BeamSearch sampling decoder

decoder_beam_search(
  object,
  cell,
  beam_width,
  embedding_fn = NULL,
  output_layer = NULL,
  length_penalty_weight = 0,
  coverage_penalty_weight = 0,
  reorder_tensor_arrays = TRUE,
  ...
)

Arguments

object	Model or layer object
cell	An RNNCell instance.
beam_width	integer, the number of beams.
embedding_fn	A callable that takes a vector tensor of ids (argmax ids).
output_layer	(Optional) An instance of tf.keras.layers.Layer, i.e., tf$keras$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.
coverage_penalty_weight	Float weight to penalize the coverage of source sentence. 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.
...	A list, other keyword arguments for initialization.

Value

None

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 `tile_batch()` (NOT `tf$tile`). - The `batch_size` argument passed to the `get_initial_state` method of this wrapper is equal to `true_batch_size * beam_width`. - The initial state created with `get_initial_state` above contains a `cell_state` value containing properly tiled final state from the encoder.