textcnn声调识别模型pytorch代码

以下是使用 PyTorch 实现的 TextCNN 声调识别模型的示例代码：

import torch
import torch.nn as nn
import torch.nn.functional as F

class TextCNN(nn.Module):
    def __init__(self, vocab_size, embedding_dim, num_classes, num_filters):
        super(TextCNN, self).__init__()
        
        # Embedding layer
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        
        # Convolutional layers
        self.conv1 = nn.Conv2d(1, num_filters, (2, embedding_dim))
        self.conv2 = nn.Conv2d(1, num_filters, (3, embedding_dim))
        self.conv3 = nn.Conv2d(1, num_filters, (4, embedding_dim))
        
        # Pooling layer
        self.pool = nn.MaxPool2d((3, 1))
        
        # Fully connected layer
        self.fc = nn.Linear(num_filters * 3, num_classes)
        
    def forward(self, x):
        x = self.embedding(x)
        x = x.unsqueeze(1) # add channel dimension for conv2d input
        
        # Convolutional layers with ReLU activation and max pooling
        conv1_out = F.relu(self.conv1(x)).squeeze(3)
        conv1_out = self.pool(conv1_out).squeeze(2)
        
        conv2_out = F.relu(self.conv2(x)).squeeze(3)
        conv2_out = self.pool(conv2_out).squeeze(2)
        
        conv3_out = F.relu(self.conv3(x)).squeeze(3)
        conv3_out = self.pool(conv3_out).squeeze(2)
        
        # Concatenate the output of the convolutional layers
        x = torch.cat((conv1_out, conv2_out, conv3_out), 1)
        
        # Fully connected layer with softmax activation
        x = self.fc(x)
        x = F.softmax(x, dim=1)
        return x

在上面的代码中，`TextCNN` 类继承了 `nn.Module` 类，其中实现了一个带有嵌入层、卷积层、池化层和全连接层的 TextCNN 声调识别模型。在 `__init__` 方法中，我们定义了各个层的结构和参数，而在 `forward` 方法中，我们对输入进行了嵌入处理、卷积、池化和全连接处理，最终输出一个概率分布向量。

在使用该模型时，我们需要指定以下参数：

- `vocab_size`：词汇表大小。
- `embedding_dim`：嵌入维度。
- `num_classes`：类别数。
- `num_filters`：卷积核数量。

例如，以下是使用该模型进行声调分类的示例代码：

# Define model hyperparameters
vocab_size = len(word2id)
embedding_dim = 128
num_classes = 4
num_filters = 64

# Create TextCNN model
model = TextCNN(vocab_size, embedding_dim, num_classes, num_filters)

# Define loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# Train the model
for epoch in range(num_epochs):
    for i, batch in enumerate(train_loader):
        inputs, labels = batch
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        
        if (i+1) % 1000 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, i+1, len(train_loader), loss.item()))

# Test the model
with torch.no_grad():
    correct = 0
    total = 0
    for batch in test_loader:
        inputs, labels = batch
        outputs = model(inputs)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

    print('Test Accuracy: {} %'.format(100 * correct / total))

在上面的代码中，我们首先定义了模型的超参数，并创建了一个 `TextCNN` 对象。然后，我们定义了损失函数和优化器，使用训练集训练模型，并在测试集上评估模型的性能。