(n_units, kernel_size=(3, 3))
return GeneralGRUCell(
      candidate_transform=BuildConv,
      memory_transform_fn=None,
      gate_nonlinearity=activation_fns.Sigmoid,
      candidate_nonlinearity=activation_fns.Tanh)
Builds a convolutional GRU.

  Paper: https://arxiv.org/abs/1511.06432.

  Args:
    n_units: Number of hidden units
    kernel_size: Kernel size for convolution

  Returns:
    A Stax model representing a GRU cell with convolution transforms.
Builds a convolutional GRU.
Builds a convolutional GRU .
def ConvGRUCell(n_units, kernel_size=(3, 3)):
  """Builds a convolutional GRU.

  Paper: https://arxiv.org/abs/1511.06432.

  Args:
    n_units: Number of hidden units
    kernel_size: Kernel size for convolution

  Returns:
    A Stax model representing a GRU cell with convolution transforms.
  """

  def BuildConv():
    return convolution.Conv(
        filters=n_units, kernel_size=kernel_size, padding='SAME')

  return GeneralGRUCell(
      candidate_transform=BuildConv,
      memory_transform_fn=None,
      gate_nonlinearity=activation_fns.Sigmoid,
      candidate_nonlinearity=activation_fns.Tanh)
def ConvGRUCell ( n_units , kernel_size = ( 3 , 3 ) ) : def BuildConv ( ) : return convolution . Conv ( filters = n_units , kernel_size = kernel_size , padding = 'SAME' ) return GeneralGRUCell ( candidate_transform = BuildConv , memory_transform_fn = None , gate_nonlinearity = activation_fns . Sigmoid , candidate_nonlinearity = activation_fns . Tanh )