From b1b628bb738f7bcb2ac3400efa2cf55c325276a1 Mon Sep 17 00:00:00 2001
From: zy <zy_Fighting21@163.com>
Date: Wed, 27 Nov 2019 23:44:40 +0800
Subject: [PATCH] 'update'

---
 input_data.py | 6 +++---
 model.py      | 2 +-
 word2vec.py   | 2 +-
 3 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/input_data.py b/input_data.py
index bb47ed4..a029ea7 100644
--- a/input_data.py
+++ b/input_data.py
@@ -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()
@@ -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(' '):
@@ -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 = []
diff --git a/model.py b/model.py
index 8f8bcc1..fcde0db 100644
--- a/model.py
+++ b/model.py
@@ -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]
diff --git a/word2vec.py b/word2vec.py
index 6b844d9..968239e 100644
--- a/word2vec.py
+++ b/word2vec.py
@@ -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)