Keras GRUCell 缺少 1 个必需的位置参数：'states'

Question

我尝试用 Keras 构建一个 3 层 RNN。部分代码在这里：

    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 

Answer 1

1. 不要直接在 Keras 中使用 Cell 类（即GRUCell或LSTMCell）。它们是由相应层包裹的计算单元。而是使用 Layer 类（即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))
   

   该LSTM和GRU使用其相应的细胞在所有时间步长进行计算。阅读此SO 答案以了解有关它们差异的更多信息。

2. 当你堆叠在彼此需要其设置的顶部的多个RNN层return_sequences参数True，以便产生每个时步，而这又是使用由下一RNN层的输出。请注意，您可能会也可能不会在最后一个 RNN 层上执行此操作（这取决于您的架构和您尝试解决的问题）：

   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))