Tensorflow中GRU单元的解释？

以下是 Tensorflow 的代码GRUCell单元显示了当先前的隐藏状态与序列中的当前输入一起提供时获得更新的隐藏状态的典型操作。

  def __call__(self, inputs, state, scope=None):
    """Gated recurrent unit (GRU) with nunits cells."""
    with vs.variable_scope(scope or type(self).__name__):  # "GRUCell"
      with vs.variable_scope("Gates"):  # Reset gate and update gate.
        # We start with bias of 1.0 to not reset and not update.
        r, u = array_ops.split(1, 2, _linear([inputs, state],
                                             2 * self._num_units, True, 1.0))
        r, u = sigmoid(r), sigmoid(u)
      with vs.variable_scope("Candidate"):
        c = self._activation(_linear([inputs, r * state],
                                     self._num_units, True))
      new_h = u * state + (1 - u) * c
return new_h, new_h

但我没有看到任何weights and biases这里。 例如我的理解是r and u需要将权重和偏差与当前输入和/或隐藏状态相乘以获​​得更新的隐藏状态。

我写了一个gru单元如下：

def gru_unit(previous_hidden_state, x):
    r  = tf.sigmoid(tf.matmul(x, Wr) + br)
    z  = tf.sigmoid(tf.matmul(x, Wz) + bz)
    h_ = tf.tanh(tf.matmul(x, Wx) + tf.matmul(previous_hidden_state, Wh) * r)
    current_hidden_state = tf.mul((1 - z), h_) + tf.mul(previous_hidden_state, z)
    return current_hidden_state

这里我明确地使用了权重Wx, Wr, Wz, Wh和偏见br, bh, bz等来获取更新的隐藏状态。这些权重和偏差是训练后学习/调整的。

如何利用 Tensorflow 的内置功能GRUCell达到与上面相同的结果？

它们在那里，您只是在代码中看不到它们，因为 _线性 函数添加了权重和偏差。

r, u = array_ops.split(1, 2, _linear([inputs, state],
                                             2 * self._num_units, True, 1.0))

...

def _linear(args, output_size, bias, bias_start=0.0, scope=None):
  """Linear map: sum_i(args[i] * W[i]), where W[i] is a variable.

  Args:
    args: a 2D Tensor or a list of 2D, batch x n, Tensors.
    output_size: int, second dimension of W[i].
    bias: boolean, whether to add a bias term or not.
    bias_start: starting value to initialize the bias; 0 by default.
    scope: VariableScope for the created subgraph; defaults to "Linear".

  Returns:
    A 2D Tensor with shape [batch x output_size] equal to
    sum_i(args[i] * W[i]), where W[i]s are newly created matrices.

  Raises:
    ValueError: if some of the arguments has unspecified or wrong shape.
  """
  if args is None or (nest.is_sequence(args) and not args):
    raise ValueError("`args` must be specified")
  if not nest.is_sequence(args):
    args = [args]

  # Calculate the total size of arguments on dimension 1.
  total_arg_size = 0
  shapes = [a.get_shape().as_list() for a in args]
  for shape in shapes:
    if len(shape) != 2:
      raise ValueError("Linear is expecting 2D arguments: %s" % str(shapes))
    if not shape[1]:
      raise ValueError("Linear expects shape[1] of arguments: %s" % str(shapes))
    else:
      total_arg_size += shape[1]

  # Now the computation.
  with vs.variable_scope(scope or "Linear"):
    matrix = vs.get_variable("Matrix", [total_arg_size, output_size])
    if len(args) == 1:
      res = math_ops.matmul(args[0], matrix)
    else:
      res = math_ops.matmul(array_ops.concat(1, args), matrix)
    if not bias:
      return res
    bias_term = vs.get_variable(
        "Bias", [output_size],
        initializer=init_ops.constant_initializer(bias_start))
  return res + bias_term