tf.contrib.seq2seq.CustomHelper

Base abstract class that allows the user to customize sampling.

Inherits From: Helper

tf.contrib.seq2seq.CustomHelper(
    initialize_fn, sample_fn, next_inputs_fn, sample_ids_shape=None,
    sample_ids_dtype=None
)

Args
`initialize_fn`	callable that returns `(finished, next_inputs)` for the first iteration.
`sample_fn`	callable that takes `(time, outputs, state)` and emits tensor `sample_ids`.
`next_inputs_fn`	callable that takes `(time, outputs, state, sample_ids)` and emits `(finished, next_inputs, next_state)`.
`sample_ids_shape`	Either a list of integers, or a 1-D Tensor of type `int32`, the shape of each value in the `sample_ids` batch. Defaults to a scalar.
`sample_ids_dtype`	The dtype of the `sample_ids` tensor. Defaults to int32.

Attributes
`batch_size`	Batch size of tensor returned by `sample`. Returns a scalar int32 tensor.
`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`.

Attributes

batch_size

Batch size of tensor returned by sample.

Returns a scalar int32 tensor.

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.

initialize(
    name=None
)

Returns (initial_finished, initial_inputs).

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

Returns (finished, next_inputs, next_state).

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

Returns sample_ids.