NAME

            

              
              
AI::MXNet::Gluon::Data::Sampler
            
          

DESCRIPTION

            

              
              
Base class for samplers.
All samplers should subclass AI::MXNet::Gluon::Data::Sampler 
and define method 'len' and 'next'
methods.
            
          

NAME

            

              
              
AI::MXNet::Gluon::Data::Sampler::SequentialSampler
            
          

DESCRIPTION

            

              
              
Samples elements from [0, length) sequentially.
Parameters
----------
length : int
    Length of the sequence.
            
          

NAME

            

              
              
AI::MXNet::Gluon::Data::Sampler::RandomSampler
            
          

DESCRIPTION

            

              
              
Samples elements from [0, length) randomly without replacement.
Parameters
----------
length : int
    Length of the sequence.
            
          

NAME

            

              
              
AI::MXNet::Gluon::Data::Sampler::BatchSampler
            
          

DESCRIPTION

            

              
              
Wraps over another AI::MXNet::Gluon::Data::Sampler and return mini-batches of samples.
Parameters
----------
sampler : AI::MXNet::Gluon::Data::Sampler
    The source Sampler.
batch_size : int
    Size of mini-batch.
last_batch : {'keep', 'discard', 'rollover'}
    Specifies how the last batch is handled if batch_size does not evenly
    divide sequence length.
    If 'keep', the last batch will be returned directly, but will contain
    less element than `batch_size` requires.
    If 'discard', the last batch will be discarded.
    If 'rollover', the remaining elements will be rolled over to the next
    iteration.
Examples
--------
>>> $sampler = gluon->data->SequentialSampler(10)
>>> $batch_sampler = gluon->data->BatchSampler($sampler, batch_size => 3, last_batch => 'keep');
>>> @{ $batch_sampler }
[[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]]