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Gated Recurrent Unit cell (cf.
tf.compat.v1.nn.rnn_cell.GRUCell(
num_units, activation=None, reuse=None, kernel_initializer=None,
bias_initializer=None, name=None, dtype=None, **kwargs
)
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.
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().graph
: DEPRECATED FUNCTION
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Stop using this property because tf.layers layers no longer track their 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.
get_initial_state
get_initial_state(
inputs=None, batch_size=None, dtype=None
)
zero_state
zero_state(
batch_size, dtype
)
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
.