|View source on GitHub|
A Sampler for use during training.
Only reads inputs.
Returned sample_ids are the argmax of the RNN output logits.
time_major: Python bool. Whether the tensors in
inputsare time major. If
False(default), they are assumed to be batch major.
sequence_lengthis not a 1D tensor.
Batch size of tensor returned by
Returns a scalar int32 tensor. The return value might not available before the invocation of initialize(), in this case, ValueError is raised.
DType of tensor returned by
Returns a DType. The return value might not available before the invocation of initialize().
Shape of tensor returned by
sample, excluding the batch dimension.
TensorShape. The return value might not available
before the invocation of initialize().
initialize( inputs, sequence_length=None )
Initialize the TrainSampler.
inputs: A (structure of) input tensors.
sequence_length: An int32 vector tensor.
(finished, next_inputs), a tuple of two items. The first item is a boolean vector to indicate whether the item in the batch has finished. The second item is the first slide of input data based on the timestep dimension (usually the second dim of the input).
next_inputs( time, outputs, state, sample_ids )
(finished, next_inputs, next_state).
sample( time, outputs, state )