Keras双向"RuntimeError:您必须在使用模型之前对其进行编译."编译完成后 [英] Keras Bidirectional "RuntimeError: You must compile your model before using it." after compilation completed

问题描述

我正在尝试创建一个小的双向递归神经网络.模型本身编译时没有错误，但是在尝试拟合模型时出现错误，指出应该首先编译.请参见下面的代码段:

I'm trying to create a small Bidirectional recurrent NN. The model itself compiles without error, but when trying to fit the model I get the error stating I should compile first. Please see the code snippet below:

# fourth recurrent model, bidirectional
bidirectional_recurrent = Sequential()
bidirectional_recurrent.add(Bidirectional(GRU(32, input_shape=(int(lookback/steps), data_scaled.shape[-1]))))
bidirectional_recurrent.add(Dense(1))

bidirectional_recurrent.compile(optimizer='rmsprop', loss='mae')

bidirectional_recurrent_history = bidirectional_recurrent.fit_generator(train_gen, steps_per_epoch=500, epochs=40,
                                               validation_data=val_gen, validation_steps=val_steps)

RuntimeError:您必须先编译模型，然后再使用它.

RuntimeError: You must compile your model before using it.

我使用相同的设置来训练单向RNN，效果很好.任何帮助解决运行时错误的技巧都将受到赞赏. (重新启动内核没有帮助)
也许我没有正确实例化双向"?

I've used the same setup to train unidirectional RNN's which worked fine. Any tips to help solve the run-time error are appreciated. (restarting the kernel did not help)
Maybe I did not instantiate 'Bidirectional' correctly?

请注意:此问题与不同在"X"之前类型的问题
注意2:可以找到这里

Please note: This question is different from Do I need to compile before 'X' type of questions
Note2: R examples of the same code can be found here

推荐答案

找到了，
使用双向时，应将其视为一个层，将input_shape包含在Bidirectional()中而不是GRU()对象中即可解决此问题

Found it,
When using Bidirectional, it should be treated as a layer, shifting the input_shape to be contained in Bidirectional() instead of in the GRU() object solved the problem

如此

bidirectional_recurrent.add(Bidirectional(GRU(32, input_shape=(int(lookback/steps),
                            data_scaled.shape[-1]))))

成为

bidirectional_recurrent.add(Bidirectional(GRU(32), input_shape=(int(lookback/steps),
                            data_scaled.shape[-1])))

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