tf.compat.v1.nn.rnn_cell.GRUCell

Gated Recurrent Unit cell.

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.

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)

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

zero_state

View source