{"id":53512,"date":"2018-05-19T15:44:20","date_gmt":"2018-05-19T06:44:20","guid":{"rendered":"https:\/\/www.sejuku.net\/blog\/?p=53512"},"modified":"2024-05-06T11:47:34","modified_gmt":"2024-05-06T02:47:34","slug":"%e3%80%90tensorflow%e3%80%91%e7%b5%b1%e5%90%88%e3%81%95%e3%82%8c%e3%81%9fkeras%e3%82%92%e4%bd%bf%e3%81%a3%e3%81%a6%e3%81%bf%e3%82%88%e3%81%86%ef%bc%81","status":"publish","type":"post","link":"https:\/\/www.sejuku.net\/blog\/53512","title":{"rendered":"\u3010TensorFlow\u3011\u7d71\u5408\u3055\u308c\u305fKeras\u3092\u4f7f\u3063\u3066\u307f\u3088\u3046\uff01"},"content":{"rendered":"<p>\u4eca\u56de\u306f\u3001<br \/>\n<div class=\"box01\"><\/p>\n<ul>\n<li><b>TensorFlow\u3092\u4f7f\u3046\u306a\u3089Keras\u304c\u30a4\u30a4\u30e8\uff01\u3068\u3069\u3053\u304b\u3067\u8aad\u3093\u3060<\/b><\/li>\n<li><b>Keras\u304cTensorFlow\u306b\u7d71\u5408\u3055\u308c\u305f\u307f\u305f\u3044\u3060\u3051\u3069\u30b5\u30f3\u30d7\u30eb\u30b3\u30fc\u30c9\u304c\u898b\u3064\u304b\u3089\u306a\u3044<\/b><\/li>\n<\/ul>\n<p><\/div><br \/>\n\u3068\u3044\u3046\u3042\u306a\u305f\u306b\u9001\u308b\u3001TensorFlow\u306b\u7d71\u5408\u3055\u308c\u305fKeras\u3092\u4f7f\u3063\u3066\u307f\u3088\u3046\u3068\u3044\u3046\u8a18\u4e8b\u3067\u3059\u3002<\/p>\n<p>import\u6587\u304c\u5f15\u3063\u304b\u304b\u308b\u3068\u3053\u308d\u3067\u3059\u304c\u3001\u305d\u308c\u4ee5\u5916\u306f\u307b\u3068\u3093\u3069\u56f0\u3089\u306a\u3044\u3068\u601d\u3044\u307e\u3059\u3002\u3067\u306f\u3001\u884c\u3063\u3066\u307f\u307e\u3057\u3087\u3046\uff01<\/p>\n<h2>TensorFlow\u3068\u306f<\/h2>\n<p>TensorFlo\u306f\u3001<b>\u6a5f\u68b0\u5b66\u7fd2\u3084\u6df1\u5c64\u5b66\u7fd2\u306e\u30d7\u30ed\u30b0\u30e9\u30e0\u3092\u5b9f\u88c5\u3059\u308b\u969b\u306b\u4f7f\u7528\u3067\u304d\u308b\u30e9\u30a4\u30d6\u30e9\u30ea<\/b>\u3067\u3059\u3002<\/p>\n<p>TensorFlow\u3068\u306f\u4f55\u304b\uff1f\u306b\u3064\u3044\u3066\u306f\u3001\u4ee5\u4e0b\u306e\u8a18\u4e8b\u3067\u8aac\u660e\u3057\u3066\u3044\u307e\u3059\u306e\u3067\u3001\u3088\u304f\u5206\u304b\u3089\u306a\u3044\u65b9\u306f\u78ba\u8a8d\u3057\u3066\u307f\u3066\u304f\u3060\u3055\u3044\u306d\u3002\u3082\u3061\u308d\u3093\u3001\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u306b\u3064\u3044\u3066\u3082\u89e6\u308c\u3089\u308c\u3066\u3044\u307e\u3059\u306e\u3067\u3001\u5b89\u5fc3\u3057\u3066\u304f\u3060\u3055\u3044\uff01<\/p>\n            <a href=\"https:\/\/www.sejuku.net\/blog\/38134\" class=\"blog-card-anchor\">\n                <div class=\"blog-card\">\n                <div class=\"blog-card-content\">\n                    <div class=\"blog-card-thumbnail\">\n                    <img decoding=\"async\" class=\"blog-card-thumbnail-img\" src=\"https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2017\/11\/Tensor-150x85.png\">\n                    <\/div>\n                    <div class=\"blog-card-title-wrap\">\n                    <div class=\"blog-card-title\">TensorFlow\u3068\u306f\uff1f\u6a5f\u68b0\u5b66\u7fd2\u306b\u5fc5\u9808\u306e\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u5206\u304b\u308a\u3084\u3059\u304f\u7d39\u4ecb<\/div>\n                    <div class=\"blog-card-update-date\">\u66f4\u65b0\u65e5\uff1a2025\u5e7412\u670826\u65e5<\/div>\n                    <\/div>\n                <\/div>\n                <\/div>\n            <\/a>\n            \n<h2>Keras\u3068\u306f<\/h2>\n<p>Keras\u306f\u3001\u30d0\u30c3\u30af\u30a8\u30f3\u30c9\u306bTensorFlow\u3092\u5229\u7528\u3067\u304d\u308b\u6df1\u5c64\u5b66\u7fd2\u30e9\u30a4\u30d6\u30e9\u30ea\u3067\u3059\u3002<\/p>\n<h3>\u306a\u305cKeras\u3092\u4f7f\u3046\u306e\u304b\uff1f<\/h3>\n<p>Keras\u306e\u516c\u5f0f\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u306b\u300c\u306a\u305cKeras\u3092\u4f7f\u3046\u304b\uff1f\u300d\u3068\u3044\u3046\u30da\u30fc\u30b8\u304c\u3042\u308a\u307e\u3059\u306e\u3067\u3001Keras\u304c\u3059\u3054\u3044\u3068\u3053\u308d\u306f\u3001\u305d\u3061\u3089\u3092\u3054\u89a7\u3044\u305f\u3060\u3044\u305f\u65b9\u304c\u826f\u3044\u3067\u3057\u3087\u3046\u3002<\/p>\n<p><img decoding=\"async\" class=\"alignnone size-full wp-image-53515\" src=\"https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/05\/tensor-keras01.png\" alt=\"tensor-keras01\" width=\"1200\" height=\"686\" srcset=\"https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/05\/tensor-keras01.png 1200w, https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/05\/tensor-keras01-150x86.png 150w, https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/05\/tensor-keras01-300x172.png 300w, https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/05\/tensor-keras01-640x366.png 640w, https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/05\/tensor-keras01-810x463.png 810w, https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/05\/tensor-keras01-1140x652.png 1140w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" \/><div class=\"img-caption\">\u5f15\u7528\u5143\uff1aKeras Documentation <a href=\"https:\/\/keras.io\/ja\/why-use-keras\/\">https:\/\/keras.io\/ja\/why-use-keras\/<\/a><\/div><\/p>\n<p>Keras\u306f\u3001\u30d0\u30c3\u30af\u30a8\u30f3\u30c9\u3068\u3057\u3066CNTK\u3084Theano\u3082\u5229\u7528\u3067\u304d\u307e\u3059\u304c\u3001\u3053\u306e\u8a18\u4e8b\u3067\u306fTensorFlow\u306b\u6ce8\u76ee\u3057\u3066\u3044\u304d\u307e\u3059\u3002<\/p>\n<h3>2\u3064\u306eKeras<\/h3>\n<p>\u5b9f\u306fKeras\u306b\u306f\u30012\u3064\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n\n<table id=\"tablepress-459\" class=\"tablepress tablepress-id-459\">\n<thead>\n<tr class=\"row-1\">\n\t<th class=\"column-1\">\u30d0\u30fc\u30b8\u30e7\u30f3<\/th><th class=\"column-2\">\u8aac\u660e<\/th>\n<\/tr>\n<\/thead>\n<tbody class=\"row-striping row-hover\">\n<tr class=\"row-2\">\n\t<td class=\"column-1\">TensorFlow\u304b\u3089\u72ec\u7acb\u3057\u3066\u3044\u308bKeras<\/td><td class=\"column-2\">\u30aa\u30ea\u30b8\u30ca\u30eb\u306eKeras\u3002\u30d0\u30c3\u30af\u30a8\u30f3\u30c9\u306bCNTK\u3084Theano\u3092\u5229\u7528\u3067\u304d\u308b\u3002\u5225\u9014Keras\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u304c\u5fc5\u8981\u3002import keras\u3067\u4f7f\u3048\u308b\u3002<\/td>\n<\/tr>\n<tr class=\"row-3\">\n\t<td class=\"column-1\">TensorFlow\u3068\u7d71\u5408\u3055\u308c\u3066\u3044\u308bKeras<\/td><td class=\"column-2\">\u4eba\u6c17\u304c\u51fa\u305f\u305f\u3081TensorFlow\u306b\u7d71\u5408\u3055\u308c\u305fKeras\u3002TensorFlow\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u306b\u3088\u3063\u3066\u3001import tensorflow.contrib.keras\u3001\u307e\u305f\u306fimport tensorflow.python.keras\u3067\u4f7f\u3048\u308b\uff08\u4eca\u56de\u8aac\u660e\u3057\u3066\u3044\u308b\u306e\u306f\u5f8c\u8005\uff09\u3002<br \/>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<!-- #tablepress-459 from cache -->\n<p>\u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001\u30bf\u30a4\u30c8\u30eb\u306b\u3042\u308b\u3068\u304a\u308aTensorFlow\u3068\u7d71\u5408\u3055\u308c\u3066\u3044\u308bKeras\u3092\u4f7f\u3063\u3066\u307f\u307e\u3059\u3002\u3057\u305f\u304c\u3063\u3066\u3001\u3053\u306e\u8a18\u4e8b\u306b\u66f8\u304b\u308c\u3066\u3044\u308b\u30b3\u30fc\u30c9\u3092\u8a66\u3059\u7bc4\u56f2\u3067\u306f\u3001Keras\u3092\u5225\u9014\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u5fc5\u8981\u306f\u3042\u308a\u307e\u305b\u3093\u3002<\/p>\n<h2>TensorFlow\u306b\u7d71\u5408\u3055\u308c\u305fKeras\u3067MNIST\u306b\u6311\u6226<\/h2>\n<p>\u307e\u305a\u306f\u3001\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u304c\u5fc5\u8981\u306aKeras\u306e\u30b3\u30fc\u30c9\u3092\u3054\u89a7\u3044\u305f\u3060\u304d\u307e\u3059\u3002<\/p>\n<p>\u53c2\u8003\uff1a<a href=\"https:\/\/github.com\/keras-team\/keras\/blob\/master\/examples\/mnist_cnn.py\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/github.com\/keras-team\/keras\/blob\/master\/examples\/mnist_cnn.py<\/a><\/p>\n<pre class=\"decode:true \">'''Trains a simple convnet on the MNIST dataset.\r\nGets to 99.25% test accuracy after 12 epochs\r\n(there is still a lot of margin for parameter tuning).\r\n16 seconds per epoch on a GRID K520 GPU.\r\n'''\r\n\r\nfrom __future__ import print_function\r\nimport keras\r\nfrom keras.datasets import mnist\r\nfrom keras.models import Sequential\r\nfrom keras.layers import Dense, Dropout, Flatten\r\nfrom keras.layers import Conv2D, MaxPooling2D\r\nfrom keras import backend as K\r\n\r\nbatch_size = 128\r\nnum_classes = 10\r\nepochs = 12\r\n\r\n# input image dimensions\r\nimg_rows, img_cols = 28, 28\r\n\r\n# the data, split between train and test sets\r\n(x_train, y_train), (x_test, y_test) = mnist.load_data()\r\n\r\nif K.image_data_format() == 'channels_first':\r\n    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\r\n    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\r\n    input_shape = (1, img_rows, img_cols)\r\nelse:\r\n    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\r\n    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\r\n    input_shape = (img_rows, img_cols, 1)\r\n\r\nx_train = x_train.astype('float32')\r\nx_test = x_test.astype('float32')\r\nx_train \/= 255\r\nx_test \/= 255\r\nprint('x_train shape:', x_train.shape)\r\nprint(x_train.shape[0], 'train samples')\r\nprint(x_test.shape[0], 'test samples')\r\n\r\n# convert class vectors to binary class matrices\r\ny_train = keras.utils.to_categorical(y_train, num_classes)\r\ny_test = keras.utils.to_categorical(y_test, num_classes)\r\n\r\nmodel = Sequential()\r\nmodel.add(Conv2D(32, kernel_size=(3, 3),\r\n                 activation='relu',\r\n                 input_shape=input_shape))\r\nmodel.add(Conv2D(64, (3, 3), activation='relu'))\r\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\r\nmodel.add(Dropout(0.25))\r\nmodel.add(Flatten())\r\nmodel.add(Dense(128, activation='relu'))\r\nmodel.add(Dropout(0.5))\r\nmodel.add(Dense(num_classes, activation='softmax'))\r\n\r\nmodel.compile(loss=keras.losses.categorical_crossentropy,\r\n              optimizer=keras.optimizers.Adadelta(),\r\n              metrics=['accuracy'])\r\n\r\nmodel.fit(x_train, y_train,\r\n          batch_size=batch_size,\r\n          epochs=epochs,\r\n          verbose=1,\r\n          validation_data=(x_test, y_test))\r\nscore = model.evaluate(x_test, y_test, verbose=0)\r\nprint('Test loss:', score[0])\r\nprint('Test accuracy:', score[1])<\/pre>\n<p>\u3053\u308c\u3092\u3001TensorFlow\u306b\u7d71\u5408\u3055\u308c\u305fKeras\u3092\u4f7f\u3046\u3088\u3046\u306b\u5909\u66f4\u3059\u308b\u306b\u306f\u3001\u5192\u982d\u306e\u90e8\u5206\u3092\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u4fee\u6b63\u3057\u307e\u3059\u3002<\/p>\n<pre class=\"decode:true \">'''Trains a simple convnet on the MNIST dataset.\r\nGets to 99.25% test accuracy after 12 epochs\r\n(there is still a lot of margin for parameter tuning).\r\n16 seconds per epoch on a GRID K520 GPU.\r\n'''\r\n\r\nfrom __future__ import print_function\r\nfrom tensorflow.python import keras\r\nfrom tensorflow.python.keras.datasets import mnist\r\nfrom tensorflow.python.keras.models import Sequential\r\nfrom tensorflow.python.keras.layers import Dense, Dropout, Flatten\r\nfrom tensorflow.python.keras.layers import Conv2D, MaxPooling2D\r\nfrom tensorflow.python.keras import backend as K<\/pre>\n<p>\u300cbatch_size = 128\u300d\u4ee5\u964d\u306f\u4fee\u6b63\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u305b\u3093\u306e\u3067\u3001\u8aac\u660e\u306f\u3053\u308c\u3067\u7d42\u308f\u308a\u3067\u3059\u3002\u601d\u3063\u305f\u3088\u308a\u7c21\u5358\u306b\u30012\u3064\u306eKeras\u3092\u5207\u308a\u66ff\u3048\u3089\u308c\u308b\u3053\u3068\u306b\u9a5a\u304d\u307e\u3059\u306d\uff01<br \/>\n<\/strong><\/span><\/p>\n<h2>\u307e\u3068\u3081<\/h2>\n<p>\u4eca\u56de\u306f\u3001Keras\u306b\u306f2\u3064\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u304c\u3042\u308b\u3053\u3068\u3092\u8aac\u660e\u3057\u3001\u4e92\u3044\u306b\u5c11\u3057\u306e\u624b\u9593\u3067\u5207\u308a\u66ff\u3048\u3089\u308c\u308b\u3053\u3068\u3092\u8aac\u660e\u3057\u307e\u3057\u305f\u3002<\/p>\n<p>TensorFlow\u306b\u7d71\u5408\u3059\u308b\u524d\u306eKeras\uff08TensorFlow\u304b\u3089\u72ec\u7acb\u3057\u3066\u3044\u308bKeras\uff09\u306e\u60c5\u5831\u306f\u305f\u304f\u3055\u3093\u3042\u308a\u307e\u3059\u304c\u3001TensorFlow\u306b\u7d71\u5408\u3055\u308c\u305fKeras\u306e\u60c5\u5831\u306f\u601d\u3044\u306e\u307b\u304b\u5c11\u306a\u3044\u306e\u304c\u73fe\u5728\u306e\u72b6\u6cc1\u3067\u3059\u3002<\/p>\n<p>\u3053\u306e\u8a18\u4e8b\u3092\u304d\u3063\u304b\u3051\u306b\u3001TensorFlow\u306b\u7d71\u5408\u3055\u308c\u305fKeras\u306e\u30e6\u30fc\u30b6\u30fc\u304c\u5897\u3048\u3001\u95a2\u9023\u8a18\u4e8b\u304c\u5897\u3048\u308b\u3053\u3068\u3092\u9858\u3044\u307e\u3059\u3002\u305d\u308c\u3067\u306f\uff01<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u4eca\u56de\u306f\u3001 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