tfa.seq2seq.ScheduledEmbeddingTrainingSampler

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A training sampler that adds scheduled sampling.

Inherits From: TrainingSampler, Sampler

tfa.seq2seq.ScheduledEmbeddingTrainingSampler(
    sampling_probability: tfa.types.TensorLike,
    embedding_fn: Optional[Callable] = None,
    time_major: bool = False,
    seed: Optional[int] = None,
    scheduling_seed: Optional[TensorLike] = None
)

Returns -1s for sample_ids where no sampling took place; valid sample id values elsewhere.

Args
sampling_probability	A float32 0-D or 1-D tensor: the probability of sampling categorically from the output ids instead of reading directly from the inputs.
embedding_fn	A callable that takes a vector tensor of ids (argmax ids).
time_major	Python bool. Whether the tensors in inputs are time major. If False (default), they are assumed to be batch major.
seed	The sampling seed.
scheduling_seed	The schedule decision rule sampling seed.

Raises
ValueError	if sampling_probability is not a scalar or vector.

Attributes
batch_size	Batch size of tensor returned by sample. Returns a scalar int32 tensor. The return value might not available before the invocation of initialize(), in this case, ValueError is raised.
sample_ids_dtype	DType of tensor returned by sample. Returns a DType. The return value might not available before the invocation of initialize().
sample_ids_shape	Shape of tensor returned by sample, excluding the batch dimension. Returns a TensorShape. The return value might not available before the invocation of initialize().

Methods

initialize

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initialize(
    inputs, sequence_length=None, mask=None, embedding=None
)

Initialize the TrainSampler.

Args
inputs	A (structure of) input tensors.
sequence_length	An int32 vector tensor.
mask	A boolean 2D tensor.

Returns
(finished, next_inputs), a tuple of two items. The first item is a boolean vector to indicate whether the item in the batch has finished. The second item is the first slide of input data based on the timestep dimension (usually the second dim of the input).

next_inputs

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next_inputs(
    time, outputs, state, sample_ids
)

Returns (finished, next_inputs, next_state).

sample

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sample(
    time, outputs, state
)

Returns sample_ids.