tf.contrib.seq2seq.ScheduledEmbeddingTrainingHelper

A training helper that adds scheduled sampling.

Inherits From: TrainingHelper

tf.contrib.seq2seq.ScheduledEmbeddingTrainingHelper(
    inputs, sequence_length, embedding, sampling_probability, time_major=False,
    seed=None, scheduling_seed=None, name=None
)

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

Args
inputs	A (structure of) input tensors.
sequence_length	An int32 vector tensor.
embedding	A callable that takes a vector tensor of ids (argmax ids), or the params argument for embedding_lookup.
sampling_probability	A 0D float32 tensor: the probability of sampling categorically from the output ids instead of reading directly from the inputs.
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.
name	Name scope for any created operations.

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.
inputs
sample_ids_dtype	DType of tensor returned by sample. Returns a DType.
sample_ids_shape	Shape of tensor returned by sample, excluding the batch dimension. Returns a TensorShape.
sequence_length

Methods

initialize

View source

initialize(
    name=None
)

Returns (initial_finished, initial_inputs).

next_inputs

View source

next_inputs(
    time, outputs, state, sample_ids, name=None
)

next_inputs_fn for TrainingHelper.

sample

View source

sample(
    time, outputs, state, name=None
)

Returns sample_ids.