layer.py 文件源码

def connect(self, inputs, mask, is_train):
    max_length = inputs.shape[0]
    batch_size = inputs.shape[1]
    outputs_info = [tensor.alloc(numpy_floatX(0.), batch_size, self.hidden_dim),
            tensor.alloc(numpy_floatX(0.), batch_size, self.hidden_dim)]

    # Dropout layers
    self.is_train = is_train
    if self.recurrent_dropout_layer != None:
      self.recurrent_dropout_layer.generate_mask([batch_size, self.hidden_dim], is_train)

    proj_inputs = tensor.dot(inputs, self.W) + self.b
    rval, _ = theano.scan(self._step, # Scan function
                sequences=[inputs, proj_inputs, mask], # Input sequence
                outputs_info=outputs_info,
                name=_p(self.prefix, '_layers'),
                n_steps=max_length) # scan steps
    return rval[0]