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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 |
sequence_length
|
Methods
initialize
initialize(
name=None
)
Returns (initial_finished, initial_inputs)
.
next_inputs
next_inputs(
time, outputs, state, sample_ids, name=None
)
next_inputs_fn for TrainingHelper.
sample
sample(
time, outputs, state, name=None
)
Returns sample_ids
.