Python与机器学习（第2版）（微课版） 课件 8-04-拓展实训：影评数据分析拓展实训说明

项目背景|项目概述|学习目标|任务实施步骤拓展实训影评数据分析应用【数据挖掘应用】课程负责人：陈清华01101111011010111101010000101101010100111101

熟练使用循环神经网络算法进行情感分析；

熟练使用Keras库设计实现循环神经网络；技能学习目标

会使用混淆矩阵对模型进行评估。项目总体要求原始数据结果呈现IMDB是影评数据集，这些数据标识有情感标签（正面/负面）。数据包含50000条电影评论，其中有25000条训练数据及25000条用于测试，有着相同数量的正面与负面评论。LSTM情感分类准确率:0.8422LSTM情感分类混淆矩阵Predicted01Accuracy01093715631238110119一、数据加载max_features=15000 #设置最大特征为15000max_len=300 #单个句子最大长度为300batch_size=64 #每个批次大小#加载数据(x_train,y_train),(x_test,y_test)=imdb.load_data(num_words=max_features)print(len(x_train),'trainobservations')print(len(x_test),'testobservations')二、数据预处理#使得数据长度保持一致x_train_2=sequence.pad_sequences(x_train,maxlen=max_len)x_test_2=sequence.pad_sequences(x_test,maxlen=max_len)print('x_trainshape:',x_train_2.shape)print('x_testshape:',x_test_2.shape)y_train=np.array(y_train)y_test=np.array(y_test)三、模型构建与训练#构建模型model=Sequential()model.add(Embedding(max_features,128,input_length=max_len))model.add(LSTM(64))model.add(Dense(1,activation='sigmoid'))model.compile('adam','binary_crossentropy',metrics=['accuracy'])#训练模型model.fit(x_train_2,y_train,batch_size=batch_size,epochs=4,validation_split=0.2)三、模型构建与训练Trainon20000samples,validateon5000samplesEpoch1/1020000/20000[==============================]-37s2ms/step-loss:0.5066-accuracy:0.7582-val_loss:0.3708-val_accuracy:0.8484...Epoch9/1020000/20000[==============================]-33s2ms/step-loss:0.0608-accuracy:0.9804-val_loss:0.4903-val_accuracy:0.8666Epoch10/1020000/20000[==============================]-33s2ms/step-loss:0.0952-accuracy:0.9656-val_loss:0.4883-val_accuracy:0.8562四、模型评估#模型预测y_train_predclass=model.predict_classes(x_train_2,batch_size=100)y_test_predclass=model.predict_classes(x_test_2,batch_size=100)y_train_predclass.shape=y_train.shapey_test_predclass.shape=y_test.shape#输出模型评估结果print(("\nLSTMBidirectionalSentimentClassification-Testaccuracy:"),(round(accuracy_score(y_test,y_test_predclass),3)))print("\nLSTMBidirectionalSentimentClassificationofTestdata\n",classification_report(y_test,y_test_predclass))print("\nLSTMBidirectionalSentimentClassification-TestConfusionMatrix\n\n",pd.crosstab(y_test,y_test_predclass,rownames=["Actuall"],colnames=["Predicted"]))四、模型评估LSTM情感分类准确率:0.8422LSTM情感分类混淆矩阵Predicted

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1Accuracy