1. 手写数字识别，计算机视觉领域的Hello World
2. 利用MNIST数据集，55000训练集，5000验证集。
3. Pytorch实现神经网络手写数字识别
4. 感知机与神经元、权重和偏置、神经网络、输入层、隐藏层、输出层
5. mac gpu的使用
6. 本节就是对Pytorch可以做的事情有个直观的理解，先理解表面，把大概知识打通，然后再研究细节的东西

import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim

# Check that MPS is available
if not torch.backends.mps.is_available():if not torch.backends.mps.is_built():print("MPS not available because the current PyTorch install was not ""built with MPS enabled.")else:print("MPS not available because the current MacOS version is not 12.3+ ""and/or you do not have an MPS-enabled device on this machine.")
else:device = torch.device("mps")

class Net(nn.Module):def __init__(self):super().__init__()# 28*28 = 784为输入，100为输出self.fcl = nn.Linear(784,100)self.fc2 = nn.Linear(100,10)def forward(self,x):x = torch.flatten(x,start_dim = 1)x = torch.relu(self.fcl(x))x = self.fc2(x)return x

# 当前模型对数据集学几次
max_epochs = 5
# 每次训练模型对多少张图片进行训练
batch_size = 16# data
# ToTensor 把当前数据类型转换为 Tensor
# Compose是组合多个转换操作的类
transform = transforms.Compose([transforms.ToTensor()])# 55000
trainset = torchvision.datasets.MNIST(root='./data',train=True,download=True,transform=transform)
train_loader = torch.utils.data.DataLoader(trainset,batch_size=batch_size,shuffle=True)
testset = torchvision.datasets.MNIST(root='./data',train=False,download=True,transform=transform)
test_loader = torch.utils.data.DataLoader(testset,batch_size=batch_size,shuffle=True)

# net init
net = Net()
net.to(device)# nn.MSE
loss = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(),lr=0.0001)def train():acc_num=0for epoch in range(max_epochs):for i,(data,label) in enumerate(train_loader):data = data.to(device)label = label.to(device)optimizer.zero_grad()output = net(data)Loss = loss(output,label)Loss.backward()optimizer.step()pred_class = torch.max(output,dim=1)[1]acc_num += torch.eq(pred_class,label.to(device)).sum().item()train_acc = acc_num / len(trainset)net.eval()acc_num = 0.0best_acc = 0with torch.no_grad():for val_data in test_loader:val_image,val_label = val_dataoutput = net(val_image.to(device))predict_y = torch.max(output , dim=1)[1]acc_num += torch.eq(predict_y,val_label.to(device)).sum().item()val_acc = acc_num/len(testset)print(train_acc,val_acc)if val_acc > best_acc:torch.save(net.state_dict(),'./minst.pth')best_acc = val_accacc_num = 0train_acc = 0test_acc = 0print('done')train()

0.1348 0.3007
done
0.4361 0.5548
done
0.5870666666666666 0.6335
done
0.6435333333333333 0.672
done
0.67915 0.7011
done