hoodiexxx/my_text_cnn_classification_model

Text CNN Classification Model

Param

textCNN_param = {
        'vocab_size': len(word2ind) + 1,
        'embed_dim': 128, # 1 x 128 vector
        'class_num': len(label_w2n),
        "kernel_num": 16,
        "kernel_size": [3, 4, 5],
        "dropout": 0.5,
    }
    dataLoader_param = {
        'batch_size': 128,
        'shuffle': True,
    }

Model

textcnn.bin

class textCNN(nn.Module):
    def __init__(self, param):
        super(textCNN, self).__init__()
        ci = 1  # input chanel size
        # kernel 卷积核
        kernel_num = param['kernel_num']  # output chanel size
        kernel_size = param['kernel_size']
        vocab_size = param['vocab_size']
        embed_dim = param['embed_dim']  # embedding dimension
        dropout = param['dropout']
        class_num = param['class_num']
        self.param = param
        # 把token随机向量化
        self.embed = nn.Embedding(vocab_size, embed_dim, padding_idx=1)

        # 三个不同长度的卷积
        self.conv11 = nn.Conv2d(ci, kernel_num, (kernel_size[0], embed_dim))
        self.conv12 = nn.Conv2d(ci, kernel_num, (kernel_size[1], embed_dim))
        self.conv13 = nn.Conv2d(ci, kernel_num, (kernel_size[2], embed_dim))
        # 三个不同长度的卷积

        # increasing the ability of calculation by dropout
        self.dropout = nn.Dropout(dropout)
        self.fc1 = nn.Linear(len(kernel_size) * kernel_num, class_num)

    def init_embed(self, embed_matrix):
        self.embed.weight = nn.Parameter(torch.Tensor(embed_matrix))

    @staticmethod
    def conv_and_pool(x, conv):
        # x: (batch, 1, sentence_length,  )
        x = conv(x)
        # x: (batch, kernel_num, H_out, 1)
        x = F.relu(x.squeeze(3))
        # x: (batch, kernel_num, H_out)
        x = F.max_pool1d(x, x.size(2)).squeeze(2)
        #  (batch, kernel_num)
        return x

    def forward(self, x):
        # x: (batch, sentence_length)
        x = self.embed(x)
        # x: (batch, sentence_length, embed_dim)
        # TODO init embed matrix with pre-trained
        x = x.unsqueeze(1)
        # x: (batch, 1, sentence_length, embed_dim)
        x1 = self.conv_and_pool(x, self.conv11)  # (batch, kernel_num)
        x2 = self.conv_and_pool(x, self.conv12)  # (batch, kernel_num)
        x3 = self.conv_and_pool(x, self.conv13)  # (batch, kernel_num)
        x = torch.cat((x1, x2, x3), 1)  # (batch, 3 * kernel_num)
        x = self.dropout(x)
        logit = F.log_softmax(self.fc1(x), dim=1)
        return logit

Trainer

    # set the seed for ensuring reproducibility
    seed = 3407
    torch.cuda.manual_seed(seed)
    torch.manual_seed(seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False

    word2ind, ind2word = sen2inds.get_worddict('wordLabel.txt')
    label_w2n, label_n2w = sen2inds.read_labelFile('data/label.txt')

    textCNN_param = {
        'vocab_size': len(word2ind) + 1,
        'embed_dim': 128, # 1 x 128 vector
        'class_num': len(label_w2n),
        "kernel_num": 16,
        "kernel_size": [3, 4, 5],
        "dropout": 0.5,
    }
    dataLoader_param = {
        'batch_size': 128,
        'shuffle': True,
    }

    # # device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
    device = 'cpu'

    # init dataset
    print('init dataset...')
    trainDataFile = 'traindata_vec.txt'
    valDataFile = 'devdata_vec.txt'
    train_dataset = textCNN_data(trainDataFile)
    train_dataLoader = DataLoader(train_dataset,
                                  batch_size=dataLoader_param['batch_size'],
                                  shuffle=True)

    val_dataset = textCNN_data(valDataFile)
    val_dataLoader = DataLoader(val_dataset,
                                batch_size=dataLoader_param['batch_size'], # batch size 128
                                shuffle=False)

    # init net
    print('init net...')
    net = textCNN(textCNN_param)
    print(net)
    net.to(device)
    optimizer = torch.optim.Adam(net.parameters(), lr=0.001)
    criterion = nn.CrossEntropyLoss()

    print("training...")
    net.train()
    best_dev_acc = 0
    for epoch in range(100):
        for i, (clas, sentences) in enumerate(train_dataLoader):
            out = net(sentences)
            loss = criterion(out, clas)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            if (i + 1) % 10 == 0:
                print("epoch:", epoch + 1, "step:", i + 1, "loss:", loss.item())

        dev_acc = validation(model=net, val_dataLoader=val_dataLoader,
                             device=device)
        if best_dev_acc < dev_acc:
            best_dev_acc = dev_acc
            print("save model...")
            torch.save(net.state_dict(), "textcnn.bin")
            print("epoch:", epoch + 1, "step:", i + 1, "loss:", loss.item())
        print("best dev acc %.4f  dev acc %.4f" % (best_dev_acc, dev_acc))