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modify the way opening the file and update the version of pytorch to 1.1 #9

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modify the way opening the file and update the version of pytorch to 1.1 #9

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6 changes: 3 additions & 3 deletions input_data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,7 +23,7 @@ def __init__(self, file_name, min_count):
print('Sentence Length: %d' % (self.sentence_length))

def get_words(self, min_count):
self.input_file = open(self.input_file_name)
self.input_file = open(self.input_file_name, 'r', encoding='utf-8', errors='ignore')
self.sentence_length = 0
self.sentence_count = 0
word_frequency = dict()
Expand Down Expand Up @@ -66,7 +66,7 @@ def get_batch_pairs(self, batch_size, window_size):
while len(self.word_pair_catch) < batch_size:
sentence = self.input_file.readline()
if sentence is None or sentence == '':
self.input_file = open(self.input_file_name)
self.input_file = open(self.input_file_name, 'r', encoding='utf-8', errors='ignore')
sentence = self.input_file.readline()
word_ids = []
for word in sentence.strip().split(' '):
Expand All @@ -79,7 +79,7 @@ def get_batch_pairs(self, batch_size, window_size):
word_ids[max(i - window_size, 0):i + window_size]):
assert u < self.word_count
assert v < self.word_count
if i == j:
if u == v:
continue
self.word_pair_catch.append((u, v))
batch_pairs = []
Expand Down
2 changes: 1 addition & 1 deletion model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -85,7 +85,7 @@ def save_embedding(self, id2word, file_name, use_cuda):
embedding = self.u_embeddings.weight.cpu().data.numpy()
else:
embedding = self.u_embeddings.weight.data.numpy()
fout = open(file_name, 'w')
fout = open(file_name, 'w',encoding='utf-8',errors='ignore')
fout.write('%d %d\n' % (len(id2word), self.emb_dimension))
for wid, w in id2word.items():
e = embedding[wid]
Expand Down
2 changes: 1 addition & 1 deletion word2vec.py
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Expand Up @@ -81,7 +81,7 @@ def train(self):
self.optimizer.step()

process_bar.set_description("Loss: %0.8f, lr: %0.6f" %
(loss.data[0],
(loss,
self.optimizer.param_groups[0]['lr']))
if i * self.batch_size % 100000 == 0:
lr = self.initial_lr * (1.0 - 1.0 * i / batch_count)
Expand Down