Tensorflow中GRU cell的解释？

Question

Tensorflow 的 GRUCell 单元的以下代码显示了在序列中提供先前的隐藏状态以及当前输入时获取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

但我在这里没有看到任何 weights 和 biases。例如我的理解是，获得 r 和 u 需要将权重和偏差乘以当前输入 and/or 隐藏状态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

这里我明确地使用权重 Wx, Wr, Wz, Wh 和偏差 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得到的 learned/tuned。

如何利用 Tensorflow 的内置 GRUCell 达到与上述相同的结果？

Answer 1

它们就在那里，您只是在该代码中看不到它们，因为 _linear 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

Tensorflow中GRU cell的解释？

Explanation of GRU cell in Tensorflow?

neural-network

tensorflow

recurrent-neural-network

gated-recurrent-unit