TextCNN算法流程

• 整体流程是将词拼接在一起，一句话构成一个特征图
• 根据卷积核得到多个特征向量
• 每个特征向量全局池化，选最大的特征作为这个特征向量的值
• 拼接特征值，得到句子的特征向量
• 全连接后得到目标维度的结果

完整代码

#!/usr/bin/env Python
# coding=utf-8
import torch
import torch.nn as nn
import torch.utils.data as Data
import torch.nn.functional as F


class TextCnnModel(nn.Module):
    def __init__(self, embedding_size, output_size, channels=256, filter_sizes=(2, 3, 4), dropout=0.5):
        """
        TextCnn 模型做文本分类
        :param embedding_size: 每个词向量的embedding长度
        :param output_size: 最后的输出个数、待分类的个数
        :param channels: 卷积核的数量
        :param filter_sizes: 卷积核尺寸
        :param dropout: 随机失活概率
        """
        super(TextCnnModel, self).__init__()
        self.convs = nn.ModuleList(
            [nn.Conv2d(1, channels, (k, embedding_size)) for k in filter_sizes])
        self.dropout = nn.Dropout(dropout)
        self.fc = nn.Linear(channels * len(filter_sizes), output_size)

    def conv_and_pool(self, x, conv):
        x = F.relu(conv(x)).squeeze(3)
        x = F.max_pool1d(x, x.size(2)).squeeze(2)
        return x

    def forward(self, x):
        out = x.unsqueeze(1)
        out = torch.cat([self.conv_and_pool(out, conv) for conv in self.convs], 1)
        out = self.dropout(out)
        out = self.fc(out)
        return out


def get_total_train_data(word_embedding_size, class_count):
    """得到全部的训练数据，这里需要替换成自己的数据"""
    import numpy as np
    x_train = torch.Tensor(np.random.random((1000, 20, word_embedding_size)))  # 维度是 [ 数据量, 一句话的单词个数, 句子的embedding]
    y_train = torch.Tensor(
        np.random.randint(0, class_count, size=(1000, 1))).long()  # [ 数据量, 句子的分类], 这里的class_count=4，就是四分类任务
    return x_train, y_train


if __name__ == '__main__':
    # ================参数=================
    epochs = 1000
    batch_size = 30
    embedding_size = 350
    output_class = 14
    # ================开始训练================
    x_train, y_train = get_total_train_data(embedding_size, output_class)
    train_loader = Data.DataLoader(
        dataset=Data.TensorDataset(x_train, y_train),  # 封装进Data.TensorDataset()类的数据，可以为任意维度
        batch_size=batch_size,  # 每块的大小
        shuffle=True,  # 要不要打乱数据 (打乱比较好)
        num_workers=6,  # 多进程（multiprocess）来读数据
        drop_last=True,
    )
    model = TextCnnModel(embedding_size=embedding_size, output_size=output_class)
    cross_loss = nn.CrossEntropyLoss()
    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)  # 优化器
    model.train()
    for i in range(epochs):
        for seq, labels in train_loader:
            optimizer.zero_grad()
            y_pred = model(seq)  # 压缩维度：得到输出，并将维度为1的去除
            single_loss = cross_loss(y_pred, labels.squeeze())
            single_loss.backward()
            optimizer.step()
            print("Step: " + str(i) + " loss : " + str(single_loss.detach().numpy()))