From 7db0d3df0701c13edb5f3e26dad8838c528a6721 Mon Sep 17 00:00:00 2001 From: jiangzhonglian Date: Wed, 11 Dec 2019 11:43:12 +0800 Subject: [PATCH] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=20attention=20=E6=9C=BA?= =?UTF-8?q?=E5=88=B6=E4=BC=98=E5=8C=96=E7=BB=93=E6=9E=9C?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/py3.x/tensorflow2.x/text_Emotion.py | 15 ++++++++++----- 1 file changed, 10 insertions(+), 5 deletions(-) diff --git a/src/py3.x/tensorflow2.x/text_Emotion.py b/src/py3.x/tensorflow2.x/text_Emotion.py index c44e6f39..a3f6d7bf 100644 --- a/src/py3.x/tensorflow2.x/text_Emotion.py +++ b/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()