MNIST多分类Pytorch实现——up主：刘二大人《PyTorch深度学习实践》-优快云博客

本文链接：https://blog.youkuaiyun.com/weixin_44223180/article/details/129860972

该教程介绍了如何使用PyTorch进行深度学习实践，具体是通过一个无层模型在MNIST数据集上进行训练。模型包括了线性层、ReLU和sigmoid激活函数，以及CrossEntropyLoss作为损失函数，使用SGD优化器进行参数更新。文章展示了训练过程和损失函数变化情况。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

b站up主：刘二大人《PyTorch深度学习实践》
教程： https://www.bilibili.com/video/BV1Y7411d7Ys?p=6&vd_source=715b347a0d6cb8aa3822e5a102f366fe
$\\激活函数：ReLU+sigmoid \\交叉熵损失函数：nn.CrossEntropyLoss \\优化器：optim.SGD，lr=0.01，momentum=0.5$
网络结构：
在这里插入图片描述
源码：

import torch
from torchvision import transforms
from torchvision import datasets
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
import matplotlib.pyplot as plt

batch_size = 64
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.1307, ), (0.3081, ))
])
train_dataset = datasets.MNIST(root='../dataset/mnist/',
                train=True,
                download=True,
                transform=transform)
train_loader = DataLoader(train_dataset,
              shuffle=True,
              batch_size=batch_size)
test_dataset = datasets.MNIST(root='../dataset/mnist',
                train=False,
                download=True,
                transform=transform)
test_loader = DataLoader(test_dataset,
              shuffle=False,
              batch_size=batch_size)

class Net(torch.nn.Module):
  def __init__(self):
    super(Net, self).__init__()
    self.fc1 = torch.nn.Linear(784, 512)
    self.fc2 = torch.nn.Linear(512, 256)
    self.fc3 = torch.nn.Linear(256, 128)
    self.fc4 = torch.nn.Linear(128, 64)
    self.fc5 = torch.nn.Linear(64, 10)

  def forward(self, x):
    x = x.reshape(-1,784)
    x = F.relu(self.fc1(x))
    x = F.relu(self.fc2(x))
    x = F.relu(self.fc3(x))
    x = F.relu(self.fc4(x))
    x = self.fc5(x)
    return x

model = Net()
print(model, '\n')

criterion=torch.nn.CrossEntropyLoss()
optimizer=optim.SGD(model.parameters(),lr=0.01,momentum=0.5)

loss_val = []

def train(epoch):
  running_loss = 0.0
  for i, data in enumerate(train_loader, 0):
    inputs, target = data
    optimizer.zero_grad()
    outputs = model(inputs)
    # print('inputs = ', inputs.shape)
    # print('outputs = ', outputs.shape)
    # print('target = ', target.shape)
    loss = criterion(outputs, target)
    loss.backward()
    optimizer.step()

    running_loss += loss.item()

    if i%300 == 299:
      print('[%d,%5d]loss:%.3f'%(epoch+1,i+1,running_loss/300))
      loss_val.append(running_loss)
      running_loss = 0.0

def test():
  correct = 0
  total = 0
  with torch.no_grad():
    for data in test_loader:
      images, labels = data
      outputs = model(images)
      _, predicted = torch.max(outputs.data, dim = 1) #在outputs中找到最高概率的index赋值给predicted
      total += labels.size(0) #batch_size++ 也就是样本总数
      correct += (predicted == labels).sum().item()
  print('Accuracy on test set: %d %%' % (100 * correct / total))


for epoch in range(10):
  train(epoch)
  test()

plt.plot(np.squeeze(loss_val))
plt.ylabel('loss')
plt.xlabel('Iteration')
plt.show()