1 目标

从中文文本中识别出句子里的情绪。和上一章节单车预测回归问题相比，这个问题是分类问题，不是回归问题

2 神经网络分类器

2.1 如何用神经网络分类

第二章节用torch.nn.Sequantial做的回归预测器，输出神经元只有一个。分类器和其区别如下

1 分类器输出单元有几个，就有几个分类

2 分类器各输出单元输出值加和为1（值表示某个预测分类的概率，概率求和为1）

3 回归预测最后一层可以用sigmoid，分类不行，因为sigmoid无法保证分类器输出单元值求和为1，可以用softmax，表达式如下

2.2 分类问题损失函数

分类问题损失函数一般用交叉熵：

N表示样本数量，Ci表示第i个样本的类别标签，y表示每个样本的损失值

3 词袋模型分类器

直白理解：就是统计句子里各词语出现数量，忽略语法，语义等因素

思想：1 将句子转化为词袋，然后可以onehot处理，方便用神经网络预测；2 大概思想：在词袋里寻找和输出相关关键词语，然后统计词语数量，哪个输出类别的敏感词语统计数量高，结果就是哪个输出类别

数据预处理：可以归一化处理，网络计算会更快

3.1 简单文本分类器

背景与数据：获取网购商城顾客评论信息，从评论信息获取句子情绪

神经网络组成：用前馈神经网络，有3层，分别是输入，中间，输出层，输出层数量有两个，分别代表情绪是正面还是负面

3.2 程序实现

3.2.1 数据处理

主要步骤：处理标点，句子分词，建立词表

标点处理：正则，涉及的标点全部删除

句子分词：使用jieba模块对句子分词

建立词表：python字典建立词表

def deal_punc(sentence):
    sentence = re.sub('[\s+\.\!\/_,$%^*(+\"\'“”《》?]+|[+——！，。？、~@#￥%……&*（）：)]+', '', sentence)
    return sentence


def prepare_data(good_file, bad_file, is_filter=True):
    print('prepare data begin')
    all_words = []
    pos_sen, neg_sen = [], []
    for path, sen in zip((good_file, bad_file), (pos_sen, neg_sen)):
        with open(path, 'r', encoding='utf-8') as f:
            for index, line in enumerate(f):
                if is_filter:
                    line = deal_punc(line)
                
                words = jieba.lcut(line)
                if len(words) > 0:
                    all_words += words
                    sen.append(words)
            print(f'{path} include {index} rows, all words:{len(all_words)}')
    print(f'pos_sen len:{len(pos_sen)}, neg_sen len:{len(neg_sen)}')
            
    diction = {}
    cnt = Counter(all_words)
    for word, freq in cnt.items():
        diction[word] = [len(diction), freq]
    print(f'diction len:{len(diction)}')
    return (pos_sen, neg_sen, diction)

def word2index(word, diction):
    if word in diction:
        value = diction[word][0]
    else:
        value = -1
    return (value)

def index2word(index, diction):
    for w, v in diction.items():
        if v[0] == index:
            return (w)
    return (None)

3.2.2 文本数据向量化

    pos_sen, neg_sen, diction = prepare_data_sample(good_file, bad_file)
    st = sorted([(v[1], w) for w, v in diction.items()])
    datasets, labels, sentences = [], [], []
    '''
    for sens, tag in zip((pos_sen, neg_sen), (0, 1)):
        for sen in sens:
            new_sen = []
            for l in sen:
                if l in diction:
                    new_sen.append(word2index_sample(1, diction))
            datasets.append(sentence2vec_sample(new_sen, diction))
            labels.append(tag)
            sentences.append(sen)
    '''
    for sentence in pos_sen:
        new_sentence = []
        for l in sentence:
            if l in diction:
                new_sentence.append(word2index(l, diction))
        datasets.append(sentence2vec_sample(new_sentence, diction))
        labels.append(0) #正标签为0
        sentences.append(sentence)

    # 处理负向评论
    for sentence in neg_sen:
        new_sentence = []
        for l in sentence:
            if l in diction:
                new_sentence.append(word2index(l, diction))
        datasets.append(sentence2vec_sample(new_sentence, diction))
        labels.append(1) #负标签为1
        sentences.append(sentence)
    indices = np.random.permutation(len(datasets))
    datasets = [datasets[i] for i in indices]
    labels = [labels[i] for i in indices]
    sentences = [sentences[i] for i in indices]

3.3.3 划分数据集

训练集，校验集，测试集，比例一般为10：1：1

    # split train, test, verify datasets
    test_size = int(len(datasets) // 10)
    train_data = datasets[2 * test_size :]
    train_label = labels[2 * test_size : ]
    
    valid_data = datasets[: test_size]
    valid_label = labels[: test_size]
    
    test_data = datasets[test_size : 2 * test_size]
    test_label = labels[test_size : 2 * test_size]

3.3.4 建立神经网络

结构：输入层（约7000个样本），一个中间层（10个隐单元），输出层（2个单元）

注意，此处用的是relu，不是sigmoid。原因：虽然x>0时没处理数据，但x<0时为0让relu有了非线性的能力，且运算比sigmoid简单，速度更快，方便反向误差传导

def plot(records):
    print('plot begin')
    a = [i[0] for i in records]
    b = [i[1] for i in records]
    c = [i[2] for i in records]
    pyplot.plot(a, label='train loss')
    pyplot.plot(b, label='verify loss')
    pyplot.plot(c, label='valid accuracy')
    pyplot.xlabel('step')
    pyplot.ylabel('losses & accuracy')
    pyplot.legend()
    pyplot.show()


def main():
    model = torch.nn.Sequential(
        torch.nn.Linear(len(diction), 10),
        torch.nn.ReLU(),
        torch.nn.Linear(10, 2),
        torch.nn.LogSoftmax(dim=1),
    )
    cost = torch.nn.NLLLoss()
    optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
    records = []
    losses = []
    for epoch in range(3):
        for i, data in enumerate(zip(train_data, train_label)):
            x, y = data
            x = torch.tensor(x, requires_grad=True, dtype=torch.float).view(1, -1)
            y = torch.tensor(np.array([y]), dtype=torch.long)
            optimizer.zero_grad()
            predict = model(x)
            loss = cost(predict, y)
            losses.append(loss.data.numpy())
            loss.backward()
            optimizer.step()

        val_losses = []
        rights = []
        for j, val in enumerate(zip(valid_data, valid_label)):
            x, y = val
            x = torch.tensor(x, requires_grad=True, dtype=torch.float).view(1, -1)
            y = torch.tensor(np.array([y]), dtype = torch.long)
            predict = model(x)
            right = rightness_sample(predict, y)
            rights.append(right)
            loss = cost(predict, y)
            val_losses.append(loss.data.numpy())
        right_ratio = 1.0 * np.sum([i[0] for i in rights]) / np.sum([i[1] for i in rights])
        print(f'No.{epoch}, train loss:{np.mean(losses):.4f}, verify loss:{np.mean(val_losses):.4f}, verify accuracy:{right_ratio}')
        records.append([np.mean(losses), np.mean(val_losses), right_ratio])
    # plot
    plot(records)

3.3 运行结果