, train_set_y=train_set # setの中身が\n",
" for i in range(data_index,data_index+1):\n",
" for y in range(0,28):\n",
" for x in range(0,28):\n",
" if train_set_x[i][y*28+x]<0.5:\n",
" sys.stdout.write(\" \")\n",
" elif train_set_x[i][y*28+x]<0.8:\n",
" sys.stdout.write(\"+\")\n",
" else:\n",
" sys.stdout.write(\"*\")\n",
" sys.stdout.write(\"\\n\")\n",
" print \"correct =\",train_set_y[i]\n",
" print \"--------------------------------\"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## [手書き数字データ MNIST の取り扱い方](http://www.cs.miyazaki-u.ac.jp/~date/lectures/mneuro/mnist/index.html)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3\n"
]
},
{
"data": {
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GZmYVlGVkdNhRb+Swo97YZ98jD67hG+d/ttlnbpa0kqQieilsW/ZtKjC/yWF+CZxWt+8g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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd34fa246d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"5\n"
]
},
{
"data": {
"image/png": 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F+ZmRmZmVztV0ZmZWvhG2AoOTkZlZBUWG1443O64buZrOzMxK556RmVklFaum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\nQ9lfAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd35010cf50>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"3\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7fd350256bd0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"6\n"
]
},
{
"data": {
"image/png": 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VUIum6TqAHYGumv1dwH4ZT/FN4DGSBJaZk5GZWSV1Q6HKuELVdCLDuyckfRn4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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd34f9d8e10>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n"
]
},
{
"data": {
"image/png": 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DvpTlftwzMjOroryrKbSoExGrJU0GlpEM120C5kfEzrTIdGDfoPLbJb0F+FtgM/BI+vdG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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd34f8dcf50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#!/usr/bin/env python\n",
"# -*- coding: utf-8 -*-\n",
"\n",
"import matplotlib\n",
"\n",
"import pylab as pl, numpy as np\n",
"#-----\n",
"# way1 from : http://stackoverflow.com/questions/2801882/generating-a-png-with-matplotlib-when-display-is-undefined\n",
"# Force matplotlib to not use any Xwindows backend.\n",
"matplotlib.use('Agg')\n",
"# way2 from : http://stackoverflow.com/questions/19518352/tkinter-tclerror-couldnt-connect-to-display-localhost18-0\n",
"#matplotlib.use('pdf')\n",
"#import matplotlib.pyplot as plt\n",
"#-----\n",
"\n",
"import cPickle, gzip\n",
"\n",
"mnist = cPickle.load(gzip.open(\"./neural-networks-and-deep-learning/data/mnist.pkl.gz\", \"rb\"))\n",
"\n",
"start = 10\n",
"end = 15\n",
"\n",
"for index in range(start, end):\n",
" # mnistデータから1枚持ってくる\n",
" a_digit = mnist[0][0][index]\n",
" # 答えを出力\n",
" print mnist[0][1][index]\n",
"\n",
" # 1次元なので，2次元のリストに変換\n",
" arr = []\n",
" for i in range(28):\n",
" arr.append(a_digit[(i*28):(i*28)+28])\n",
"\n",
" # オフスクリーンにプロット\n",
" pl.imshow(arr, interpolation='nearest', cmap='bone', origin='lower')\n",
" #pl.colorbar(shrink=.92)\n",
" pl.colorbar()\n",
"\n",
" # 軸の反転\n",
" ax = pl.gca()\n",
" #ax.invert_xaxis()\n",
" ax.invert_yaxis()\n",
"\n",
" # 表示\n",
" pl.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## MNIST入門"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting ./mnist/MNIST_data/train-images-idx3-ubyte.gz\n",
"Extracting ./mnist/MNIST_data/train-labels-idx1-ubyte.gz\n",
"Extracting ./mnist/MNIST_data/t10k-images-idx3-ubyte.gz\n",
"Extracting ./mnist/MNIST_data/t10k-labels-idx1-ubyte.gz\n",
"0.9114\n"
]
}
],
"source": [
"#!/usr/bin/env python\n",
"# -*- coding: utf-8 -*-\n",
"\n",
"import tensorflow as tf\n",
"\n",
"# 入力\n",
"import input_data\n",
"#MNISTの784ピクセルのグレースケール値。\n",
"mnist = input_data.read_data_sets('./mnist/MNIST_data/', one_hot=True)\n",
"x = tf.placeholder('float', [None, 784]) #入力xxを格納するplaceholder\n",
"\n",
"# 重み・バイアス\n",
"#tf.zerosにより、ゼロで初期化\n",
"#tf.Varibleにより、計算途中でmodifiableな変数として定義\n",
"W = tf.Variable(tf.zeros([784, 10]))\n",
"b = tf.Variable(tf.zeros([10]))\n",
"\n",
"# 出力: 0-9のClass\n",
"y = tf.nn.softmax(tf.matmul(x, W) + b)\n",
"\n",
"# 誤差関数\n",
"y_ = tf.placeholder(\"float\", [None, 10])\n",
"#y′ を真のClass Distribution(正しいClassに対応する成分が1、他の成分は0)とし、以下のクロスエントロピーを考える。\n",
"cross_entropy = -tf.reduce_sum(y_*tf.log(y)) #この式を各画像に対してSUMする。\n",
"\n",
"# 最小化問題とそのアルゴリズム\n",
"learning_coefficient = 0.01 #学習係数。最急降下法を使うことと、それによって最小化する値を指定。\n",
"train_step = tf.train.GradientDescentOptimizer(learning_coefficient).minimize(cross_entropy)\n",
"\n",
"# 変数の初期化\n",
"init = tf.initialize_all_variables()\n",
"\n",
"# 学習の実行\n",
"repeat_num = 1000 #1000回の反復\n",
"batch_size = 100 #バッチサイズ100\n",
"sess = tf.Session()\n",
"sess.run(init)\n",
"for i in range(repeat_num):\n",
" batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n",
" sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})\n",
"\n",
"# 学習の評価\n",
"#「尤もらしいClass、すなわちargmax(y)argmax(y)が教師のラベルと等しいか」を以下のように記述\n",
"correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))\n",
"#この値を各サンプル毎に評価して平均を計算する\n",
"accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n",
"\n",
"print sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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