tfa.seq2seq.CustomSampler

Base abstract class that allows the user to customize sampling.

Inherits From: Sampler

initialize_fn callable that returns (finished, next_inputs) for the first iteration.
sample_fn callable that takes (time, outputs, state) and emits tensor sample_ids.
next_inputs_fn callable that takes (time, outputs, state, sample_ids) and emits (finished, next_inputs, next_state).
sample_ids_shape Either a list of integers, or a 1-D Tensor of type int32, the shape of each value in the sample_ids batch. Defaults to a scalar.
sample_ids_dtype The dtype of the sample_ids tensor. Defaults to int32.

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

View source

initialize the sampler with the input tensors.

This method must be invoked exactly once before calling other methods of the Sampler.

Args
inputs A (structure of) input tensors, it could be a nested tuple or a single tensor.
**kwargs Other kwargs for initialization. It could contain tensors like mask for inputs, or non tensor parameter.

Returns
(initial_finished, initial_inputs).

next_inputs

View source

Returns (finished, next_inputs, next_state).

sample

View source

Returns sample_ids.