Coding-神经网络训练框架

• 摘要
• main.py

摘要

神经网络训练框架

# 设置超参数
# input_size = 1625 # mini_spectrum
input_size = 2048 # good_spectrum
hidden_size = 128
num_classes = 5
learning_rate = 0.001
num_epochs = 300
batch_size = 32

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# 训练模型
for epoch in range(num_epochs):
    model.train()
    running_loss = 0.0
    for inputs, labels in train_loader:
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    
    print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {running_loss/len(train_loader):.4f}')

# 测试模型
model.eval()
with torch.no_grad():
    correct = 0
    total = 0
    class_correct = list(0. for i in range(num_classes))
    class_total = list(0. for i in range(num_classes))

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

        # 统计每个类的准确率
        c = (predicted == labels).squeeze()
        for i in range(len(labels)):
            _label = labels[i]
            class_correct[_label] += c[i].item()
            class_total[_label] += 1

    print(f'Accuracy of the model on the test data: {100 * correct / total:.2f}%')
    # 统计每个类的样本数量