Missed TensorFlow World? Check out the recap. Learn more

tf.contrib.rnn.UGRNNCell

View source on GitHub

Class UGRNNCell

Update Gate Recurrent Neural Network (UGRNN) cell.

Inherits From: RNNCell

Compromise between a LSTM/GRU and a vanilla RNN. There is only one gate, and that is to determine whether the unit should be integrating or computing instantaneously. This is the recurrent idea of the feedforward Highway Network.

This implements the recurrent cell from the paper:

https://arxiv.org/abs/1611.09913

Jasmine Collins, Jascha Sohl-Dickstein, and David Sussillo. "Capacity and Trainability in Recurrent Neural Networks" Proc. ICLR 2017.

__init__

View source

__init__(
    num_units,
    initializer=None,
    forget_bias=1.0,
    activation=tf.math.tanh,
    reuse=None
)

Initialize the parameters for an UGRNN cell.

Args:

  • num_units: int, The number of units in the UGRNN cell
  • initializer: (optional) The initializer to use for the weight matrices.
  • forget_bias: (optional) float, default 1.0, The initial bias of the forget gate, used to reduce the scale of forgetting at the beginning of the training.
  • activation: (optional) Activation function of the inner states. Default is tf.tanh.
  • reuse: (optional) Python boolean describing whether to reuse variables in an existing scope. If not True, and the existing scope already has the given variables, an error is raised.

Properties

graph

DEPRECATED FUNCTION

output_size

Integer or TensorShape: size of outputs produced by this cell.

scope_name

state_size

size(s) of state(s) used by this cell.

It can be represented by an Integer, a TensorShape or a tuple of Integers or TensorShapes.

Methods

get_initial_state

View source

get_initial_state(
    inputs=None,
    batch_size=None,
    dtype=None
)

zero_state

View source

zero_state(
    batch_size,
    dtype
)

Return zero-filled state tensor(s).

Args:

  • batch_size: int, float, or unit Tensor representing the batch size.
  • dtype: the data type to use for the state.

Returns:

If state_size is an int or TensorShape, then the return value is a N-D tensor of shape [batch_size, state_size] filled with zeros.

If state_size is a nested list or tuple, then the return value is a nested list or tuple (of the same structure) of 2-D tensors with the shapes [batch_size, s] for each s in state_size.