tf.contrib.rnn.DropoutWrapper
Defined in tensorflow/python/ops/rnn_cell_impl.py.
def __init__(self, cell, input_keep_prob=1.0, output_keep_prob=1.0,
state_keep_prob=1.0, variational_recurrent=False,
input_size=None, dtype=None, seed=None):
Args:
cell: an RNNCell, a projection to output_size is added to it.
input_keep_prob: unit Tensor or float between 0 and 1, input keep
probability; if it is constant and 1, no input dropout will be added.
output_keep_prob: unit Tensor or float between 0 and 1, output keep
probability; if it is constant and 1, no output dropout will be added.
state_keep_prob: unit Tensor or float between 0 and 1, output keep
probability; if it is constant and 1, no output dropout will be added.
State dropout is performed on the *output* states of the cell.
variational_recurrent: Python bool. If `True`, then the same
dropout pattern is applied across all time steps per run call.
If this parameter is set, `input_size` **must** be provided.
input_size: (optional) (possibly nested tuple of) `TensorShape` objects
containing the depth(s) of the input tensors expected to be passed in to
the `DropoutWrapper`. Required and used **iff**
`variational_recurrent = True` and `input_keep_prob < 1`.
dtype: (optional) The `dtype` of the input, state, and output tensors.
Required and used **iff** `variational_recurrent = True`.
seed: (optional) integer, the randomness seed.