tflearn 基于循环神经网络（LSTM）计算文本情感值

╰半夏微凉° 2022-05-29 08:54 206阅读 0赞

修改自[官方教程][Link 1]

对于中文文本，可以先用[hanziconv][]把繁体转简体，英文转小写，再用[结巴分词][Link 2]把句子转成词序列，根据词汇表转成词ID序列

这个模型我用来分类淘宝的好评、差评，正确率达到了88.7%

# -*- coding: utf-8 -*-
    
    """ tflearn教程，用LSTM循环神经网络分类文本 https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm.py """
    
    import tflearn
    from tflearn.data_utils import to_categorical, pad_sequences
    from tflearn.datasets import imdb
    
    # 词汇表词数
    VOCAB_LEN = 10000
    # 输入最长词数
    SEQUENCE_LEN = 100
    # 词向量特征数
    WORD_FEATURE_DIM = 128
    # 文本特征数
    DOC_FEATURE_DIM = 128
    
    
    # 载入IMDB数据集，下载地址http://www.iro.umontreal.ca/~lisa/deep/data/imdb.pkl
    # 限制词汇表长度VOCAB_LEN个词
    train, test, _ = imdb.load_data(path='imdb.pkl', n_words=VOCAB_LEN,
                                    valid_portion=0.1)
    # X为词ID序列，Y为标签
    train_x, train_y = train
    test_x, test_y = test
    
    # 数据预处理
    # 把输入填充0或截断到长度=SEQUENCE_LEN
    train_x = pad_sequences(train_x, maxlen=SEQUENCE_LEN, value=0.)
    test_x = pad_sequences(test_x, maxlen=SEQUENCE_LEN, value=0.)
    # 把标签转为2维向量
    train_y = to_categorical(train_y, 2)
    test_y = to_categorical(test_y, 2)
    
    # 构造神经网络
    # 输入层，SEQUENCE_LEN个神经元
    net = tflearn.input_data([None, SEQUENCE_LEN])
    # 嵌入层，输入维度为VOCAB_LEN，输出维度为WORD_FEATURE_DIM
    net = tflearn.embedding(net, input_dim=VOCAB_LEN, output_dim=WORD_FEATURE_DIM)
    # LSTM层（循环神经网络），DOC_FEATURE_DIM个神经元
    # 后面跟保持概率为0.8的dropout层，防止过拟合
    net = tflearn.lstm(net, DOC_FEATURE_DIM, dropout=0.8)
    # 全连接层，softmax分类器，分成2个类，输出评论为好评、差评的概率
    net = tflearn.fully_connected(net, 2, activation='softmax')
    net = tflearn.regression(net, optimizer='adam', learning_rate=0.001,
                             loss='categorical_crossentropy')
    model = tflearn.DNN(net, tensorboard_verbose=0)
    
    # 训练10代
    model.fit(train_x, train_y, validation_set=(test_x, test_y), show_metric=True,
              n_epoch=10, batch_size=32)
    # | Adam | epoch: 010 | loss: 0.04662 - acc: 0.9880 | val_loss: 0.91889 - val_acc: 0.7992 -- iter: 22500/22500
    
    # 预测
    x = [
        [17, 25, 10, 406, 26, 14, 56, 61, 62, 323, 4],
        [6691, 1, 10, 333, 10, 17, 27, 4, 34, 181, 6, 1418, 256, 4],
    ]
    x = pad_sequences(x, maxlen=SEQUENCE_LEN, value=0.)
    # 计算评论分类概率
    print(model.predict(x))
    # [[0.9944102 0.00558983]
    # [0.00333996 0.99666 ]]
    # 计算评论分类标签
    print(model.predict_label(x))
    # 返回分类索引数组，按概率降序排列
    # [[0 1]
    # [1 0]]

![训练][Image 1]

[Link 1]: https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm.py
[hanziconv]: https://github.com/berniey/hanziconv
[Link 2]: https://github.com/fxsjy/jieba
[Image 1]: