def __init__(self, text, batch_size, num_unrollings=1, vocabulary=None, random_batch_initiation=False):
self._text = text
self._text_size = len(text)
self._batch_size = batch_size
self.vocabulary = vocabulary
self._vocabulary_size = len(self.vocabulary)
self.character_positions_in_vocabulary = get_positions_in_vocabulary(self.vocabulary)
self._num_unrollings = num_unrollings
if random_batch_initiation:
self._cursor = random.sample(range(self._text_size), batch_size)
else:
segment = self._text_size // batch_size
self._cursor = [offset * segment for offset in range(batch_size)]
self._last_batch = self._start_batch()
# valid_text = text[:valid_size]
# train_text = text[valid_size:]
voc_name = 'razvedopros_voc.txt'
if os.path.isfile(voc_name):
with open(voc_name, 'r') as f:
vocabulary = list(f.read())
else:
vocabulary = create_vocabulary(text)
with open(voc_name, 'w') as f:
f.write(''.join(vocabulary))
vocabulary_size = len(vocabulary)
env = Environment(Lstm, BatchGenerator, vocabulary=vocabulary)
cpiv = get_positions_in_vocabulary(vocabulary)
add_feed = [{'placeholder': 'dropout', 'value': 0.9} #,
# {'placeholder': 'sampling_prob',
# 'value': {'type': 'linear', 'start': 0., 'end': 1., 'interval': 3000}},
# {'placeholder': 'loss_comp_prob',
# 'value': {'type': 'linear', 'start': 1., 'end': 0., 'interval': 3000}}
]
valid_add_feed = [# {'placeholder': 'sampling_prob', 'value': 1.},
{'placeholder': 'dropout', 'value': 1.}]
add_metrics = ['bpc', 'perplexity', 'accuracy']
# tf.set_random_seed(1)
NUM_UNROLLINGS = 30
BATCH_SIZE = 32