{"id":2743,"date":"2024-03-13T17:15:38","date_gmt":"2024-03-13T08:15:38","guid":{"rendered":"https:\/\/www.silicloud.com\/ja\/blog\/keras%e3%81%a7%e5%bd%a2%e6%85%8b%e7%b4%a0%e8%a7%a3%e6%9e%90%e3%82%bf%e3%82%b9%e3%82%af%e3%82%92%e3%81%a9%e3%81%ae%e3%82%88%e3%81%86%e3%81%ab%e6%89%b1%e3%81%86%e3%81%8b\/"},"modified":"2025-08-02T09:07:08","modified_gmt":"2025-08-02T00:07:08","slug":"keras%e3%81%a7%e5%bd%a2%e6%85%8b%e7%b4%a0%e8%a7%a3%e6%9e%90%e3%82%bf%e3%82%b9%e3%82%af%e3%82%92%e3%81%a9%e3%81%ae%e3%82%88%e3%81%86%e3%81%ab%e6%89%b1%e3%81%86%e3%81%8b","status":"publish","type":"post","link":"https:\/\/www.silicloud.com\/ja\/blog\/keras%e3%81%a7%e5%bd%a2%e6%85%8b%e7%b4%a0%e8%a7%a3%e6%9e%90%e3%82%bf%e3%82%b9%e3%82%af%e3%82%92%e3%81%a9%e3%81%ae%e3%82%88%e3%81%86%e3%81%ab%e6%89%b1%e3%81%86%e3%81%8b\/","title":{"rendered":"Keras\u3067\u5f62\u614b\u7d20\u89e3\u6790\u30bf\u30b9\u30af\u3092\u3069\u306e\u3088\u3046\u306b\u6271\u3046\u304b"},"content":{"rendered":"

Keras\u3067\u306e\u5358\u8a9e\u5206\u5272\u4f5c\u696d\u306b\u306f\u3001\u901a\u5e38Tokenizer\u30af\u30e9\u30b9\u3092\u4f7f\u7528\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u3053\u306e\u30af\u30e9\u30b9\u306f\u30c6\u30ad\u30b9\u30c8\u30c7\u30fc\u30bf\u3092\u6574\u6570\u306e\u30b7\u30fc\u30b1\u30f3\u30b9\u306b\u5909\u63db\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002\u5358\u8a9e\u5206\u5272\u4f5c\u696d\u306e\u4e3b\u8981\u306a\u30b9\u30c6\u30c3\u30d7\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\u3002<\/p>\n

    \n
  1. \u30c8\u30fc\u30af\u30ca\u30a4\u30b6\u30fc\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3092\u4f5c\u6210\u3057\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30c7\u30fc\u30bf\u306b\u5408\u308f\u305b\u308b\u3002<\/li>\n<\/ol>\n
    from<\/span> keras.preprocessing.text import<\/span> Tokenizer\r\n\r\ntokenizer = Tokenizer()\r\ntokenizer.fit_on_texts(train_texts)\r\n<\/code><\/pre>\n
      \n
    1. \u30c6\u30ad\u30b9\u30c8\u30c7\u30fc\u30bf\u3092\u6574\u6570\u5217\u306b\u5909\u63db\u3059\u308b\u3002<\/li>\n<\/ol>\n
      train_sequences = tokenizer.texts_to_sequences(train_texts)\r\ntest_sequences = tokenizer.texts_to_sequences(test_texts)\r\n<\/code><\/pre>\n
        \n
      1. \u6574\u6570\u306e\u30b7\u30fc\u30b1\u30f3\u30b9\u3092\u57cb\u3081\u3066\u3001\u305d\u308c\u3089\u304c\u540c\u3058\u9577\u3055\u3092\u6301\u3064\u3088\u3046\u306b\u3057\u307e\u3059\u3002<\/li>\n<\/ol>\n
        from<\/span> keras.preprocessing.sequence import<\/span> pad_sequences\r\n\r\nmax_len = 100<\/span>\r\ntrain_sequences_padded = pad_sequences(train_sequences, maxlen=max_len)\r\ntest_sequences_padded = pad_sequences(test_sequences, maxlen=max_len)\r\n<\/code><\/pre>\n
          \n
        1. \u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3057\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3092\u884c\u3046\u3002<\/li>\n<\/ol>\n
          from<\/span> keras.models import<\/span> Sequential\r\nfrom<\/span> keras.layers import<\/span> Embedding, LSTM, Dense\r\n\r\nmodel = Sequential()\r\nmodel.add(Embedding(input_dim=num_words, output_dim=embedding_dim, input_length=max_len))\r\nmodel.add(LSTM(units=64<\/span>))\r\nmodel.add(Dense(units=num_classes, activation='softmax'<\/span>))\r\n\r\nmodel.compile<\/span>(optimizer='adam'<\/span>, loss='categorical_crossentropy'<\/span>, metrics=['accuracy'<\/span>])\r\nmodel.fit(train_sequences_padded, train_labels, epochs=10<\/span>, batch_size=32<\/span>)\r\n<\/code><\/pre>\n
            \n
          1. \u30c6\u30b9\u30c8\u30c7\u30fc\u30bf\u3092\u4e88\u6e2c\u3057\u3066\u30e2\u30c7\u30eb\u306e\u6027\u80fd\u3092\u8a55\u4fa1\u3059\u308b\u3002<\/li>\n<\/ol>\n
            predictions = model.predict(test_sequences_padded)\r\n<\/code><\/pre>\n
            \u3053\u308c\u306f\u5358\u8a9e\u5206\u5272\u30bf\u30b9\u30af\u3092\u51e6\u7406\u3059\u308b\u305f\u3081\u306e\u57fa\u672c\u7684\u306a\u624b\u9806\u3067\u3059\u3002\u5177\u4f53\u7684\u306a\u8981\u4ef6\u3084\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u5fdc\u3058\u3066\u8abf\u6574\u3084\u62e1\u5f35\u304c\u53ef\u80fd\u3067\u3059\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
            Keras\u3067\u306e\u5358\u8a9e\u5206\u5272\u4f5c\u696d\u306b\u306f\u3001\u901a\u5e38Tokenizer\u30af\u30e9\u30b9\u3092\u4f7f\u7528\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u3053\u306e\u30af\u30e9\u30b9\u306f\u30c6\u30ad\u30b9\u30c8\u30c7\u30fc\u30bf\u3092\u6574\u6570\u306e\u30b7\u30fc\u30b1\u30f3\u30b9\u306b\u5909\u63db\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002\u5358\u8a9e\u5206\u5272\u4f5c\u696d\u306e\u4e3b\u8981\u306a\u30b9\u30c6\u30c3\u30d7\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\u3002 \u30c8\u30fc\u30af\u30ca\u30a4\u30b6 […]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[26,61],"class_list":["post-2743","post","type-post","status-publish","format-standard","hentry","category-uncategorized","tag-26","tag-61"],"yoast_head":"\nKeras\u3067\u5f62\u614b\u7d20\u89e3\u6790\u30bf\u30b9\u30af\u3092\u3069\u306e\u3088\u3046\u306b\u6271\u3046\u304b - 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