PyTorch在CentOS上的可视化工具如何使用

pip install tensorboard matplotlib seaborn pandas torchviz

from torch.utils.tensorboard import SummaryWriter

writer = SummaryWriter()

for epoch in range(num_epochs):
    # 训练代码
    writer.add_scalar('训练损失', loss, epoch)
    writer.add_scalar('训练准确率', accuracy, epoch)
writer.close()

tensorboard --logdir=runs

import matplotlib.pyplot as plt

plt.plot(epochs, train_losses, 'bo', label='训练损失')
plt.plot(epochs, val_losses, 'b', label='验证损失')
plt.title('训练和验证损失')
plt.xlabel('轮次')
plt.ylabel('损失')
plt.legend()
plt.show()

import seaborn as sns

sns.histplot(data['损失'], kde=True)
sns.histplot(data['准确率'], kde=True)
plt.show()

import torch
from torchviz import make_dot

input_tensor = torch.randn(1, 3, 224, 224)
dot = make_dot(model(input_tensor), params=dict(model.named_parameters()))
dot.render("模型结构", format="pdf")

def get_activation(name):
    def hook(module, input, output):
        activations[name] = output.detach()
    return hook

model.layer1[0].conv1.register_forward_hook(get_activation('layer1_0_conv1'))

# ... (可视化代码，根据激活数据的形状调整) ...