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tf.contrib.seq2seq.BasicDecoder

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Class BasicDecoder

Basic sampling decoder.

Inherits From: Decoder

__init__

View source

__init__(
    cell,
    helper,
    initial_state,
    output_layer=None
)

Initialize BasicDecoder.

Args:

  • cell: An RNNCell instance.
  • helper: A Helper instance.
  • initial_state: A (possibly nested tuple of...) tensors and TensorArrays. The initial state of the RNNCell.
  • output_layer: (Optional) An instance of tf.compat.v1.layers.Layer, i.e., tf.compat.v1.layers.Dense. Optional layer to apply to the RNN output prior to storing the result or sampling.

Raises:

  • TypeError: if cell, helper or output_layer have an incorrect type.

Properties

batch_size

output_dtype

output_size

tracks_own_finished

Describes whether the Decoder keeps track of finished states.

Most decoders will emit a true/false finished value independently at each time step. In this case, the dynamic_decode function keeps track of which batch entries are already finished, and performs a logical OR to insert new batches to the finished set.

Some decoders, however, shuffle batches / beams between time steps and dynamic_decode will mix up the finished state across these entries because it does not track the reshuffle across time steps. In this case, it is up to the decoder to declare that it will keep track of its own finished state by setting this property to True.

Returns:

Python bool.

Methods

finalize

View source

finalize(
    outputs,
    final_state,
    sequence_lengths
)

initialize

View source

initialize(name=None)

Initialize the decoder.

Args:

  • name: Name scope for any created operations.

Returns:

(finished, first_inputs, initial_state).

step

View source

step(
    time,
    inputs,
    state,
    name=None
)

Perform a decoding step.

Args:

  • time: scalar int32 tensor.
  • inputs: A (structure of) input tensors.
  • state: A (structure of) state tensors and TensorArrays.
  • name: Name scope for any created operations.

Returns:

(outputs, next_state, next_inputs, finished).