GRU in tensorflow

GRUの中身をいろいろ見たい場合、定義したグラフからスコープ変数を元に全Variableは取ってくることはできる。
しかし、update, resetはGatesにまとめられていたり、隠れ状態がCandidateにまとめられていたり、それぞれの重み、バイアスパラメータがあったり、よくわからないので中身を見る必要がある。。。
どうやら、行列演算をまとめている。

そうすると、_linear関数の中身も見る必要がある。

http://stackoverflow.com/questions/38692531/explanation-of-gru-cell-in-tensorflow



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    """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



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