tfa.seq2seq.CustomSampler
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Base abstract class that allows the user to customize sampling.
Inherits From: Sampler
tfa.seq2seq.CustomSampler(
initialize_fn: tfa.types.Initializer
,
sample_fn: Callable,
next_inputs_fn: Callable,
sample_ids_shape: Optional[TensorLike] = None,
sample_ids_dtype: tfa.types.AcceptableDTypes
= 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. The return value might not
available before the invocation of initialize(), in this case,
ValueError is raised.
|
sample_ids_dtype
|
DType of tensor returned by sample .
Returns a DType. The return value might not available before the
invocation of initialize().
|
sample_ids_shape
|
Shape of tensor returned by sample , excluding the batch dimension.
Returns a TensorShape . The return value might not available
before the invocation of initialize().
|
Methods
initialize
View source
initialize(
inputs, **kwargs
)
initialize the sampler with the input tensors.
This method must be invoked exactly once before calling other
methods of the Sampler.
Args |
inputs
|
A (structure of) input tensors, it could be a nested tuple or
a single tensor.
|
**kwargs
|
Other kwargs for initialization. It could contain tensors
like mask for inputs, or non tensor parameter.
|
Returns |
(initial_finished, initial_inputs) .
|
View source
next_inputs(
time, outputs, state, sample_ids
)
Returns (finished, next_inputs, next_state)
.
sample
View source
sample(
time, outputs, state
)
Returns sample_ids
.