tfa.seq2seq.TrainingSampler

A training sampler that simply reads its inputs.

Inherits From: Sampler

tfa.seq2seq.TrainingSampler(
    time_major: bool = False
)

Returned sample_ids are the argmax of the RNN output logits.

time_major Python bool. Whether the tensors in inputs are time major. If False (default), they are assumed to be batch major.

ValueError if sequence_length is not a 1D tensor or mask is not a 2D boolean tensor.

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
)

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

View source

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.