添加 attention 机制优化结果

src/py3.x/tensorflow2.x/text_Emotion.py

@@ -5,6 +5,8 @@
 #   https://blog.csdn.net/alip39/article/details/95891321
 # 参考代码:
 #   https://blog.csdn.net/u012052268/article/details/90238282
+# Attention:
+#   https://github.com/philipperemy/keras-attention-mechanism
 import re
 import os
 import keras
@@ -16,7 +18,8 @@ import jieba
 from sklearn.model_selection import train_test_split
 from keras import Model
 from keras.models import load_model
-from keras.layers import Dropout, Dense, Flatten, Bidirectional, Embedding, GRU, Input
+from keras.layers.normalization import BatchNormalization
+from keras.layers import Dropout, Dense, Flatten, Bidirectional, Embedding, GRU, Input, multiply
 from keras.preprocessing.sequence import pad_sequences
 from keras.utils.np_utils import to_categorical
 from keras.optimizers import Adam
@@ -117,12 +120,15 @@ class EmotionModel(object):
         # ) #设置句子的最大长度
 
         print("开始训练模型.....")
-        # 使用
         sequence_input = Input(shape=(self.MAX_SEQUENCE_LENGTH,), dtype='int32')  # 返回一个张量，长度为1000，也就是模型的输入为batch_size*1000
         embedded_sequences = embedding_layer(sequence_input)  # 返回batch_size*1000*100
-        x = Bidirectional(GRU(100, return_sequences=True))(embedded_sequences)
-        x = Dropout(0.6)(x)
+        # 添加 注意力(本质上是通过加入  一个随机向量 作为 权重 来优化 输入的值 - 与全链接不同的是，这个还会作为输入项 和 输入做点乘 )
+        attention_probs = Dense(self.EMBEDDING_DIM, activation='softmax', name='attention_probs')(embedded_sequences)
+        attention_mul = multiply([embedded_sequences, attention_probs], name='attention_mul')
+        x = Bidirectional(GRU(self.EMBEDDING_DIM, return_sequences=True, dropout=0.5))(attention_mul)
+        x = Dropout(0.5)(x)
         x = Flatten()(x)
+        # x = BatchNormalization()(x)
         preds = Dense(self.pre_num, activation='softmax')(x)
         self.model = Model(sequence_input, preds)
         # 设置优化器
@@ -176,7 +182,6 @@ class EmotionModel(object):
         x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=test_size, random_state=10000)
         return (x_train, y_train), (x_test, y_test) 
 
-
     def train(self):
         '''训练模型'''
         vocab_list, word_index, embeddings_matrix = load_embeding()