tf.contrib.seq2seq.ScheduledOutputTrainingHelper

A training helper that adds scheduled sampling directly to outputs.

Inherits From: TrainingHelper

tf.contrib.seq2seq.ScheduledOutputTrainingHelper(
    inputs, sequence_length, sampling_probability, time_major=False, seed=None,
    next_inputs_fn=None, auxiliary_inputs=None, name=None
)

Returns False for sample_ids where no sampling took place; True elsewhere.

Args
inputs	A (structure) of input tensors.
sequence_length	An int32 vector tensor.
sampling_probability	A 0D float32 tensor: the probability of sampling from the outputs 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.
next_inputs_fn	(Optional) callable to apply to the RNN outputs to create the next input when sampling. If None (default), the RNN outputs will be used as the next inputs.
auxiliary_inputs	An optional (structure of) auxiliary input tensors with a shape that matches inputs in all but (potentially) the final dimension. These tensors will be concatenated to the sampled output or the inputs when not sampling for use as the next input.
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.