質問編集履歴
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@@ -38,7 +38,7 @@
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model.summary()
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Model: "sequential_6"
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_________________________________________________________________
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Layer (type) Output Shape Param
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=================================================================
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conv2d_9 (Conv2D) (None, 58, 62, 20) 520
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_________________________________________________________________
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@@ -22,17 +22,14 @@
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loss='categorical_crossentropy',
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metrics=['accuracy'])
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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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code = np.array(labels['expression'])
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label_encoder = LabelEncoder()
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vec = label_encoder.fit_transform(code)
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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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//////////////////////////////////////////////////////////
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col.shape=(312,120,128)
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@@ -22,11 +22,6 @@
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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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#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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