tensorflow教程:tf.contrib.rnn.DropoutWrapper

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.