質問編集履歴
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#ラベル生成
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from sklearn.preprocessing import LabelEncoder
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import numpy as np
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# ラベルデータをカテゴリの1-hotベクトルにエンコードする
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one_hot_labels = keras.utils.to_categorical(vec, num_classes=10)
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# 各イテレーションのバッチサイズを32で学習を行なう(fitでデータとラベルを一括で渡す)
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model.fit(col, one_hot_labels, epochs=10, batch_size=32)
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loss='categorical_crossentropy',
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metrics=['accuracy'])
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# ダミーデータ作成
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#import numpy as np
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#data = np.random.random((32, 100))
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#labels = np.random.randint(4, size=(1000, 1))
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