pytorch通过 tensorboardX 调用 Tensorboard 进行可视化

示例

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import datasets, transformsfrom tensorboardX import SummaryWriter# 定义神经网络模型
class SimpleCNN(nn.Module):def __init__(self):super(SimpleCNN, self).__init__()self.conv1 = nn.Conv2d(1, 32, 3)self.fc = nn.Linear(32*26*26, 10)def forward(self, x):x = self.conv1(x)x = x.view(x.size(0), -1)x = self.fc(x)return x# 数据预处理和加载
transform = transforms.Compose([transforms.ToTensor()])
train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)# 初始化模型、损失函数和优化器
model = SimpleCNN()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)# 创建 Summary