|View source on GitHub|
Temporary GRUBlockCell impl with a different variable naming scheme.
Only differs from GRUBlockCell by variable names.
__init__( num_units=None, cell_size=None, reuse=None, name='gru_cell' )
Initialize the Block GRU cell. (deprecated arguments)
num_units: int, The number of units in the GRU cell.
cell_size: int, The old (deprecated) name for
reuse: (optional) 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. By default this is "lstm_cell", for variable-name compatibility with
ValueError: if both cell_size and num_units are not None; or both are None.
Integer or TensorShape: size of outputs produced by this cell.
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