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tf.nn.rnn_cell.GRUCell

View source on GitHub

Class GRUCell

Gated Recurrent Unit cell (cf.

Inherits From: LayerRNNCell

Aliases:

  • Class tf.compat.v1.nn.rnn_cell.GRUCell
  • Class tf.contrib.rnn.GRUCell

http://arxiv.org/abs/1406.1078).

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().

__init__

View source

__init__(
    num_units,
    activation=None,
    reuse=None,
    kernel_initializer=None,
    bias_initializer=None,
    name=None,
    dtype=None,
    **kwargs
)

DEPRECATED FUNCTION

Properties

graph

DEPRECATED FUNCTION

output_size

scope_name

state_size

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.