tf.contrib.rnn.DropoutWrapper

class tf.contrib.rnn.DropoutWrapper

class tf.contrib.rnn.core_rnn_cell.DropoutWrapper

See the guide: RNN and Cells (contrib) > Core RNN Cell wrappers (RNNCells that wrap other RNNCells)

Operator adding dropout to inputs and outputs of the given cell.

Properties

output_size

state_size

Methods

__init__(cell, input_keep_prob=1.0, output_keep_prob=1.0, seed=None)

Create a cell with added input and/or output dropout.

Dropout is never used on the state.

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 float 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 float and 1, no output dropout will be added.
  • seed: (optional) integer, the randomness seed.

Raises:

  • TypeError: if cell is not an RNNCell.
  • ValueError: if keep_prob is not between 0 and 1.

zero_state(batch_size, dtype)

Return zero-filled state tensor(s).

Args:

  • batch_size: int, float, or unit Tensor representing the batch size.
  • dtype: the data type to use for the state.

Returns:

If state_size is an int or TensorShape, then the return value is a N-D tensor of shape [batch_size x state_size] filled with zeros.

If 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 the shapes [batch_size x s] for each s in state_size.

Defined in tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.py.