tfa.seq2seq.SampleEmbeddingSampler

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An inference sampler that randomly samples from the output distribution.

Inherits From: GreedyEmbeddingSampler, Sampler

tfa.seq2seq.SampleEmbeddingSampler(
    embedding_fn: Optional[Callable] = None,
    softmax_temperature: Optional[TensorLike] = None,
    seed: Optional[TensorLike] = None
)

Uses sampling (from a distribution) instead of argmax and passes the result through an embedding layer to get the next input.

embedding_fn (Optional) A callable that takes a vector tensor of ids (argmax ids). The returned tensor will be passed to the decoder input.
softmax_temperature (Optional) float32 scalar, value to divide the logits by before computing the softmax. Larger values (above 1.0) result in more random samples, while smaller values push the sampling distribution towards the argmax. Must be strictly greater than 0. Defaults to 1.0.
seed (Optional) The sampling seed.

ValueError if start_tokens is not a 1D tensor or end_token is not a scalar.

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(
    embedding, start_tokens=None, end_token=None
)

Initialize the GreedyEmbeddingSampler.

Args
embedding tensor that contains embedding states matrix. It will be used to generate generate outputs with start_tokens and end_token. The embedding will be ignored if the embedding_fn has been provided at init().
start_tokens int32 vector shaped [batch_size], the start tokens.
end_token int32 scalar, the token that marks end of decoding.

Returns
Tuple of two items: (finished, self.start_inputs).

Raises
ValueError if start_tokens is not a 1D tensor or end_token is not a scalar.

next_inputs

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next_inputs(
    time, outputs, state, sample_ids
)

next_inputs_fn for GreedyEmbeddingHelper.

sample

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sample(
    time, outputs, state
)

sample for SampleEmbeddingHelper.