Pytorch学习笔记——AlexNet模型

1.代码

import time
import torch
from torch import nn,optim
import torchvision

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

class AlexNet(nn.Module):
    def __init__(self):
        super(AlexNet,self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(1,96,11,4),
            nn.ReLU(),
            nn.MaxPool2d(3,2),
            nn.Conv2d(96,256,5,1,2),
            nn.ReLU(),
            nn.MaxPool2d(3,2),
            nn.Conv2d(256,384,3,1,1),
            nn.ReLU(),
            nn.Conv2d(384,384,3,1,1),
            nn.ReLU(),
            nn.Conv2d(384,256,3,1,1),
            nn.ReLU(),
            nn.MaxPool2d(3,2)
        )
        self.fc = nn.Sequential(
            nn.Linear(256*5*5,4096),
            nn.ReLU(),
            nn.Dropout(0.5),
            nn.Linear(4096,4096),
            nn.ReLU(),
            nn.Dropout(0.5),
            nn.Linear(4096,10),
        )

    def forward(self,img):
        feature = self.conv(img)
        output = self.fc(feature.view(img.shape[0],-1))
        return output

net = AlexNet()

def load_data_fashion_mnist(batch_size,resize=None,root='~/Datasets/FashionMNIST'):
    trans = []
    if resize:
        trans.append(torchvision.transforms.Resize(size=resize))
    trans.append(torchvision.transforms.ToTensor())

    transform = torchvision.transforms.Compose(trans)
    mnist_train = torchvision.datasets.FashionMNIST(root=root,train=True,download=True,transform=transform)
    mnist_test = torchvision.datasets.FashionMNIST(root=root,train=False,download=True,transform=transform)

    train_iter = torch.utils.data.DataLoader(mnist_train,batch_size=batch_size,shuffle=True,num_workers=4)
    test_iter = torch.utils.data.DataLoader(mnist_test,batch_size=batch_size,shuffle=False,num_workers=4)

    return train_iter,test_iter

batch_size = 128

train_iter,test_iter = load_data_fashion_mnist(batch_size,resize=224)

def evaluate_accuracy(data_iter,net,device=torch.device('cuda' if torch.cuda.is_available() else 'cpu')):
    acc_sum,n = 0.0,0
    with torch.no_grad():
        for X,y in data_iter:
            if isinstance(net,torch.nn.Module):
                net.eval()
                acc_sum += (net(X.to(device)).argmax(dim=1) == y.to(device)).float().sum().cpu().item()
                net.train()
            else:
                if('is_training' in net.__code__.co_varnames):
                    acc_sum += (net(X,is_training=False).argmax(dim=1) == y).float().sum().item()
                else:
                    acc_sum += (net(X).argmax(dim=1) == y).float().sum().item()
            n += y.shape[0]
    return acc_sum/n

def train_ch5(net,train_iter,test_iter,batch_size,optimizer,device,num_epochs):
    net = net.to(device)
    print("training on ",device)
    loss = torch.nn.CrossEntropyLoss()
    batch_count = 0
    for epoch in range(num_epochs):
        train_l_sum,train_acc_sum,n,start = 0.0,0.0,0,time.time()
        for X,y in train_iter:
            X = X.to(device)
            y = y.to(device)
            y_hat = net(X)
            l = loss(y_hat,y)
            optimizer.zero_grad()
            l.backward()
            optimizer.step()
            train_l_sum += l.cpu().item()
            train_acc_sum += (y_hat.argmax(dim=1) == y).sum().cpu().item()
            n += y.shape[0]
            batch_count += 1
        test_acc = evaluate_accuracy(test_iter,net)
        print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f, time %.1f sec' %(epoch+1,train_l_sum/batch_count,train_acc_sum/n,test_acc,time.time()-start))

lr,num_epochs = 0.001,5
optimizer = torch.optim.Adam(net.parameters(),lr=lr)
train_ch5(net,train_iter,test_iter,batch_size,optimizer,device,num_epochs)

2.结果

最后

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