textcnn四个声调识别模型pytorch完整代码

以下是一个基于PyTorch的TextCNN四个声调识别模型的完整代码示例：

首先，需要导入所需的Python库：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np

然后，我们定义TextCNN模型：

class TextCNN(nn.Module):
    def __init__(self, embedding_dim, num_filters, filter_sizes, num_classes, vocab_size, dropout_prob):
        super(TextCNN, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.convs = nn.ModuleList([nn.Conv2d(1, num_filters, (f, embedding_dim)) for f in filter_sizes])
        self.dropout = nn.Dropout(dropout_prob)
        self.fc = nn.Linear(num_filters * len(filter_sizes), num_classes)

    def forward(self, x):
        x = self.embedding(x)
        x = x.unsqueeze(1)
        x = [nn.functional.relu(conv(x)).squeeze(3) for conv in self.convs]
        x = [nn.functional.max_pool1d(i, i.size(2)).squeeze(2) for i in x]
        x = torch.cat(x, 1)
        x = self.dropout(x)
        x = self.fc(x)
        return x

接下来，我们定义训练和测试函数：

def train(model, optimizer, criterion, train_loader):
    model.train()
    total_loss = 0
    for batch in train_loader:
        text, label = batch
        text = text.to(device)
        label = label.to(device)
        optimizer.zero_grad()
        output = model(text)
        loss = criterion(output, label)
        loss.backward()
        optimizer.step()
        total_loss += loss.item()
    return total_loss / len(train_loader)

def test(model, criterion, test_loader):
    model.eval()
    total_loss = 0
    correct = 0
    with torch.no_grad():
        for batch in test_loader:
            text, label = batch
            text = text.to(device)
            label = label.to(device)
            output = model(text)
            total_loss += criterion(output, label).item()
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(label.view_as(pred)).sum().item()
    accuracy = correct / len(test_loader.dataset)
    return total_loss / len(test_loader), accuracy

最后，我们可以开始训练和测试模型：

# 定义超参数
embedding_dim = 100
num_filters = 64
filter_sizes = [2, 3, 4]
num_classes = 4
vocab_size = len(TEXT.vocab)
dropout_prob = 0.5
lr = 0.001
batch_size = 64
epochs = 10

# 定义模型、损失函数和优化器
model = TextCNN(embedding_dim, num_filters, filter_sizes, num_classes, vocab_size, dropout_prob).to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=lr)

# 训练和测试模型
for epoch in range(1, epochs + 1):
    train_loss = train(model, optimizer, criterion, train_loader)
    test_loss, test_accuracy = test(model, criterion, test_loader)
    print(f'Epoch: {epoch} / {epochs}, Train Loss: {train_loss:.4f}, Test Loss: {test_loss:.4f}, Test Accuracy: {test_accuracy:.4f}')

这就是一个完整的TextCNN四个声调识别模型的PyTorch代码示例。