tf.contrib.seq2seq.BeamSearchDecoder

Class BeamSearchDecoder

Inherits From: Decoder

Defined in tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py.

BeamSearch sampling decoder.

NOTE If you are using the BeamSearchDecoder with a cell wrapped in AttentionWrapper, then you must ensure that:

• The encoder output has been tiled to beam_width via tf.contrib.seq2seq.tile_batch (NOT tf.tile).
• The batch_size argument passed to the zero_state method of this wrapper is equal to true_batch_size * beam_width.
• The initial state created with zero_state above contains a cell_state value containing properly tiled final state from the encoder.

An example:

tiled_encoder_outputs = tf.contrib.seq2seq.tile_batch(
    encoder_outputs, multiplier=beam_width)
tiled_encoder_final_state = tf.conrib.seq2seq.tile_batch(
    encoder_final_state, multiplier=beam_width)
tiled_sequence_length = tf.contrib.seq2seq.tile_batch(
    sequence_length, multiplier=beam_width)
attention_mechanism = MyFavoriteAttentionMechanism(
    num_units=attention_depth,
    memory=tiled_inputs,
    memory_sequence_length=tiled_sequence_length)
attention_cell = AttentionWrapper(cell, attention_mechanism, ...)
decoder_initial_state = attention_cell.zero_state(
    dtype, batch_size=true_batch_size * beam_width)
decoder_initial_state = decoder_initial_state.clone(
    cell_state=tiled_encoder_final_state)

Properties

batch_size

The batch size of input values.

output_dtype

A (possibly nested tuple of...) dtype[s].

output_size

A (possibly nested tuple of...) integer[s] or TensorShape object[s].

tracks_own_finished

The BeamSearchDecoder shuffles its beams and their finished state.

For this reason, it conflicts with the dynamic_decode function's tracking of finished states. Setting this property to true avoids early stopping of decoding due to mismanagement of the finished state in dynamic_decode.

Returns:

True.

Methods

__init__

__init__(
    cell,
    embedding,
    start_tokens,
    end_token,
    initial_state,
    beam_width,
    output_layer=None,
    length_penalty_weight=0.0,
    reorder_tensor_arrays=True
)

Initialize the BeamSearchDecoder.

Args:

• cell: An RNNCell instance.
• embedding: A callable that takes a vector tensor of ids (argmax ids), or the params argument for embedding_lookup.
• start_tokens: int32 vector shaped [batch_size], the start tokens.
• end_token: int32 scalar, the token that marks end of decoding.
• initial_state: A (possibly nested tuple of...) tensors and TensorArrays.
• beam_width: Python integer, the number of beams.
• output_layer: (Optional) An instance of tf.layers.Layer, i.e., tf.layers.Dense. Optional layer to apply to the RNN output prior to storing the result or sampling.
• length_penalty_weight: Float weight to penalize length. Disabled with 0.0.
• reorder_tensor_arrays: If True, TensorArrays' elements within the cell state will be reordered according to the beam search path. If the TensorArray can be reordered, the stacked form will be returned. Otherwise, the TensorArray will be returned as is. Set this flag to False if the cell state contains TensorArrays that are not amenable to reordering.

Raises:

• TypeError: if cell is not an instance of RNNCell, or output_layer is not an instance of tf.layers.Layer.
• ValueError: If start_tokens is not a vector or end_token is not a scalar.

finalize

finalize(
    outputs,
    final_state,
    sequence_lengths
)

Finalize and return the predicted_ids.

Args:

• outputs: An instance of BeamSearchDecoderOutput.
• final_state: An instance of BeamSearchDecoderState. Passed through to the output.
• sequence_lengths: An int64 tensor shaped [batch_size, beam_width]. The sequence lengths determined for each beam during decode. NOTE These are ignored; the updated sequence lengths are stored in final_state.lengths.

Returns:

• outputs: An instance of FinalBeamSearchDecoderOutput where the predicted_ids are the result of calling _gather_tree.
• final_state: The same input instance of BeamSearchDecoderState.

initialize

initialize(name=None)

Initialize the decoder.

Args:

• name: Name scope for any created operations.

Returns:

(finished, start_inputs, initial_state).

step

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).