tf.contrib.seq2seq.ScheduledOutputTrainingHelper

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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.

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.

ValueError if sampling_probability is not a scalar or vector.

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

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initialize(
    name=None
)

Returns (initial_finished, initial_inputs).

next_inputs

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

next_inputs_fn for TrainingHelper.

sample

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

Returns sample_ids.