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RNN cell composed sequentially of multiple simple cells.
num_units = [128, 64] cells = [BasicLSTMCell(num_units=n) for n in num_units] stacked_rnn_cell = MultiRNNCell(cells)
__init__( cells, state_is_tuple=True )
Create a RNN cell composed sequentially of a number of RNNCells. (deprecated)
cells: list of RNNCells that will be composed in this order.
state_is_tuple: If True, accepted and returned states are n-tuples, where
n = len(cells). If False, the states are all concatenated along the column axis. This latter behavior will soon be deprecated.
ValueError: if cells is empty (not allowed), or at least one of the cells returns a state tuple but the flag
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