def __init__(self):
vocabulary.load_vocabulary()
self.parameters = Parameters()
self.train_loss = 0
self.train_err = 0
self.train_lossnonzero = 0
self.train_cnt = 0
def __init__(self):
vocabulary.load_vocabulary()
self.parameters = Parameters()
self.train_loss = 0
self.train_err = 0
self.train_lossnonzero = 0
self.train_cnt = 0
self.COMPILE_MODE = theano.compile.Mode('c|py', 'fast_run')
self.train_function = self._get_train_function()
def __init__(self):
vocabulary.load_vocabulary()
self.parameters = Parameters()
self.train_loss = 0
self.train_err = 0
self.train_lossnonzero = 0
self.train_cnt = 0
self.COMPILE_MODE = theano.compile.Mode('c|py', 'fast_run')
self.train_function = self._get_train_function()
def build_samples():
if not vocabulary.length():
vocabulary.load_vocabulary()
sample_file = open(config.SAMPLE_FILE, 'w')
for line in open(config.TRAIN_FILE, 'r'):
line = line.strip('\n')
words = [getNormalWord(word) for word in line.split() if word]
word_ids = [vocabulary.id(word) for word in words]
sent_length = len(word_ids)
half_window = config.WINDOW_SIZE / 2
window = []
padding_id = vocabulary.id(config.PADDING_WORD)
unknown_id = vocabulary.id(config.UNKNOWN_WORD)
symbol_id = vocabulary.id(config.SYMBOL_WORD)
for index, word_id in enumerate(word_ids):
if word_id == unknown_id:
continue
if index - half_window >= 0 and index + half_window < sent_length:
window = word_ids[index - half_window:index + half_window + 1]
if window.count(unknown_id) + window.count(
symbol_id) + window.count(padding_id) <= half_window:
sample_file.write(' '.join([str(id)
for id in window]) + '\n')
window = []
continue
if index - half_window < 0:
for i in range(half_window - index):
window.append(padding_id)
window.extend(word_ids[:index + 1])
else:
window.extend(word_ids[index - half_window:index + 1])
if index + half_window >= sent_length:
window.extend(word_ids[index + 1:])
for i in range(index + half_window - sent_length + 1):
window.append(padding_id)
else:
window.extend(word_ids[index + 1:index + half_window + 1])
if window.count(unknown_id) + window.count(
symbol_id) + window.count(padding_id) <= half_window:
sample_file.write(' '.join([str(id) for id in window]) + '\n')
window = []
sample_file.close()
def build_samples():
if not vocabulary.length():
vocabulary.load_vocabulary()
sample_file = open(config.SAMPLE_FILE, 'w')
for line in open(config.TRAIN_FILE, 'r'):
line = line.strip('\n')
words = [getNormalWord(word) for word in line.split() if word]
word_ids = [vocabulary.id(word) for word in words]
sent_length = len(word_ids)
half_window = config.WINDOW_SIZE / 2
window = []
padding_id = vocabulary.id(config.PADDING_WORD)
unknown_id = vocabulary.id(config.UNKNOWN_WORD)
symbol_id = vocabulary.id(config.SYMBOL_WORD)
for index, word_id in enumerate(word_ids):
if word_id == unknown_id:
continue
if index - half_window >= 0 and index + half_window < sent_length:
window = word_ids[index - half_window : index + half_window + 1]
if window.count(unknown_id) + window.count(symbol_id) + window.count(padding_id) <= half_window:
sample_file.write(' '.join([str(id) for id in window]) + '\n')
window = []
continue
if index - half_window < 0:
for i in range(half_window - index):
window.append(padding_id)
window.extend(word_ids[:index + 1])
else:
window.extend(word_ids[index - half_window : index + 1])
if index + half_window >= sent_length:
window.extend(word_ids[index + 1:])
for i in range(index + half_window - sent_length + 1):
window.append(padding_id)
else:
window.extend(word_ids[index + 1 : index + half_window + 1])
if window.count(unknown_id) + window.count(symbol_id) + window.count(padding_id) <= half_window:
sample_file.write(' '.join([str(id) for id in window]) + '\n')
window = []
sample_file.close()
def initialize_model(cfg):
"""
Initialize a new model using the given config.
:param cfg: the `Config` instance used to create the model
:returns: (model, optimizer, vocab, stats, cfg)
"""
print("Initializing a new model...")
vocab = load_vocabulary(cfg.vocab_file, cfg.vocab_size)
model = Seq2Seq(vocab, cfg)
if torch.cuda.device_count() > 1:
print("%d GPUs available - wrapping in DataParallel" %
torch.cuda.device_count())
model = nn.DataParallel(model)
model.to(DEVICE)
optimizer = initialize_optimizer(model.parameters(), cfg)
stats = defaultdict(int)
stats["model_identifier"] = get_model_identifier()
return model, optimizer, vocab, stats, cfg