GRU.py 文件源码

def call(self, inputs, state):
    """Gated recurrent unit (GRU) with nunits cells."""
    with vs.variable_scope("gates"):  # Reset gate and update gate.
      # We start with bias of 1.0 to not reset and not update.
      bias_ones = self._bias_initializer
      if self._bias_initializer is None:
        dtype = [a.dtype for a in [inputs, state]][0]
        bias_ones = init_ops.constant_initializer(1.0, dtype=dtype)
      value = math_ops.sigmoid(
          linear([inputs, state], 2 * self._num_units, True, bias_ones,
                  self._kernel_initializer))
      r, u = array_ops.split(value=value, num_or_size_splits=2, axis=1)
    with vs.variable_scope("candidate"):
      c = self._activation(
          linear([inputs, r * state], self._num_units, True,
                  self._bias_initializer, self._kernel_initializer))
    #   recurrent dropout as proposed in https://arxiv.org/pdf/1603.05118.pdf (currently disabled)
      #if self._is_training and Params.dropout is not None:
        #c = tf.nn.dropout(c, 1 - Params.dropout)
    new_h = u * state + (1 - u) * c
    return new_h, new_h