文本分类(二) | (4) 模型及其配置的定义

完整项目

本篇博客，主要介绍各个模型的模块定义，包括模型本身的定义以及模型对应的配置(超参数)的定义，二者在一个模块文件中。

1. FastText

1. FastText

配置类

class Config(object):

    """FastText配置参数"""
    def __init__(self, dataset, embedding):
        self.model_name = 'FastText'
        #训练集、验证集、测试集路径
        self.train_path = dataset + '/data/train.txt'
        self.dev_path = dataset + '/data/dev.txt'
        self.test_path = dataset + '/data/test.txt'
        #数据集的所有类别
        self.class_list = [x.strip() for x in open(
            dataset + '/data/class.txt').readlines()]
        #构建好的词/字典路径
        self.vocab_path = dataset + '/data/vocab.pkl'
        #训练好的模型参数保存路径
        self.save_path = dataset + '/saved_dict/' + self.model_name + '.ckpt'
        #模型日志保存路径
        self.log_path = dataset + '/log/' + self.model_name
        #如果词/字嵌入矩阵不随机初始化 则加载初始化好的词/字嵌入矩阵 类别为float32 并转换为tensor 否则为None
        self.embedding_pretrained = torch.tensor(
            np.load(dataset + '/data/' + embedding)["embeddings"].astype('float32'))\
            if embedding != 'random' else None
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')   # 设备

        self.dropout = 0.5                                              # 随机失活 丢弃率
        self.require_improvement = 1000                                 # 若超过1000batch效果还没提升，则提前结束训练
        self.num_classes = len(self.class_list)                         # 类别数
        self.n_vocab = 0                                                # 词表大小，在运行时赋值
        self.num_epochs = 20                                            # epoch数
        self.batch_size = 128                                           # mini-batch大小
        self.pad_size = 32                                              # 每句话处理成的长度(短填长切)
        self.learning_rate = 1e-3                                       # 学习率
        self.embed = self.embedding_pretrained.size(1)\
            if self.embedding_pretrained is not None else 300           # 字向量维度
        self.hidden_size = 256                                          # 隐藏层大小
        self.n_gram_vocab = 250499        #n-gram词表大小

模型定义类

class Model(nn.Module):
    def __init__(self, config):
        super(Model, self).__init__()

        if config.embedding_pretrained is not None: #加载初始化好的预训练词/字嵌入矩阵  微调funetuning
            self.embedding = nn.Embedding.from_pretrained(config.embedding_pretrained, freeze=False)
        else: #否则随机初始化词/字嵌入矩阵 指定填充对应的索引
            self.embedding = nn.Embedding(config.n_vocab, config.embed, padding_idx=config.n_vocab - 1)
        
        #分别随机初始化 bi-gram tri-gram对应的词嵌入矩阵 
        self.embedding_ngram2 = nn.Embedding(config.n_gram_vocab, config.embed)
        self.embedding_ngram3 = nn.Embedding(config.n_gram_vocab, config.embed)
        #dropout
        self.dropout = nn.Dropout(config.dropout)
        #隐层
        self.fc1 = nn.Linear(config.embed * 3, config.hidden_size)
        # self.dropout2 = nn.Dropout(config.dropout)
        #输出层
        self.fc2 = nn.Linear(config.hidden_size, config.num_classes)

    def forward(self, x):
        #x (uni-gram,seq_len,bi-gram,tri-gram)
        #基于uni-gram、bi-gram、tri-gram对应的索引 在各自的词嵌入矩阵中查询 得到词嵌入
        #（batch,seq_len,embed）
        out_word = self.embedding(x[0])  
        out_bigram = self.embedding_ngram2(x[2])
        out_trigram = self.embedding_ngram3(x[3])
        #三种嵌入进行拼接 (batch,seq,embed*3)
        out = torch.cat((out_word, out_bigram, out_trigram), -1)
        
        #沿长度维 作平均 (batch,embed*3)
        out = out.mean(dim=1)
        #通过fropout
        out = self.dropout(out)
        #通过隐层 (batch,hidden_size)
        out = self.fc1(out)
        out = F.relu(out)
        #输出层 (batch,classes)
        out = self.fc2(out)
        return out

2. TextCNN

配置类

class Config(object):

    """TextCNN配置参数"""
    def __init__(self, dataset, embedding):
        
        self.model_name = 'TextCNN'
        # 训练集、验证集、测试集路径
        self.train_path = dataset + '/data/train.txt'
        self.dev_path = dataset + '/data/dev.txt'
        self.test_path = dataset + '/data/test.txt'
        # 数据集的所有类别
        self.class_list = [x.strip() for x in open(
            dataset + '/data/class.txt').readlines()]
        # 构建好的词/字典路径
        self.vocab_path = dataset + '/data/vocab.pkl'
        # 训练好的模型参数保存路径
        self.save_path = dataset + '/saved_dict/' + self.model_name + '.ckpt'
        # 模型日志保存路径
        self.log_path = dataset + '/log/' + self.model_name
        # 如果词/字嵌入矩阵不随机初始化 则加载初始化好的词/字嵌入矩阵 类别为float32 并转换为tensor 否则为None
        self.embedding_pretrained = torch.tensor(
            np.load(dataset + '/data/' + embedding)["embeddings"].astype('float32')) \
            if embedding != 'random' else None
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')  # 设备

        self.dropout = 0.5                                              # 随机失活
        self.require_improvement = 1000                                 # 若超过1000batch效果还没提升，则提前结束训练
        self.num_classes = len(self.class_list)                         # 类别数
        self.n_vocab = 0                                                # 词表大小，在运行时赋值
        self.num_epochs = 20                                            # epoch数
        self.batch_size = 128                                           # mini-batch大小
        self.pad_size = 32                                              # 每句话处理成的长度(短填长切)
        self.learning_rate = 1e-3                                       # 学习率
        self.embed = self.embedding_pretrained.size(1)\
            if self.embedding_pretrained is not None else 300           # 字向量维度
        self.filter_sizes = (2, 3, 4)                                   # 不同大小卷积核尺寸
        self.num_filters = 256                                          # 每种卷积核数量

模型定义类

class Model(nn.Module):
    def __init__(self, config):
        super(Model, self).__init__()

        if config.embedding_pretrained is not None:  # 加载初始化好的预训练词/字嵌入矩阵  微调funetuning
            self.embedding = nn.Embedding.from_pretrained(config.embedding_pretrained, freeze=False)
        else:  # 否则随机初始化词/字嵌入矩阵 指定填充对应的索引
            self.embedding = nn.Embedding(config.n_vocab, config.embed, padding_idx=config.n_vocab - 1)
        
        #不同大小卷积核对应的卷积操作
        self.convs = nn.ModuleList(
            [nn.Conv2d(1, config.num_filters, (k, config.embed)) for k in config.filter_sizes])
        
        self.dropout = nn.Dropout(config.dropout)
        self.fc = nn.Linear(config.num_filters * len(config.filter_sizes), config.num_classes)

    def conv_and_pool(self, x, conv):
        x = F.relu(conv(x)).squeeze(3) #（batch,num_filters,height）
        x = F.max_pool1d(x, x.size(2)).squeeze(2) #(batch,num_filters) 全局最大池化
        return x

    def forward(self, x):
        out = self.embedding(x[0]) #（batch,seq） -> (batch,seq,embed)
        out = out.unsqueeze(1) #添加通道维 (batch,1,seq,embed)
        #通过不同大小的卷积核提取特征 并对池化结果进行拼接  (batch,num_filters*len(filter_size))
        out = torch.cat([self.conv_and_pool(out, conv) for conv in self.convs], 1)
        out = self.dropout(out)
        out = self.fc(out) #(batch,classes)
        return out

3. TextRNN

配置类

class Config(object):

    """配置参数"""
    def __init__(self, dataset, embedding):
        self.model_name = 'TextRNN'
        # 训练集、验证集、测试集路径
        self.train_path = dataset + '/data/train.txt'
        self.dev_path = dataset + '/data/dev.txt'
        self.test_path = dataset + '/data/test.txt'
        # 数据集的所有类别
        self.class_list = [x.strip() for x in open(
            dataset + '/data/class.txt').readlines()]
        # 构建好的词/字典路径
        self.vocab_path = dataset + '/data/vocab.pkl'
        # 训练好的模型参数保存路径
        self.save_path = dataset + '/saved_dict/' + self.model_name + '.ckpt'
        # 模型日志保存路径
        self.log_path = dataset + '/log/' + self.model_name
        # 如果词/字嵌入矩阵不随机初始化 则加载初始化好的词/字嵌入矩阵 类别为float32 并转换为tensor 否则为None
        self.embedding_pretrained = torch.tensor(
            np.load(dataset + '/data/' + embedding)["embeddings"].astype('float32')) \
            if embedding != 'random' else None
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')  # 设备

        self.dropout = 0.5                                              # 随机失活
        self.require_improvement = 1000                                 # 若超过1000batch效果还没提升，则提前结束训练
        self.num_classes = len(self.class_list)                         # 类别数
        self.n_vocab = 0                                                # 词表大小，在运行时赋值
        self.num_epochs = 10                                            # epoch数
        self.batch_size = 128                                           # mini-batch大小
        self.pad_size = 32                                              # 每句话处理成的长度(短填长切)
        self.learning_rate = 1e-3                                       # 学习率
        self.embed = self.embedding_pretrained.size(1)\
            if self.embedding_pretrained is not None else 300           # 字向量维度, 若使用了预训练词向量，则维度统一
        self.hidden_size = 128                                          # lstm隐藏单元数
        self.num_layers = 2                                             # lstm层数

模型定义类

class Model(nn.Module):
    def __init__(self, config):
        super(Model, self).__init__()
        
        if config.embedding_pretrained is not None:  # 加载初始化好的预训练词/字嵌入矩阵  微调funetuning
            self.embedding = nn.Embedding.from_pretrained(config.embedding_pretrained, freeze=False)
        else:  # 否则随机初始化词/字嵌入矩阵 指定填充对应的索引
            self.embedding = nn.Embedding(config.n_vocab, config.embed, padding_idx=config.n_vocab - 1)

        #2层双向lstm batch_size为第一维度
        self.lstm = nn.LSTM(config.embed, config.hidden_size, config.num_layers,
                            bidirectional=True, batch_first=True, dropout=config.dropout)
        #输出层
        self.fc = nn.Linear(config.hidden_size * 2, config.num_classes)

    def forward(self, x):
        x, _ = x     #（batch,SEQ_LEN）
        out = self.embedding(x)  # [batch_size, seq_len, embeding]=[128, 32, 300]
        out, _ = self.lstm(out) #(batch_size,seq_len,hidden_size*2)
        out = self.fc(out[:, -1, :])  # 句子最后时刻的 hidden state (batch,hidden_size*2)->(batch,classes)
        return out

    '''变长RNN，效果差不多，甚至还低了点...'''
    # def forward(self, x):
    #     x, seq_len = x
    #     out = self.embedding(x)
    #     _, idx_sort = torch.sort(seq_len, dim=0, descending=True)  # 长度从长到短排序（index）
    #     _, idx_unsort = torch.sort(idx_sort)  # 排序后，原序列的 index
    #     out = torch.index_select(out, 0, idx_sort)
    #     seq_len = list(seq_len[idx_sort])
    #     out = nn.utils.rnn.pack_padded_sequence(out, seq_len, batch_first=True)
    #     # [batche_size, seq_len, num_directions * hidden_size]
    #     out, (hn, _) = self.lstm(out)
    #     out = torch.cat((hn[2], hn[3]), -1)
    #     # out, _ = nn.utils.rnn.pad_packed_sequence(out, batch_first=True)
    #     out = out.index_select(0, idx_unsort)
    #     out = self.fc(out)
    #     return out

4. TextRCNN

class Config(object):

    """配置参数"""
    def __init__(self, dataset, embedding):
        self.model_name = 'TextRCNN'
        # 训练集、验证集、测试集路径
        self.train_path = dataset + '/data/train.txt'
        self.dev_path = dataset + '/data/dev.txt'
        self.test_path = dataset + '/data/test.txt'
        # 数据集的所有类别
        self.class_list = [x.strip() for x in open(
            dataset + '/data/class.txt').readlines()]
        # 构建好的词/字典路径
        self.vocab_path = dataset + '/data/vocab.pkl'
        # 训练好的模型参数保存路径
        self.save_path = dataset + '/saved_dict/' + self.model_name + '.ckpt'
        # 模型日志保存路径
        self.log_path = dataset + '/log/' + self.model_name
        # 如果词/字嵌入矩阵不随机初始化 则加载初始化好的词/字嵌入矩阵 类别为float32 并转换为tensor 否则为None
        self.embedding_pretrained = torch.tensor(
            np.load(dataset + '/data/' + embedding)["embeddings"].astype('float32')) \
            if embedding != 'random' else None
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')  # 设备

        self.dropout = 1.0                                              # 随机失活
        self.require_improvement = 1000                                 # 若超过1000batch效果还没提升，则提前结束训练
        self.num_classes = len(self.class_list)                         # 类别数
        self.n_vocab = 0                                                # 词表大小，在运行时赋值
        self.num_epochs = 10                                            # epoch数
        self.batch_size = 128                                           # mini-batch大小
        self.pad_size = 32                                              # 每句话处理成的长度(短填长切)
        self.learning_rate = 1e-3                                       # 学习率
        self.embed = self.embedding_pretrained.size(1)\
            if self.embedding_pretrained is not None else 300           # 字向量维度, 若使用了预训练词向量，则维度统一
        self.hidden_size = 256                                          # lstm隐藏单元数
        self.num_layers = 1                                             # lstm层数

模型定义类

class Model(nn.Module):
    def __init__(self, config):
        super(Model, self).__init__()
        
        if config.embedding_pretrained is not None:  # 加载初始化好的预训练词/字嵌入矩阵  微调funetuning
            self.embedding = nn.Embedding.from_pretrained(config.embedding_pretrained, freeze=False)
        else:  # 否则随机初始化词/字嵌入矩阵 指定填充对应的索引
            self.embedding = nn.Embedding(config.n_vocab, config.embed, padding_idx=config.n_vocab - 1)
        
        #单层双向lstm batch_size为第一维度
        self.lstm = nn.LSTM(config.embed, config.hidden_size, config.num_layers,
                            bidirectional=True, batch_first=True, dropout=config.dropout)
        
        self.maxpool = nn.MaxPool1d(config.pad_size) #沿长度方向作全局最大池化
        #输出层
        self.fc = nn.Linear(config.hidden_size * 2 + config.embed, config.num_classes)

    def forward(self, x):
        x, _ = x #（batch,seq_len）
        embed = self.embedding(x)  # [batch_size, seq_len, embeding]=[64, 32, 64]
        out, _ = self.lstm(embed)  #(batch_size,seq_len,hidden_size*2)
        out = torch.cat((embed, out), 2) #把词嵌入和lstm输出进行拼接 (batch,seq_len.embed+hidden_size*2)
        out = F.relu(out)
        out = out.permute(0, 2, 1) #(batch,embed+hidden_size*2,seq_len)
        out = self.maxpool(out).squeeze() #（batch,embed+hidden_size*2）
        out = self.fc(out) #(batch,classes)
        return out

5. TextRNN_Atten

配置类

class Config(object):

    """配置参数"""
    def __init__(self, dataset, embedding):
        self.model_name = 'TextRNN_Att'
        # 训练集、验证集、测试集路径
        self.train_path = dataset + '/data/train.txt'
        self.dev_path = dataset + '/data/dev.txt'
        self.test_path = dataset + '/data/test.txt'
        # 数据集的所有类别
        self.class_list = [x.strip() for x in open(
            dataset + '/data/class.txt').readlines()]
        # 构建好的词/字典路径
        self.vocab_path = dataset + '/data/vocab.pkl'
        # 训练好的模型参数保存路径
        self.save_path = dataset + '/saved_dict/' + self.model_name + '.ckpt'
        # 模型日志保存路径
        self.log_path = dataset + '/log/' + self.model_name
        # 如果词/字嵌入矩阵不随机初始化 则加载初始化好的词/字嵌入矩阵 类别为float32 并转换为tensor 否则为None
        self.embedding_pretrained = torch.tensor(
            np.load(dataset + '/data/' + embedding)["embeddings"].astype('float32')) \
            if embedding != 'random' else None
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')  # 设备

        self.dropout = 0.5                                              # 随机失活
        self.require_improvement = 1000                                 # 若超过1000batch效果还没提升，则提前结束训练
        self.num_classes = len(self.class_list)                         # 类别数
        self.n_vocab = 0                                                # 词表大小，在运行时赋值
        self.num_epochs = 10                                            # epoch数
        self.batch_size = 128                                           # mini-batch大小
        self.pad_size = 32                                              # 每句话处理成的长度(短填长切)
        self.learning_rate = 1e-3                                       # 学习率
        self.embed = self.embedding_pretrained.size(1)\
            if self.embedding_pretrained is not None else 300           # 字向量维度, 若使用了预训练词向量，则维度统一
        self.hidden_size = 128                                          # lstm隐藏单元数
        self.num_layers = 2                                             # lstm层数
        self.hidden_size2 = 64                                          #全连接层隐藏单元数

模型定义类

class Model(nn.Module):
    def __init__(self, config):
        super(Model, self).__init__()
        
        if config.embedding_pretrained is not None:  # 加载初始化好的预训练词/字嵌入矩阵  微调funetuning
            self.embedding = nn.Embedding.from_pretrained(config.embedding_pretrained, freeze=False)
        else:  # 否则随机初始化词/字嵌入矩阵 指定填充对应的索引
            self.embedding = nn.Embedding(config.n_vocab, config.embed, padding_idx=config.n_vocab - 1)
        
        #2层双向 LSTM batch_size为第一维度
        self.lstm = nn.LSTM(config.embed, config.hidden_size, config.num_layers,
                            bidirectional=True, batch_first=True, dropout=config.dropout)
        self.tanh1 = nn.Tanh()
        # self.u = nn.Parameter(torch.Tensor(config.hidden_size * 2, config.hidden_size * 2))
        #定义一个参数向量 作为Query
        self.w = nn.Parameter(torch.Tensor(config.hidden_size * 2))
        self.tanh2 = nn.Tanh()
        #隐层
        self.fc1 = nn.Linear(config.hidden_size * 2, config.hidden_size2)
        #输出层
        self.fc = nn.Linear(config.hidden_size2, config.num_classes)

    def forward(self, x):
        x, _ = x #（batch,seq_len)
        emb = self.embedding(x)  # [batch_size, seq_len, embeding]=[128, 32, 300]
        H, _ = self.lstm(emb)  # [batch_size, seq_len, hidden_size * num_direction]=[128, 32, 256] 各时刻隐状态 作为value

        M = self.tanh1(H)  # [128, 32, 256] 各时刻隐状态通过tanh激活函数 作为Key
        # M = torch.tanh(torch.matmul(H, self.u))
        #Key和Query作运算 在通过softmax 得到每个时刻对应的权重
        alpha = F.softmax(torch.matmul(M, self.w), dim=1).unsqueeze(-1)  # [128, 32, 1]
        #各时刻的权重和各时刻的隐状态Value对应相乘
        out = H * alpha  # [128, 32, 256]
        #再相加
        out = torch.sum(out, 1)  # [128, 256] （batch,hidden*2）
        out = F.relu(out)
        out = self.fc1(out) #(batch,hidden2）
        out = self.fc(out)  # (batch,classes）
        return out

6. DPCNN

配置类

class Config(object):

    """配置参数"""
    def __init__(self, dataset, embedding):
        self.model_name = 'DPCNN'
        # 训练集、验证集、测试集路径
        self.train_path = dataset + '/data/train.txt'
        self.dev_path = dataset + '/data/dev.txt'
        self.test_path = dataset + '/data/test.txt'
        # 数据集的所有类别
        self.class_list = [x.strip() for x in open(
            dataset + '/data/class.txt').readlines()]
        # 构建好的词/字典路径
        self.vocab_path = dataset + '/data/vocab.pkl'
        # 训练好的模型参数保存路径
        self.save_path = dataset + '/saved_dict/' + self.model_name + '.ckpt'
        # 模型日志保存路径
        self.log_path = dataset + '/log/' + self.model_name
        # 如果词/字嵌入矩阵不随机初始化 则加载初始化好的词/字嵌入矩阵 类别为float32 并转换为tensor 否则为None
        self.embedding_pretrained = torch.tensor(
            np.load(dataset + '/data/' + embedding)["embeddings"].astype('float32')) \
            if embedding != 'random' else None
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')  # 设备

        self.dropout = 0.5                                              # 随机失活
        self.require_improvement = 1000                                 # 若超过1000batch效果还没提升，则提前结束训练
        self.num_classes = len(self.class_list)                         # 类别数
        self.n_vocab = 0                                                # 词表大小，在运行时赋值
        self.num_epochs = 20                                            # epoch数
        self.batch_size = 128                                           # mini-batch大小
        self.pad_size = 32                                              # 每句话处理成的长度(短填长切)
        self.learning_rate = 1e-3                                       # 学习率
        self.embed = self.embedding_pretrained.size(1)\
            if self.embedding_pretrained is not None else 300           # 字/词向量维度
        self.num_filters = 250                                          # 卷积核数量(channels数)

模型定义类


class Model(nn.Module):
    def __init__(self, config):
        super(Model, self).__init__()

        if config.embedding_pretrained is not None:  # 加载初始化好的预训练词/字嵌入矩阵  微调funetuning
            self.embedding = nn.Embedding.from_pretrained(config.embedding_pretrained, freeze=False)
        else:  # 否则随机初始化词/字嵌入矩阵 指定填充对应的索引
            self.embedding = nn.Embedding(config.n_vocab, config.embed, padding_idx=config.n_vocab - 1)

        #region embedding 类似于TextCNN中的卷积操作
        self.conv_region = nn.Conv2d(1, config.num_filters, (3, config.embed), stride=1)
        
        self.conv = nn.Conv2d(config.num_filters, config.num_filters, (3, 1), stride=1)
        self.max_pool = nn.MaxPool2d(kernel_size=(3, 1), stride=2)
        self.padding1 = nn.ZeroPad2d((0, 0, 1, 1))  # top bottom 上下各添加1个0
        self.padding2 = nn.ZeroPad2d((0, 0, 0, 1))  # bottom 下添加一个0
        self.relu = nn.ReLU()
        self.fc = nn.Linear(config.num_filters, config.num_classes)

    def forward(self, x):
        x = x[0]    #（batch,seq_len）
        x = self.embedding(x) #(batch,seq_len,embed)
        x = x.unsqueeze(1)  # 添加通道维 进行2d卷积 (batch,1,seq_len,embed)
        x = self.conv_region(x)  # (batch,num_filters,seq_len-3+1,1)
        #先卷积 再填充 等价于等长卷积  序列长度不变
        x = self.padding1(x)  # [batch_size, num_filters, seq_len, 1]
        x = self.relu(x)
        x = self.conv(x)  # [batch_size, num_filters, seq_len-3+1, 1]
        x = self.padding1(x)  # [batch_size, num_filters, seq_len, 1]
        x = self.relu(x)
        x = self.conv(x)  # [batch_size, num_filters, seq_len-3+1, 1]
        while x.size()[2] > 2: 
            x = self._block(x)
        x = x.squeeze()  # [batch_size, num_filters]
        x = self.fc(x)  #（batch,classes）
        return x

    def _block(self, x):
        x = self.padding2(x) #[batch_size, num_filters, seq_len-1, 1]
        #长度减半
        px = self.max_pool(x) #[batch_size, num_filters, （seq_len-1）/2, 1]

        #等长卷积 长度不变
        x = self.padding1(px)
        x = F.relu(x)
        x = self.conv(x)

        # 等长卷积 长度不变
        x = self.padding1(x)
        x = F.relu(x)
        x = self.conv(x)

        # Short Cut
        x = x + px
        return x

7. Transformer

配置类

class Config(object):

    """配置参数"""
    def __init__(self, dataset, embedding):
        self.model_name = 'Transformer'
        # 训练集、验证集、测试集路径
        self.train_path = dataset + '/data/train.txt'
        self.dev_path = dataset + '/data/dev.txt'
        self.test_path = dataset + '/data/test.txt'
        # 数据集的所有类别
        self.class_list = [x.strip() for x in open(
            dataset + '/data/class.txt').readlines()]
        # 构建好的词/字典路径
        self.vocab_path = dataset + '/data/vocab.pkl'
        # 训练好的模型参数保存路径
        self.save_path = dataset + '/saved_dict/' + self.model_name + '.ckpt'
        # 模型日志保存路径
        self.log_path = dataset + '/log/' + self.model_name
        # 如果词/字嵌入矩阵不随机初始化 则加载初始化好的词/字嵌入矩阵 类别为float32 并转换为tensor 否则为None
        self.embedding_pretrained = torch.tensor(
            np.load(dataset + '/data/' + embedding)["embeddings"].astype('float32')) \
            if embedding != 'random' else None
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')  # 设备

        self.dropout = 0.5                                              # 随机失活
        self.require_improvement = 2000                                 # 若超过2000batch效果还没提升，则提前结束训练
        self.num_classes = len(self.class_list)                         # 类别数
        self.n_vocab = 0                                                # 词表大小，在运行时赋值
        self.num_epochs = 20                                            # epoch数
        self.batch_size = 128                                           # mini-batch大小
        self.pad_size = 32                                              # 每句话处理成的长度(短填长切)
        self.learning_rate = 5e-4                                       # 学习率
        self.embed = self.embedding_pretrained.size(1)\
            if self.embedding_pretrained is not None else 300           # 字向量维度
        self.dim_model = 300
        self.hidden = 1024
        self.last_hidden = 512
        self.num_head = 5               #5头注意力机制
        self.num_encoder = 2           #两个transformer encoder block

模型定义类


class Model(nn.Module):
    def __init__(self, config):
        super(Model, self).__init__()
        
        #词/字嵌入
        if config.embedding_pretrained is not None:  # 加载初始化好的预训练词/字嵌入矩阵  微调funetuning
            self.embedding = nn.Embedding.from_pretrained(config.embedding_pretrained, freeze=False)
        else:  # 否则随机初始化词/字嵌入矩阵 指定填充对应的索引
            self.embedding = nn.Embedding(config.n_vocab, config.embed, padding_idx=config.n_vocab - 1)
        
        #位置编码
        self.postion_embedding = Positional_Encoding(config.embed, config.pad_size, config.dropout, config.device)
        
        #transformer encoder block
        self.encoder = Encoder(config.dim_model, config.num_head, config.hidden, config.dropout)
        
        #多个transformer encoder block
        self.encoders = nn.ModuleList([
            copy.deepcopy(self.encoder)
            # Encoder(config.dim_model, config.num_head, config.hidden, config.dropout)
            for _ in range(config.num_encoder)])
        
        #输出层
        self.fc1 = nn.Linear(config.pad_size * config.dim_model, config.num_classes)
        # self.fc2 = nn.Linear(config.last_hidden, config.num_classes)
        # self.fc1 = nn.Linear(config.dim_model, config.num_classes)

    def forward(self, x):
        out = self.embedding(x[0]) #(batch,seq_len) -> (batch,seq_len,embed)
        out = self.postion_embedding(out) # 添加位置编码 (batch,seq_len,embed)
        for encoder in self.encoders: #通过多个ender block
            out = encoder(out)  #(batch,seq_len,dim_model)
        out = out.view(out.size(0), -1) #（batch,seq_len*dim_model）
        # out = torch.mean(out, 1)
        out = self.fc1(out) #(batch,classes)
        return out


class Encoder(nn.Module):
    def __init__(self, dim_model, num_head, hidden, dropout):
        super(Encoder, self).__init__()
        #多头注意力机制
        self.attention = Multi_Head_Attention(dim_model, num_head, dropout)
        #两个全连接层
        self.feed_forward = Position_wise_Feed_Forward(dim_model, hidden, dropout)

    def forward(self, x): #x (batch,seq_len,embed_size)  embed_size = dim_model
        out = self.attention(x) #计算多头注意力结果 (batch,seq_len,dim_model)
        out = self.feed_forward(out) #通过两个全连接层增加 非线性转换能力 (batch,seq_len,dim_model)
        return out


class Positional_Encoding(nn.Module):
    #位置编码
    def __init__(self, embed, pad_size, dropout, device):
        super(Positional_Encoding, self).__init__()
        self.device = device
        #利用sin cos生成绝对位置编码
        self.pe = torch.tensor([[pos / (10000.0 ** (i // 2 * 2.0 / embed)) for i in range(embed)] for pos in range(pad_size)])
        self.pe[:, 0::2] = np.sin(self.pe[:, 0::2])
        self.pe[:, 1::2] = np.cos(self.pe[:, 1::2])
        self.dropout = nn.Dropout(dropout)

    def forward(self, x): 
        #token embedding + 绝对位置编码
        out = x + nn.Parameter(self.pe, requires_grad=False).to(self.device)
        #再通过dropout
        out = self.dropout(out)
        return out


class Scaled_Dot_Product_Attention(nn.Module):
    '''Scaled Dot-Product Attention '''
    def __init__(self):
        super(Scaled_Dot_Product_Attention, self).__init__()

    def forward(self, Q, K, V, scale=None):
        '''
        Args:
            Q: [batch_size, len_Q, dim_Q]
            K: [batch_size, len_K, dim_K]
            V: [batch_size, len_V, dim_V]
            scale: 缩放因子 论文为根号dim_K
        Return:
            self-attention后的张量，以及attention张量
        '''
        #Q与K的第2、3维转置计算内积  (batch*num_head,seq_len,seq_len)
        attention = torch.matmul(Q, K.permute(0, 2, 1))
        if scale: #作缩放 减小结果的方差 
            attention = attention * scale
        # if mask:  # TODO change this 
        #     attention = attention.masked_fill_(mask == 0, -1e9)
        attention = F.softmax(attention, dim=-1) #转换为权重
        context = torch.matmul(attention, V) #再与V运算 得到结果 (batch*num_head,seq_len,dim_head)
        return context


class Multi_Head_Attention(nn.Module):
    #多头注意力机制 encoder block的第一部分
    def __init__(self, dim_model, num_head, dropout=0.0):
        super(Multi_Head_Attention, self).__init__()
        self.num_head = num_head  #头数 
        assert dim_model % num_head == 0 #必须整除
        self.dim_head = dim_model // self.num_head 
        #分别通过三个Dense层 生成Q、K、V
        self.fc_Q = nn.Linear(dim_model, num_head * self.dim_head)
        self.fc_K = nn.Linear(dim_model, num_head * self.dim_head)
        self.fc_V = nn.Linear(dim_model, num_head * self.dim_head)
        #Attention计算
        self.attention = Scaled_Dot_Product_Attention()
        self.fc = nn.Linear(num_head * self.dim_head, dim_model)
        self.dropout = nn.Dropout(dropout)
        #层归一化
        self.layer_norm = nn.LayerNorm(dim_model)

    def forward(self, x): #（batch,seq_len,dim_model）
        batch_size = x.size(0) 
        # Q,K,V维度 (batch,seq_len,dim_head*num_head)
        Q = self.fc_Q(x)  
        K = self.fc_K(x)
        V = self.fc_V(x)
        #沿第三个维度进行切分 切分为num_head份 再沿第一个维度拼接 多个注意力头并行计算
        #Q,K,V维度 (batch*num_head,seq_len,dim_head)
        Q = Q.view(batch_size * self.num_head, -1, self.dim_head) 
        K = K.view(batch_size * self.num_head, -1, self.dim_head)
        V = V.view(batch_size * self.num_head, -1, self.dim_head)
        # if mask:  # TODO
        #     mask = mask.repeat(self.num_head, 1, 1)  # TODO change this
        scale = K.size(-1) ** -0.5  # 缩放因子 dim_head的开放取倒数 对内积结果进行缩放 减小结果的方差 有利于训练
        #attention计算 多个注意力头并行计算（矩阵运算）
        context = self.attention(Q, K, V, scale)
        
        #多头注意力计算结果 沿第一个维度进行切分 再沿第三个维度拼接 转为原来的维度(batch,seq_len,dim_head*num_head)
        context = context.view(batch_size, -1, self.dim_head * self.num_head)
        
        out = self.fc(context)#(batch,seq_len,dim_model)
        out = self.dropout(out)
        out = out + x  # 残差连接
        out = self.layer_norm(out)
        return out


class Position_wise_Feed_Forward(nn.Module):
    #encoder block的第二部分
    def __init__(self, dim_model, hidden, dropout=0.0):
        #定义两个全连接层 多头注意力的计算结果 通过两个全连接层 增加非线性 
        super(Position_wise_Feed_Forward, self).__init__()
        self.fc1 = nn.Linear(dim_model, hidden)
        self.fc2 = nn.Linear(hidden, dim_model)
        self.dropout = nn.Dropout(dropout)
        self.layer_norm = nn.LayerNorm(dim_model)

    def forward(self, x): #（batch,seq_len,dim_model）
        out = self.fc1(x)  #（batch,seq_len,hidden）
        out = F.relu(out)
        out = self.fc2(out) #（batch,seq_len,dim_model）
        out = self.dropout(out)
        out = out + x  # 残差连接
        out = self.layer_norm(out) #层归一化
        return out

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文本分类(二) | (4) 模型及其配置的定义

1. FastText

2. TextCNN

3. TextRNN

4. TextRCNN

5. TextRNN_Atten

6. DPCNN

7. Transformer

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