def __init__(self,
training_options,
optimization_options,
network,
vocabulary,
scorer,
training_files,
sampling,
validation_iter,
state,
profile=False):
"""Creates the optimizer and initializes the training process.
:type training_options: dict
:param training_options: a dictionary of training options
:type optimization_options: dict
:param optimization_options: a dictionary of optimization options
:type network: Network
:param network: a neural network to be trained
:type vocabulary: Vocabulary
:param vocabulary: vocabulary that provides mapping between words and
word IDs
:type scorer: TextScorer
:param scorer: a text scorer for computing validation set perplexity
:type training_files: list of file objects
:param training_files: list of files to be used as training data
:type sampling: list of floats
:param sampling: specifies a fraction for each training file, how much
to sample on each epoch
:type validation_iter: theanolm.BatchIterator
:param validation_iter: an iterator for computing validation set
perplexity
:type state: h5py.File
:param state: HDF5 file where initial training state will be possibly
read from, and candidate states will be saved to
:type profile: bool
:param profile: if set to True, creates Theano profile objects
"""
self.network = network
self.vocabulary = vocabulary
self.scorer = scorer
self.validation_iter = validation_iter
self.optimizer = create_optimizer(optimization_options, self.network,
profile)
self.training_iter = ShufflingBatchIterator(
training_files,
sampling,
vocabulary,
batch_size=training_options['batch_size'],
max_sequence_length=training_options['sequence_length'])
print("Computing the number of training updates per epoch.")
sys.stdout.flush()
self.updates_per_epoch = len(self.training_iter)
if self.updates_per_epoch < 1:
raise ValueError("Training data does not contain any sentences.")
self.stopper = create_stopper(training_options, self)
self.options = training_options
# current candidate for the minimum validation cost state
self._candidate_state = state
if 'trainer' in self._candidate_state:
print("Restoring initial network state from {}.".format(
self._candidate_state.filename))
sys.stdout.flush()
self._reset_state()
else:
# index to the cost history that corresponds to the current candidate
# state
self._candidate_index = None
# current training epoch
self.epoch_number = 1
# number of mini-batch updates performed in this epoch
self.update_number = 0
# validation set cost history
self._cost_history = numpy.asarray([], dtype=theano.config.floatX)
# number of mini-batch updates between log messages
self.log_update_interval = 0
# total number of mini-batch updates performed (after restart)
self.total_updates = 0
