tf.contrib.seq2seq.InferenceHelper

A helper to use during inference with a custom sampling function.

Inherits From: Helper

tf.contrib.seq2seq.InferenceHelper(
    sample_fn, sample_shape, sample_dtype, start_inputs, end_fn, next_inputs_fn=None
)

Args
`sample_fn`	A callable that takes `outputs` and emits tensor `sample_ids`.
`sample_shape`	Either a list of integers, or a 1-D Tensor of type `int32`, the shape of the each sample in the batch returned by `sample_fn`.
`sample_dtype`	the dtype of the sample returned by `sample_fn`.
`start_inputs`	The initial batch of inputs.
`end_fn`	A callable that takes `sample_ids` and emits a `bool` vector shaped `[batch_size]` indicating whether each sample is an end token.
`next_inputs_fn`	(Optional) A callable that takes `sample_ids` and returns the next batch of inputs. If not provided, `sample_ids` is used as the next batch of inputs.

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.