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tf.contrib.seq2seq.ScheduledOutputTrainingHelper

Class ScheduledOutputTrainingHelper

A training helper that adds scheduled sampling directly to outputs.

Inherits From: TrainingHelper

View source on GitHub

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

__init__

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__init__(
    inputs,
    sequence_length,
    sampling_probability,
    time_major=False,
    seed=None,
    next_inputs_fn=None,
    auxiliary_inputs=None,
    name=None
)

Initializer.

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.

Properties

batch_size

inputs

sample_ids_dtype

sample_ids_shape

sequence_length

Methods

initialize

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initialize(name=None)

next_inputs

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

sample

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