当我的模型有两次不同层的输入时，如何使用“fit_generator（）”？

Question

原始代码如下所示，显示我的计算机内存不足

encoder_inputs = Input(shape=input_shape)
encoder = LSTM(lstm_dim, return_state=True,
                unroll=unroll)
encoder_outputs, state_h, state_c = encoder(encoder_inputs)



states = Concatenate(axis=-1)([state_h, state_c])
neck = Dense(latent_dim, activation="relu")
neck_outputs = neck(states)



decode_h = Dense(lstm_dim, activation="relu")
decode_c = Dense(lstm_dim, activation="relu")
state_h_decoded =  decode_h(neck_outputs)
state_c_decoded =  decode_c(neck_outputs)
encoder_states = [state_h_decoded, state_c_decoded]



decoder_inputs = Input(shape=input_shape)
decoder_lstm = LSTM(lstm_dim,
                    return_sequences=True,
                    unroll=unroll
                   )
decoder_outputs = decoder_lstm(decoder_inputs, initial_state=encoder_states)



decoder_dense = Dense(output_dim, activation='softmax')
decoder_outputs = decoder_dense(decoder_outputs)
#Define the model, that inputs the training vector for two places, and predicts one character ahead of the input
model = Model([encoder_inputs, decoder_inputs], decoder_outputs)



model.fit([X_train,X_train],Y_train,
                    epochs=50,   
                    batch_size=256,
                    shuffle=True,
                    callbacks=[h, rlr],
                    validation_data=[[X_test,X_test],Y_test])

我已经将输入拟合到模型的不同层中两次，这对于我遇到的问题很重要

为了解决内存问题，我开始使用fit_genorator()来看看它是否能解决这个问题，但通过“输入问题”来解决