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tf.nn.rnn_cell.DropoutWrapper

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Class DropoutWrapper

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

Aliases:

  • Class tf.compat.v1.nn.rnn_cell.DropoutWrapper
  • Class tf.contrib.rnn.DropoutWrapper

__init__

View source

__init__(
    *args,
    **kwargs
)

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

If variational_recurrent is set to True (NOT the default behavior), then the same dropout mask is applied at every step, as described in: A Theoretically Grounded Application of Dropout in Recurrent Neural Networks. Y. Gal, Z. Ghahramani.

Otherwise a different dropout mask is applied at every time step.

Note, by default (unless a custom dropout_state_filter is provided), the memory state (c component of any LSTMStateTuple) passing through a DropoutWrapper is never modified. This behavior is described in the above article.

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 outgoing states of the cell. Note the state components to which dropout is applied when state_keep_prob is in (0, 1) are also determined by the argument dropout_state_filter_visitor (e.g. by default dropout is never applied to the c component of an LSTMStateTuple).
  • 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.
  • dropout_state_filter_visitor: (optional), default: (see below). Function that takes any hierarchical level of the state and returns a scalar or depth=1 structure of Python booleans describing which terms in the state should be dropped out. In addition, if the function returns True, dropout is applied across this sublevel. If the function returns False, dropout is not applied across this entire sublevel. Default behavior: perform dropout on all terms except the memory (c) state of LSTMCellState objects, and don't try to apply dropout to TensorArray objects: def dropout_state_filter_visitor(s): if isinstance(s, LSTMCellState): # Never perform dropout on the c state. return LSTMCellState(c=False, h=True) elif isinstance(s, TensorArray): return False return True

Raises:

  • TypeError: if cell is not an RNNCell, or keep_state_fn is provided but not callable.
  • ValueError: if any of the keep_probs are not between 0 and 1.

Properties

graph

DEPRECATED FUNCTION

output_size

scope_name

state_size

wrapped_cell

Methods

get_initial_state

View source

get_initial_state(
    inputs=None,
    batch_size=None,
    dtype=None
)

zero_state

View source

zero_state(
    batch_size,
    dtype
)