我尝试用Keras构建一个三层RNN。代码的一部分如下：
model = Sequential() model.add(Embedding(input_dim = 91, output_dim = 128, input_length =max_length)) model.add(GRUCell(units = self.neurons, dropout = self.dropval, bias_initializer = bias)) model.add(GRUCell(units = self.neurons, dropout = self.dropval, bias_initializer = bias)) model.add(GRUCell(units = self.neurons, dropout = self.dropval, bias_initializer = bias)) model.add(TimeDistributed(Dense(target.shape[2])))然后我遇到了这个错误：
call() missing 1 required positional argument: 'states'错误详情如下：
~/anaconda3/envs/hw3/lib/python3.5/site-packages/keras/models.py in add(self, layer)487 output_shapes=[self.outputs[0]._keras_shape])488 else:--> 489 output_tensor = layer(self.outputs[0])490 if isinstance(output_tensor, list):491 raise TypeError('All layers in a Sequential model ' ~/anaconda3/envs/hw3/lib/python3.5/site-packages/keras/engine/topology.py in __call__(self, inputs, **kwargs)601 602 # Actually call the layer, collecting output(s), mask(s), and shape(s).--> 603 output = self.call(inputs, **kwargs)604 output_mask = self.compute_mask(inputs, previous_mask)605 回答：
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不要在Keras中直接使用Cell类（例如
GRUCell或LSTMCell）。它们是计算单元，需要被相应的层包装。应该使用Layer类（例如GRU或LSTM）：model.add(GRU(units = self.neurons, dropout = self.dropval, bias_initializer = bias))model.add(GRU(units = self.neurons, dropout = self.dropval, bias_initializer = bias))model.add(GRU(units = self.neurons, dropout = self.dropval, bias_initializer = bias))LSTM和GRU使用它们对应的单元在所有时间步上进行计算。阅读这个Stack Overflow回答以了解它们之间的区别。 -
当你将多个RNN层堆叠在一起时，你需要将它们的
return_sequences参数设置为True，以便产生每个时间步的输出，这些输出将被下一层的RNN使用。请注意，最后一层RNN是否需要这样做取决于你的架构和你试图解决的问题：model.add(GRU(units = self.neurons, dropout = self.dropval, bias_initializer = bias, return_sequences=True))model.add(GRU(units = self.neurons, dropout = self.dropval, bias_initializer = bias, return_sequences=True))model.add(GRU(units = self.neurons, dropout = self.dropval, bias_initializer = bias))
