TextCNN文本分类（keras实现）

 from sklearn.model_selection import train_test_split

    
 import pandas as pd
    
 import jieba
    
 from keras.preprocessing.text import Tokenizer
    
 from keras.preprocessing.sequence import pad_sequences
    
  
    
 if __name__=='__main__':
    
     dataset = pd.read_csv('sentiment_analysis/data_train.csv', sep='\t',names=['ID', 'type', 'review', 'label']).astype(str)
    
     cw = lambda x: list(jieba.cut(x))
    
     dataset['words'] = dataset['review'].apply(cw)
    
     tokenizer=Tokenizer()  #创建一个Tokenizer对象
    
     #fit_on_texts函数可以将输入的文本中的每个词编号，编号是根据词频的，词频越大，编号越小
    
     tokenizer.fit_on_texts(dataset['words'])
    
     vocab=tokenizer.word_index #得到每个词的编号
    
     x_train, x_test, y_train, y_test = train_test_split(dataset['words'], dataset['label'], test_size=0.1)
    
     # 将每个样本中的每个词转换为数字列表，使用每个词的编号进行编号
    
     x_train_word_ids=tokenizer.texts_to_sequences(x_train)
    
     x_test_word_ids = tokenizer.texts_to_sequences(x_test)
    
     #序列模式
    
     # 每条样本长度不唯一，将每条样本的长度设置一个固定值
    
     x_train_padded_seqs=pad_sequences(x_train_word_ids,maxlen=50) #将超过固定值的部分截掉，不足的在最前面用0填充
    
     x_test_padded_seqs=pad_sequences(x_test_word_ids, maxlen=50)
    
    
    
    
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 #构建CNN分类模型(LeNet-5)

    
 #模型结构：嵌入-卷积池化*2-dropout-BN-全连接-dropout-全连接
    
 def CNN_model(x_train_padded_seqs, y_train, x_test_padded_seqs, y_test):
    
     model = Sequential()
    
     model.add(Embedding(len(vocab) + 1, 300, input_length=50)) #使用Embeeding层将每个词编码转换为词向量
    
     model.add(Conv1D(256, 5, padding='same'))
    
     model.add(MaxPooling1D(3, 3, padding='same'))
    
     model.add(Conv1D(128, 5, padding='same'))
    
     model.add(MaxPooling1D(3, 3, padding='same'))
    
     model.add(Conv1D(64, 3, padding='same'))
    
     model.add(Flatten())
    
     model.add(Dropout(0.1))
    
     model.add(BatchNormalization())  # (批)规范化层
    
     model.add(Dense(256, activation='relu'))
    
     model.add(Dropout(0.1))
    
     model.add(Dense(3, activation='softmax'))
    
     model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
    
     one_hot_labels = keras.utils.to_categorical(y_train, num_classes=3)  # 将标签转换为one-hot编码
    
     model.fit(x_train_padded_seqs, one_hot_labels,epochs=5, batch_size=800)
    
     y_predict = model.predict_classes(x_test_padded_seqs)  # 预测的是类别，结果就是类别号
    
     y_predict = list(map(str, y_predict))
    
     print('准确率', metrics.accuracy_score(y_test, y_predict))
    
     print('平均f1-score:', metrics.f1_score(y_test, y_predict, average='weighted'))
    
    
    
    
    代码解读

 #构建TextCNN模型

    
 #模型结构：词嵌入-卷积池化*3-拼接-全连接-dropout-全连接
    
 def TextCNN_model_1(x_train_padded_seqs,y_train,x_test_padded_seqs,y_test):
    
     main_input = Input(shape=(50,), dtype='float64')
    
     # 词嵌入（使用预训练的词向量）
    
     embedder = Embedding(len(vocab) + 1, 300, input_length=50, trainable=False)
    
     embed = embedder(main_input)
    
     # 词窗大小分别为3,4,5
    
     cnn1 = Conv1D(256, 3, padding='same', strides=1, activation='relu')(embed)
    
     cnn1 = MaxPooling1D(pool_size=48)(cnn1)
    
     cnn2 = Conv1D(256, 4, padding='same', strides=1, activation='relu')(embed)
    
     cnn2 = MaxPooling1D(pool_size=47)(cnn2)
    
     cnn3 = Conv1D(256, 5, padding='same', strides=1, activation='relu')(embed)
    
     cnn3 = MaxPooling1D(pool_size=46)(cnn3)
    
     # 合并三个模型的输出向量
    
     cnn = concatenate([cnn1, cnn2, cnn3], axis=-1)
    
     flat = Flatten()(cnn)
    
     drop = Dropout(0.2)(flat)
    
     main_output = Dense(3, activation='softmax')(drop)
    
     model = Model(inputs=main_input, outputs=main_output)
    
     model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    
  
    
     one_hot_labels = keras.utils.to_categorical(y_train, num_classes=3)  # 将标签转换为one-hot编码
    
     model.fit(x_train_padded_seqs, one_hot_labels, batch_size=800, epochs=10)
    
     #y_test_onehot = keras.utils.to_categorical(y_test, num_classes=3)  # 将标签转换为one-hot编码
    
     result = model.predict(x_test_padded_seqs)  # 预测样本属于每个类别的概率
    
     result_labels = np.argmax(result, axis=1)  # 获得最大概率对应的标签
    
     y_predict = list(map(str, result_labels))
    
     print('准确率', metrics.accuracy_score(y_test, y_predict))
    
     print('平均f1-score:', metrics.f1_score(y_test, y_predict, average='weighted'))
    
    
    
    
    代码解读

 w2v_model=Word2Vec.load('sentiment_analysis/w2v_model.pkl')

    
 # 预训练的词向量中没有出现的词用0向量表示
    
 embedding_matrix = np.zeros((len(vocab) + 1, 300))
    
 for word, i in vocab.items():
    
     try:
    
     embedding_vector = w2v_model[str(word)]
    
     embedding_matrix[i] = embedding_vector
    
     except KeyError:
    
     continue
    
  
    
 #构建TextCNN模型
    
 def TextCNN_model_2(x_train_padded_seqs,y_train,x_test_padded_seqs,y_test,embedding_matrix):
    
     # 模型结构：词嵌入-卷积池化*3-拼接-全连接-dropout-全连接
    
     main_input = Input(shape=(50,), dtype='float64')
    
     # 词嵌入（使用预训练的词向量）
    
     embedder = Embedding(len(vocab) + 1, 300, input_length=50, weights=[embedding_matrix], trainable=False)
    
     #embedder = Embedding(len(vocab) + 1, 300, input_length=50, trainable=False)
    
     embed = embedder(main_input)
    
     # 词窗大小分别为3,4,5
    
     cnn1 = Conv1D(256, 3, padding='same', strides=1, activation='relu')(embed)
    
     cnn1 = MaxPooling1D(pool_size=38)(cnn1)
    
     cnn2 = Conv1D(256, 4, padding='same', strides=1, activation='relu')(embed)
    
     cnn2 = MaxPooling1D(pool_size=37)(cnn2)
    
     cnn3 = Conv1D(256, 5, padding='same', strides=1, activation='relu')(embed)
    
     cnn3 = MaxPooling1D(pool_size=36)(cnn3)
    
     # 合并三个模型的输出向量
    
     cnn = concatenate([cnn1, cnn2, cnn3], axis=-1)
    
     flat = Flatten()(cnn)
    
     drop = Dropout(0.2)(flat)
    
     main_output = Dense(3, activation='softmax')(drop)
    
     model = Model(inputs=main_input, outputs=main_output)
    
     model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    
  
    
     one_hot_labels = keras.utils.to_categorical(y_train, num_classes=3)  # 将标签转换为one-hot编码
    
     model.fit(x_train_padded_seqs, one_hot_labels, batch_size=800, epochs=20)
    
     #y_test_onehot = keras.utils.to_categorical(y_test, num_classes=3)  # 将标签转换为one-hot编码
    
     result = model.predict(x_test_padded_seqs)  # 预测样本属于每个类别的概率
    
     result_labels = np.argmax(result, axis=1)  # 获得最大概率对应的标签
    
     y_predict = list(map(str, result_labels))
    
     print('准确率', metrics.accuracy_score(y_test, y_predict))
    
     print('平均f1-score:', metrics.f1_score(y_test, y_predict, average='weighted'))
    
    
    
    
    代码解读

 import os

    
 os.environ["PATH"] += os.pathsep + 'C:/Program Files (x86)/Graphviz2.38/bin/'
    
    
    
    
    代码解读

 from keras.utils import plot_model

    
     #生成一个模型图，第一个参数为模型，第二个参数为要生成图片的路径及文件名，还可以指定两个参数：
    
     #show_shapes:指定是否显示输出数据的形状，默认为False
    
     #show_layer_names:指定是否显示层名称，默认为True
    
     plot_model(model,to_file='sentiment_analysis/model.png',show_shapes=True,show_layer_names=False)
    
    
    
    
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 from keras.models import load_model

    
  
    
 #模型的保存
    
 model.save('model.h5')
    
  
    
 #模型的加载
    
 model=load_model('model.h5')
    
    
    
    
    代码解读