还原删除代码

src/py3.x/tensorflow2.x/text_bert.py

@@ -93,62 +93,62 @@ def seq_padding(X, padding=0):
 ## bert / Embedding/  + lstm + crt
 
 
- #%%
- # 加载数据
- class TextBert():
-     def __init__(self):
-         self.path_config = Config.bert.path_config
-         self.path_checkpoint = Config.bert.path_checkpoint
-
-         self.token_dict = {}
-         with codecs.open(Config.bert.dict_path, 'r', 'utf8') as reader:
-             for line in reader:
-                 token = line.strip()
-                 self.token_dict[token] = len(self.token_dict)
-
-
-     def prepare_data(self):
-         neg = pd.read_excel(Config.bert.path_neg, header=None)
-         pos = pd.read_excel(Config.bert.path_pos, header=None)
-         data = []
-         for d in neg[0]:
-             data.append((d, 0))
-         for d in pos[0]:
-             data.append((d, 1))
-         # 按照9:1的比例划分训练集和验证集
-         random_order = list(range(len(data)))
-         np.random.shuffle(random_order)
-         train_data = [data[j] for i, j in enumerate(random_order) if i % 10 != 0]
-         valid_data = [data[j] for i, j in enumerate(random_order) if i % 10 == 0]
-         return train_data, valid_data
-
-     def build_model(self, m_type="bert"):
-         if m_type == "bert":
-             bert_model = load_trained_model_from_checkpoint(self.path_config, self.path_checkpoint, seq_len=None)
-             for l in bert_model.layers:
-                 l.trainable = True
-             x1_in = Input(shape=(None,))
-             x2_in = Input(shape=(None,))
-             x = bert_model([x1_in, x2_in])
-             x = Lambda(lambda x: x[:, 0])(x)
-             p = Dense(1, activation='sigmoid')(x)#根据分类种类自行调节，也可以多加一些层数
-             model = Model([x1_in, x2_in], p)
-             model.compile(
-                 loss='binary_crossentropy',
-                 optimizer=Adam(1e-5), # 用足够小的学习率
-                 metrics=['accuracy']
-             )
-         else:
-             # 否则用 Embedding
-             model = Sequential()
-             model.add(Embedding(len(vocab), EMBED_DIM, mask_zero=True))  # Random embedding
-             model.add(Bidirectional(LSTM(BiRNN_UNITS // 2, return_sequences=True)))
-             crf = CRF(len(chunk_tags), sparse_target=True)
-             model.add(crf)
-             model.compile('adam', loss=crf.loss_function, metrics=[crf.accuracy])
-
-         model.summary()
-         return model
+#%%
+# 加载数据
+class TextBert():
+    def __init__(self):
+        self.path_config = Config.bert.path_config
+        self.path_checkpoint = Config.bert.path_checkpoint
+
+        self.token_dict = {}
+        with codecs.open(Config.bert.dict_path, 'r', 'utf8') as reader:
+            for line in reader:
+                token = line.strip()
+                self.token_dict[token] = len(self.token_dict)
+
+
+    def prepare_data(self):
+        neg = pd.read_excel(Config.bert.path_neg, header=None)
+        pos = pd.read_excel(Config.bert.path_pos, header=None)
+        data = []
+        for d in neg[0]:
+            data.append((d, 0))
+        for d in pos[0]:
+            data.append((d, 1))
+        # 按照9:1的比例划分训练集和验证集
+        random_order = list(range(len(data)))
+        np.random.shuffle(random_order)
+        train_data = [data[j] for i, j in enumerate(random_order) if i % 10 != 0]
+        valid_data = [data[j] for i, j in enumerate(random_order) if i % 10 == 0]
+        return train_data, valid_data
+
+    def build_model(self, m_type="bert"):
+        if m_type == "bert":
+            bert_model = load_trained_model_from_checkpoint(self.path_config, self.path_checkpoint, seq_len=None)
+            for l in bert_model.layers:
+                l.trainable = True
+            x1_in = Input(shape=(None,))
+            x2_in = Input(shape=(None,))
+            x = bert_model([x1_in, x2_in])
+            x = Lambda(lambda x: x[:, 0])(x)
+            p = Dense(1, activation='sigmoid')(x)#根据分类种类自行调节，也可以多加一些层数
+            model = Model([x1_in, x2_in], p)
+            model.compile(
+                loss='binary_crossentropy',
+                optimizer=Adam(1e-5), # 用足够小的学习率
+                metrics=['accuracy']
+            )
+        else:
+            # 否则用 Embedding
+            model = Sequential()
+            model.add(Embedding(len(vocab), EMBED_DIM, mask_zero=True))  # Random embedding
+            model.add(Bidirectional(LSTM(BiRNN_UNITS // 2, return_sequences=True)))
+            crf = CRF(len(chunk_tags), sparse_target=True)
+            model.add(crf)
+            model.compile('adam', loss=crf.loss_function, metrics=[crf.accuracy])
+        
+        model.summary()
+        return model
 
 
 #%%
@@ -348,4 +348,4 @@ def _generator():
 print('Fill with: ', list(map(lambda x: token_dict_rev[x], predicts[0][1:3])))
 # Fill with:  ['数', '学']
 
-# %%
+# %%