TensorboardX

    writer.add_scalar('Train_loss', loss, (epoch))

    writer.add_scalar('Test/Accu', correct/total, iterion) 

import torch 
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
from tensorboardX import SummaryWriter
writer = SummaryWriter(logdir='log')


# Device configuration
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

# Hyper parameters
num_epochs = 5
num_classes = 2
batch_size = 100
learning_rate = 0.001

# MNIST dataset
train_dataset = torchvision.datasets.MNIST(root='../../data/',
                                           train=True, 
                                           transform=transforms.ToTensor(),
                                           download=True)

test_dataset = torchvision.datasets.MNIST(root='../../data/',
                                          train=False, 
                                          transform=transforms.ToTensor())

# Data loader
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
                                           batch_size=batch_size, 
                                           shuffle=True)

test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
                                          batch_size=batch_size, 
                                          shuffle=False)

# Convolutional neural network (two convolutional layers)
class ConvNet(nn.Module):
    def __init__(self, num_classes=10):
        super(ConvNet, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2),#一般Kernel_size=5,padding=2
            nn.BatchNorm2d(16),#make feature's mean_value=1,variance=1,learn or fit better from good distribution
            nn.ReLU(),#standard activation fuction for cnn
            nn.MaxPool2d(kernel_size=2, stride=2))#demension_reduce
        self.layer2 = nn.Sequential(
            nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2))
        self.fc = nn.Linear(7*7*32, num_classes)
        
    def forward(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        out = out.reshape(out.size(0), -1)#equal to flatten layer
        out = self.fc(out)
        return out

model = ConvNet(num_classes).to(device)

# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

# Train the model
total_step = len(train_loader)
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        images = images.to(device)
        labels = labels.to(device)
        
        # Forward pass
        outputs = model(images)
        loss = criterion(outputs, labels)
        
        # Backward and optimize
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
        #tensorbordXforloss,               
        writer.add_scalar('Train_loss', loss, (epoch))#loss
        
        
        if (i+1) % 100 == 0:
            print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}' 
                   .format(epoch+1, num_epochs, i+1, total_step, loss.item()))
        

writer.close() 
# Test the model
model.eval()  # eval mode (batchnorm uses moving mean/variance instead of mini-batch mean/variance)
with torch.no_grad():
    correct = 0
    total = 0
    for i,(images, labels) in enumerate(test_loader):
        images = images.to(device)
        labels = labels.to(device)
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
        writer.add_scalar('Test/Accu', correct/total, i)
    print('Test Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))


# Save the model checkpoint
# torch.save(model.state_dict(), 'model.ckpt')
# torch.save(model.state_dict(), "my_model.pth")  # 只保存模型的参数
torch.save(model,"./my_model.pth")  # 保存整个模型

这个tensorboard需要加载的日志文件夹我放在与.py文件一个目录的叫做log，需在终端输入指令

tensorboard --logdir log

        然后复制这个链接去google游览器粘贴就出现如下所示： 

         在代码中，训练的loss每个epoch下的loss都记录一次（当然你也可以写在batch里面或者第二个内层循环中这样曲线更平滑），acc在验证推理的时候的循环中with torch.no_grad():写到。

2.画出构建的模型的图         

# -*- coding: utf-8 -*-
# @Author  : Miaoshuyu
# @Email   : miaohsuyu319@163.com
import torch
import torch.nn as nn
import torch.nn.functional as F
from tensorboardX import SummaryWriter
writer = SummaryWriter(logdir='log')
class Net1(nn.Module):
    def __init__(self):
        super(Net1, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)
        self.bn = nn.BatchNorm2d(20)

    def forward(self, x):
        x = F.max_pool2d(self.conv1(x), 2)
        x = F.relu(x) + F.relu(-x)
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = self.bn(x)
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        x = F.softmax(x, dim=1)
        return x


dummy_input = torch.rand(13, 1, 28, 28)

model = Net1()
with SummaryWriter(comment='Net1') as w:
    w.add_graph(model, (dummy_input,))

writer.close()

        tensorborad还有许多的功能，细枝末节，详细使用方式请观看下方参考资料，尤其，是最后一个链接。  

参考资料

TensorBoard in PyTorch

详解PyTorch项目使用TensorboardX进行训练可视化

详解PyTorch项目使用TensorboardX进行训练可视化_浅度寺-CSDN博客_tensorboardx

解PyTorch项目使用TensorboardX进行训练可视化

PyTorch绘制训练过程的accuracy和loss曲线_Tequila-CSDN博客_pytorch绘制loss曲线

Pytorch使用tensorboardX可视化。超详细！！！

Pytorch使用tensorboardX可视化。超详细！！！ - 简书

GitHub - miaoshuyu/pytorch-tensorboardx-visualization: The use examples of tensorboard on pytorch

PyTorch使用tensorboardX//youyong

PyTorch使用tensorboardX - 知乎