tfa.seq2seq.Sampler
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Interface for implementing sampling in seq2seq decoders.
Sampler classes implement the logic of sampling from the decoder output distribution
and producing the inputs for the next decoding step. In most cases, they should not be
used directly but passed to a tfa.seq2seq.BasicDecoder instance that will manage the
sampling.
Here is an example using a training sampler directly to implement a custom decoding loop:
batch_size = 4max_time = 7hidden_size = 16sampler = tfa.seq2seq.TrainingSampler()cell = tf.keras.layers.LSTMCell(hidden_size)input_tensors = tf.random.uniform([batch_size, max_time, hidden_size])initial_finished, initial_inputs = sampler.initialize(input_tensors)cell_input = initial_inputscell_state = cell.get_initial_state(initial_inputs)for time_step in tf.range(max_time):cell_output, cell_state = cell(cell_input, cell_state)sample_ids = sampler.sample(time_step, cell_output, cell_state)finished, cell_input, cell_state = sampler.next_inputs(time_step, cell_output, cell_state, sample_ids)if tf.reduce_all(finished):break
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 |
Methods
initialize
@abc.abstractmethodinitialize( 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).
|
next_inputs
@abc.abstractmethodnext_inputs( time, outputs, state, sample_ids )
Returns (finished, next_inputs, next_state).
sample
@abc.abstractmethodsample( time, outputs, state )
Returns sample_ids.