过去几周我一直在问你自动编码器的问题 . 今天的问题如下;如何从瓶颈层获取功能？

我推荐过这个网站 . https://github.com/keras-team/keras/issues/2495

我收到的错误信息在这里显示; UserWarning：更新您对Keras 2 API的 Model 调用： Model(inputs=[<tf.Tenso..., outputs=[<tf.Tenso...) Model（input = [inputs]，output = [intermediate_layer]）

此外，我试图通过使用此方法提取功能（请参阅下面的链接），它也不起作用 . https://keras.io/getting-started/faq/#how-can-i-obtain-the-output-of-an-intermediate-layer

任何评论都应该有所帮助 . 谢谢！

X = Input(shape=(37310,))

encoded = Dense(encoding_dim, activation='tanh')(X)
decoded = Dense(37310, activation='sigmoid')(encoded)

autoencoder = Model(X, decoded)   
encoded_input = Input(shape=(encoding_dim,))
decoder_layer = autoencoder.layers[-1]
decoder = Model(encoded_input, decoder_layer(encoded_input))

autoencoder.compile(optimizer='SGD', loss='mean_squared_error')

encoded1 = Dense(500, activation='tanh')(X)
encoded2 = Dense(100, activation='tanh')(encoded1)
encoded3 = Dense(50, activation='tanh')(encoded2)

decoded = Dense(100, activation='tanh')(encoded)
decoded = Dense(500, activation='tanh')(decoded)
decoded = Dense(37310, activation='sigmoid')(decoded)

autoencoder = Model(X, decoded)
autoencoder.compile(optimizer='SGD', loss='mean_squared_error')

autoencoder.fit(X_train, X_train,
            epochs=10,
            batch_size=100,
            shuffle=True,
            validation_data=(X_test, X_test))

model = Model(input=[X], output=[encoded3])
keras feature-extraction autoencoder