Explanation of GRU cell in Tensorflow?

Question

Following code of Tensorflow's GRUCell unit shows typical operations to get a updated hidden state, when previous hidden state is provided along with current input in the sequence.

  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

But I don't see any weights and biases here. e.g. my understanding was that getting r and u would require weights and biases to be multiplied with current input and/or hidden state to get an updated hidden state.

I have written a gru unit as follows:

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

Here I explicitly make use of weights Wx, Wr, Wz, Wh and biases br, bh, bz, etc. to get updated hidden state. These weights and biases are what get learned/tuned after training.

How can I make use of Tensorflow's built-in GRUCell to achieve the same result as above?

Answer 1

They are there you just don't see them in that code because the _linear function adds the weights and biases.

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