机器学习基础-手写数字识别

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=0
    for 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 = 0.0
        best_acc = 0
        with torch.no_grad():
            for val_data in test_loader:
                val_image,val_label = val_data
                output = 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_acc
            acc_num = 0
            train_acc = 0
            test_acc = 0
        print('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