tf.compat.v1.nn.rnn_cell.GRUCell

Gated Recurrent Unit cell.

Inherits From: RNNCell, Layer, Layer, Module

tf.compat.v1.nn.rnn_cell.GRUCell(
    num_units, activation=None, reuse=None, kernel_initializer=None,
    bias_initializer=None, name=None, dtype=None, **kwargs
)

Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnGRU for better performance on GPU, or tf.contrib.rnn.GRUBlockCellV2 for better performance on CPU.

Args
num_units	int, The number of units in the GRU cell.
activation	Nonlinearity to use. Default: 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.
kernel_initializer	(optional) The initializer to use for the weight and projection matrices.
bias_initializer	(optional) The initializer to use for the bias.
name	String, the name of the layer. Layers with the same name will share weights, but to avoid mistakes we require reuse=True in such cases.
dtype	Default dtype of the layer (default of None means use the type of the first input). Required when build is called before call.
**kwargs	Dict, keyword named properties for common layer attributes, like trainable etc when constructing the cell from configs of get_config(). References: Learning Phrase Representations using RNN Encoder Decoder for Statistical Machine Translation: Cho et al., 2014 (pdf)

Attributes
graph
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.