BasicDecoder¶

class paddle.fluid.layers.BasicDecoder(cell, helper, output_fn=None)[source]

BasicDecoder is a subclass of Decoder and assembles a RNNCell and DecodeHelper instance as members, where the DecodeHelper helps to implement customed decoding strategies.. It performs one decoding step as following steps:

1. Perform cell_outputs, cell_states = cell.call(inputs, states) to get outputs and new states from cell.

2. Perform sample_ids = helper.sample(time, cell_outputs, cell_states) to sample ids as decoded results of the current time step.

3. Perform finished, next_inputs, next_states = helper.next_inputs(time, cell_outputs, cell_states, sample_ids) to generate inputs, states and finished status for the next decoding step.

Examples

import paddle.fluid as fluid
import paddle.fluid.layers as layers
trg_emb = fluid.data(name="trg_emb",
                     shape=[None, None, 128],
                     dtype="float32")

trg_embeder = lambda x: fluid.embedding(
    x, size=[10000, 128], param_attr=fluid.ParamAttr(name="trg_embedding"))
output_layer = lambda x: layers.fc(x,
                                size=10000,
                                num_flatten_dims=len(x.shape) - 1,
                                param_attr=fluid.ParamAttr(name=
                                                        "output_w"),
                                bias_attr=False)
helper = layers.SampleEmbeddingHelper(trg_embeder, start_tokens=0, end_token=1)
decoder_cell = layers.GRUCell(hidden_size=128)
decoder = layers.BasicDecoder(decoder_cell, helper, output_fn=output_layer)
outputs = layers.dynamic_decode(
    decoder=decoder, inits=decoder_cell.get_initial_states(encoder_output))

initialize(initial_cell_states)

BasicDecoder initialization includes helper initialization and cell initialization, and cell initialization uses initial_cell_states as the result directly.

Parameters: initial_cell_states (Variable) – A (possibly nested structure of) tensor variable[s]. An argument provided by the caller dynamic_decode.
Returns: A tuple( :code:(initial_inputs, initial_cell_states, finished)` ). initial_inputs and initial_states both are a (possibly nested structure of) tensor variable[s], and finished is a tensor with bool data type. initial_inputs and finished are the results of helper.initialize(), and initial_cell_states is same as the input argument counterpart.
Return type: tuple

class OutputWrapper

The structure for the returned value outputs of decoder.step. A namedtuple includes cell_outputs, sample_ids as fields.

cell_outputs: Alias for field number 0

count(value) → integer -- return number of occurrences of value

index(value[, start[, stop]]) → integer -- return first index of value.: Raises ValueError if the value is not present.

sample_ids: Alias for field number 1

step(time, inputs, states, **kwargs)

Perform one decoding step as following steps:

1. Perform cell_outputs, cell_states = cell.call(inputs, states) to get outputs and new states from cell.

2. Perform sample_ids = helper.sample(time, cell_outputs, cell_states) to sample ids as decoded results of the current time step.

3. Perform finished, next_inputs, next_states = helper.next_inputs(time, cell_outputs, cell_states, sample_ids) to generate inputs, states and finished status for the next decoding step.

Parameters

time (Variable) – An int64 tensor with shape [1] provided by the caller, representing the current time step number of decoding.
inputs (Variable) – A tensor variable. It is same as initial_inputs returned by initialize() for the first decoding step and next_inputs returned by step() for the others.
states (Variable) – A structure of tensor variables. It is same as the initial_cell_states returned by initialize() for the first decoding step and next_states returned by step() for the others.
**kwargs – Additional keyword arguments, provided by the caller dynamic_decode.

Returns

A tuple( (outputs, next_states, next_inputs, finished) ). outputs is a namedtuple(including cell_outputs, sample_ids, as fields) of tensor variables, where cell_outputs is the result fof cell.call() and sample_ids is the result of helper.sample(). next_states and next_inputs have the same structure, shape and data type as the input arguments states and inputs separately. finished is a bool tensor with shape [batch_size].

Return type

tuple

finalize(outputs, final_states, sequence_lengths)

Called once after the decoding iterations if implemented.

Parameters

outputs (Variable) – A (possibly nested structure of) tensor variable[s]. The structure and data type is same as output_dtype. The tensor stacks all time steps’ output thus has shape \([time\_step, batch\_size, ...]\) , which is done by the caller.
final_states (Variable) – A (possibly nested structure of) tensor variable[s]. It is the next_states returned by decoder.step at last decoding step, thus has the same structure, shape and data type with states at any time step.

Returns

A tuple( (final_outputs, final_states) ). final_outputs and final_states both are a (possibly nested structure of) tensor variable[s].

Return type

tuple