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The most basic RNN cell.
tf.compat.v1.nn.rnn_cell.BasicRNNCell( num_units, activation=None, reuse=None, name=None, dtype=None, **kwargs )
Note that this cell is not optimized for performance. Please use
tf.contrib.cudnn_rnn.CudnnRNNTanh for better performance on GPU.
num_units: int, The number of units in the RNN cell.
activation: Nonlinearity to use. Default:
tanh. It could also be string that is within Keras activation function names.
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.
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
Nonemeans use the type of the first input). Required when
buildis called before
**kwargs: Dict, keyword named properties for common layer attributes, like
trainableetc when constructing the cell from configs of get_config().
graph: DEPRECATED FUNCTION
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.
get_initial_state( inputs=None, batch_size=None, dtype=None )
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.
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.
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
[batch_size, s] for each s in