\u6df1\u5c64\u5b66\u7fd2 TensorFlow \u3092 MNIST \u3067\u8a66\u3059<\/a>\u300d\u306e\u5168\u7d50\u5408\u578b\u306e Python \u30d7\u30ed\u30b0\u30e9\u30e0\u3092 keras \u7528\u306b\u66f8\u304d\u63db\u3048\u3066\u5b9f\u884c\u3057\u307e\u3057\u305f\u3002<\/p>\n———————- \u5168\u7d50\u5408\uff11\u5c64 Python \u30d6\u30ed\u30b0\u30e9\u30e0
\n## keras \u3092\u30a4\u30f3\u30dd\u30fc\u30c8
\nimport tensorflow as tf
\nimport keras as kr
\n## \u7d50\u679c\u306e\u8a55\u4fa1\u3000\u6570\u5024\u8a08\u7b97\u7528
\nimport numpy as np<\/p>\n
## trainX \u5b66\u7fd2\u7528\u30c7\u30fc\u30bf\u3001trainY \u6b63\u89e3\u30c7\u30fc\u30bf\u3001testX \u30c6\u30b9\u30c8\u7528\u30c7\u30fc\u30bf\u3001testY \u6b63\u89e3\u30c7\u30fc\u30bf
\n(trainX, trainY), (testX, testY) = kr.datasets.mnist.load_data()<\/p>\n
## \u5f62\u5f0f\u5909\u63db
\n# \u30c6\u30b9\u30c8\u30c7\u30fc\u30bf\u3092\u5909\u63db
\ntrainX = trainX.reshape(60000, 784)
\ntrainX = trainX\/255.
\ntestX = testX.reshape(10000, 784)
\ntestX = testX\/255.
\n# \u6b63\u89e3\u30c7\u30fc\u30bf\u3092\u5909\u63db
\ntrainY = kr.utils.to_categorical(trainY, 10)
\ntestY = kr.utils.to_categorical(testY, 10)<\/p>\n
## \u30e2\u30c7\u30eb\u69cb\u7bc9
\n# \u4e2d\u9593\u5c64\uff11\u5c64\u3000\u5168\u7d50\u5408\u578b
\nmodel = kr.models.Sequential()
\nmodel.add(kr.layers.Dense(units=256, input_shape=(784,), activation=’relu’))
\nmodel.add(kr.layers.Dropout(0.5))<\/p>\n
# \u51fa\u529b\u5c64
\nmodel.add(kr.layers.Dense(units=10, activation=’softmax’))<\/p>\n
## \u5b66\u7fd2
\nmodel.compile(optimizer=’sgd’, loss=’categorical_crossentropy’, metrics=[‘accuracy’])
\nmodel.fit(trainX, trainY, batch_size=100, epochs=20, validation_split=0.1)<\/p>\n
## \u30c6\u30b9\u30c8
\nresult = model.predict(testX)<\/p>\n
## \u7d50\u679c\u3092\u8868\u793a
\npred = np.array(result).argmax(axis=1)
\nprint(pred)
\nlabel=testY.argmax(axis=1)
\nprint(label)
\naccuracy = np.mean(pred == label, axis=0)
\nprint(accuracy)<\/p>\n
———————————————————–<\/p>\n
keras\u306e\u30d0\u30fc\u30b8\u30e7\u30f3 2.1.6 \u3067\u5b9f\u884c\u3059\u308b\u3068\u4e0b\u8a18\u306e\u3088\u3046\u306a\u30a8\u30e9\u30fc\u304c\u51fa\u307e\u3057\u305f\u3002<\/p>\n
TypeError: softmax() got an unexpected keyword argument ‘axis’<\/p>\n
\u5c11\u3057\u524d\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u3067\u8a66\u3059\u3068\u30a8\u30e9\u30fc\u304c\u3067\u306a\u3044\u306e\u3067 keras==2.1.3 \u306b\u5909\u66f4\u3057\u307e\u3057\u305f\u3002<\/p>\n
———– \u5b9f\u884c\u7d50\u679c
\n[7 2 1 … 4 5 6]
\n[7 2 1 … 4 5 6]
\n0.9461
\n———————————–<\/p>\n
\u3068\u308a\u3042\u3048\u305a\u3001Keras \u306e\u74b0\u5883\u304c\u3067\u304d\u307e\u3057\u305f\u3002<\/p>\n
<\/p>\n
(S.Onda 2018\/6\/9)<\/p>\n
<\/p>\n
<\/p>\n","protected":false},"excerpt":{"rendered":"\u4eca\u307e\u3067\u306f\u521d\u5fc3\u8005\u5411\u3051\u7df4\u7fd2\u74b0\u5883\u3068\u3057\u3066\u3001TensorFlow \u306eTFLearn \u3092\u5229\u7528\u3057\u3066\u3044\u307e\u3057\u305f\u3002 \u4eca\u5f8c\u306f\u3001\u3088\u308a\u8907\u96d1\u3067\u5b9f\u7528\u5411\u3051\u306e\u7d50\u679c\u3092\u5f97\u308b\u305f\u3081\u306b\u3001\u6df1\u5c64\u5b66\u7fd2\u30e9\u30a4\u30d6\u30e9\u30ea\u306e Keras \u3082\u4f7f\u3063\u3066\u307f\u307e\u3059\u3002 Keras \u306f Tenso …","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[7],"tags":[],"class_list":["post-582","post","type-post","status-publish","format-standard","hentry","category-ai_ecobio"],"_links":{"self":[{"href":"http:\/\/www.ecobioinfo.com\/index.php?rest_route=\/wp\/v2\/posts\/582","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www.ecobioinfo.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.ecobioinfo.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.ecobioinfo.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.ecobioinfo.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=582"}],"version-history":[{"count":5,"href":"http:\/\/www.ecobioinfo.com\/index.php?rest_route=\/wp\/v2\/posts\/582\/revisions"}],"predecessor-version":[{"id":1486,"href":"http:\/\/www.ecobioinfo.com\/index.php?rest_route=\/wp\/v2\/posts\/582\/revisions\/1486"}],"wp:attachment":[{"href":"http:\/\/www.ecobioinfo.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=582"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.ecobioinfo.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=582"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.ecobioinfo.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=582"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}