假設我有兩個輸入：XandY我想設計和聯合自動編碼器來重建X'and Y'。如圖所示，X是音頻輸入，Y是視頻輸入。這種深層架構很酷，因為它有兩個輸入和兩個輸出。此外，它們共享中間的某個層。我的問題是如何使用Keras來編寫這個自動編碼器。假設除了中間的共享層之外，每一層都是全連接的。這是我的代碼如下： from keras.layers import Input, Dense from keras.models import Model import numpy as np X = np.random.random((1000, 100)) y = np.random.random((1000, 300)) # x and y can be different size # the X autoencoder layer Xinput = Input(shape=(100,)) encoded = Dense(50, activation='relu')(Xinput) encoded = Dense(20, activation='relu')(encoded) encoded = Dense(15, activation='relu')(encoded) decoded = Dense(20, activation='relu')(encoded) decoded = Dense(50, activation='relu')(decoded) decoded = Dense(100, activation='relu')(decoded) # the Y autoencoder layer Yinput = Input(shape=(300,)) encoded = Dense(120, activation='relu')(Yinput) encoded = Dense(50, activation='relu')(encoded) encoded = Dense(15, activation='relu')(encoded) decoded = Dense(50, activation='relu')(encoded) decoded = Dense(120, activation='relu')(decoded) decoded = Dense(300, activation='relu')(decoded)我只是中間有和 的15節點。我的問題是如何用損失函數訓練這個聯合自動編碼器？XY\|X-X'\|^2 + \|Y-Y'\|^2
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TA貢獻1846條經驗 獲得超7個贊
您的代碼方式是您有兩個單獨的模型。雖然您可以簡單地將共享表示層的輸出用于以下兩個子網，但您必須合并這兩個子網作為輸入：
Xinput = Input(shape=(100,))
Yinput = Input(shape=(300,))
Xencoded = Dense(50, activation='relu')(Xinput)
Xencoded = Dense(20, activation='relu')(Xencoded)
Yencoded = Dense(120, activation='relu')(Yinput)
Yencoded = Dense(50, activation='relu')(Yencoded)
shared_input = Concatenate()([Xencoded, Yencoded])
shared_output = Dense(15, activation='relu')(shared_input)
Xdecoded = Dense(20, activation='relu')(shared_output)
Xdecoded = Dense(50, activation='relu')(Xdecoded)
Xdecoded = Dense(100, activation='relu')(Xdecoded)
Ydecoded = Dense(50, activation='relu')(shared_output)
Ydecoded = Dense(120, activation='relu')(Ydecoded)
Ydecoded = Dense(300, activation='relu')(Ydecoded)
現在你有兩個獨立的輸出。所以你需要兩個單獨的損失函數，無論如何都會添加它們來編譯模型：
model = Model([Xinput, Yinput], [Xdecoded, Ydecoded])
model.compile(optimizer='adam', loss=['mse', 'mse'], loss_weights=[1., 1.])
然后，您可以通過以下方式簡單地訓練模型：
model.fit([X_input, Y_input], [X_label, Y_label])
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