def generate_batches(self,
recurrent_net,
batch_size,
max_seqs=-1,
seq_drop=0.0,
max_seq_length=sys.maxsize,
shuffle_batches=False,
used_data_keys=None):
"""
:type recurrent_net: bool
:type batch_size: int
:type max_seqs: int
:type shuffle_batches: bool
:param set(str)|None used_data_keys:
:rtype: BatchSetGenerator
"""
return BatchSetGenerator(
dataset=self,
generator=self._generate_batches(recurrent_net=recurrent_net,
batch_size=batch_size,
max_seqs=max_seqs,
seq_drop=seq_drop,
max_seq_length=max_seq_length,
used_data_keys=used_data_keys),
shuffle_batches=shuffle_batches,
cache_whole_epoch=self.batch_set_generator_cache_whole_epoch())
def generate_batches(self, shuffle_batches=False, **kwargs):
"""
:param bool shuffle_batches:
:param kwargs: will be passed to :func:`_generate_batches`
:rtype: BatchSetGenerator
"""
return BatchSetGenerator(
dataset=self,
generator=self._generate_batches(**kwargs),
shuffle_batches=shuffle_batches,
cache_whole_epoch=self.batch_set_generator_cache_whole_epoch())
def forward_single(self, dataset, seq_idx, output_layer_name=None):
"""
Forwards a single sequence.
If you want to perform search, and get a number of hyps out, use :func:`search_single`.
:param Dataset.Dataset dataset:
:param int seq_idx:
:param str|None output_layer_name: e.g. "output". if not set, will read from config "forward_output_layer"
:return: numpy array, output in time major format (time,dim)
:rtype: numpy.ndarray
"""
from EngineBatch import Batch, BatchSetGenerator
batch = Batch()
batch.init_with_one_full_sequence(seq_idx=seq_idx, dataset=dataset)
batch_generator = iter([batch])
batches = BatchSetGenerator(dataset, generator=batch_generator)
forwarder = ClassificationTaskThread(self.network, self.devices,
dataset, batches)
forwarder.join()
assert forwarder.output.shape[1] == 1
return forwarder.output[:, 0]