View source on GitHub

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:

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

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.


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

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.



View source


View source

Return zero-filled state tensor(s).

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

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.