bn_gru.py 文件源码

def __call__(self, inputs, state, scope=None):
    """Gated recurrent unit (GRU) with nunits cells."""
    with _checked_scope(self, scope or "gru_cell"):
      with vs.variable_scope("gates"):  # Reset gate and update gate.
        # We start with bias of 1.0 to not reset and not update.
        value = sigmoid(_linear(
          [inputs, state], 2 * self._num_units, True, 1.0))
        r, u = array_ops.split(
            value=value,
            num_or_size_splits=2,
            axis=1)
      with vs.variable_scope("candidate"):
        res = self._activation(_linear([inputs, r * state],
                                     self._num_units, True))

        if self._batch_norm:
          c = batch_norm(res,
                         center=True, scale=True,
                         is_training=self._is_training,
                         scope='bn1')
        else:
          c = res

      new_h = u * state + (1 - u) * c
    return new_h, new_h