CNN——NiN

编程入门行业动态更新时间:2024-10-25 02:22:21

CNN——NiN

NiN块以一个普通卷积层开始，后面是两个1*1的卷积层。

import torch
import torchvision
from torch import nn
from torch.utils.data import Dataset,DataLoader
from torchvision import transforms
from d2l import torch as d2l#准备数据集
def load_data_fashion_mnist(batch_size,resize):trans = []if resize:trans.append(transforms.Resize(size=resize))trans.append(transforms.ToTensor())transform = transforms.Compose(trans)#准备数据集train_data = torchvision.datasets.FashionMNIST(root='./data', train=True, transform=transform,download=True)test_data = torchvision.datasets.FashionMNIST(root='./data', train=False, transform=transform,download=True)#加载数据集train_dataloader = DataLoader(dataset=train_data,batch_size=batch_size,shuffle=True)test_dataloader = DataLoader(dataset=test_data,batch_size=batch_size,shuffle=False)return train_dataloader,test_dataloader#搭建网络
def nin_block(in_channels,out_channels,kernel_size,strides,padding):return nn.Sequential(nn.Conv2d(in_channels,out_channels,kernel_size,strides,padding),nn.ReLU(),nn.Conv2d(out_channels,out_channels,kernel_size=1),nn.ReLU(),nn.Conv2d(out_channels,out_channels,kernel_size=1),nn.ReLU())
net = nn.Sequential(nin_block(1,96,kernel_size=11,strides=4,padding=0),nn.MaxPool2d(3,stride=2),nin_block(96,256,kernel_size=5,strides=1,padding=2),nn.MaxPool2d(3,stride=2),nin_block(256,384,kernel_size=3,strides=1,padding=1),nn.MaxPool2d(3,stride=2),nn.Dropout(0.5),nin_block(384,10,kernel_size=3,strides=1,padding=1),nn.AdaptiveAvgPool2d((1,1)),nn.Flatten()
)# Accumulator 实例中创建了 2 个变量，用于分别存储正确预测的数量和预测的总数量
class Accumulator:"""在`n`个变量上累加。"""def __init__(self, n):self.data = [0.0] * ndef add(self, *args):self.data = [a + float(b) for a, b in zip(self.data, args)]def reset(self):self.data = [0.0] * len(self.data)def __getitem__(self, idx):return self.data[idx]#GPU训练
def try_gpu(i=0):if torch.cuda.device_count() >= 1:return torch.device(f'cuda:{i}')return torch.device('cpu')#计算预测正确的数量
def accuracy(y_hat,y):if len(y_hat.shape) > 1 and y_hat.shape[1] > 1:y_hat = y_hat.argmax(axis=1)cmp = y_hat.type(y.dtype) == yreturn float(cmp.type(y.dtype).sum())#使用GPU计算模型在数据集上的精度
def evaluate_accuracy_gpu(net,data_iter,device=None):if isinstance(net,nn.Module):net.eval()   #设置为评估模式if not device:device = next(iter(net.parameters())).device#正确预测的数量，总预测的数量metric = Accumulator(2)with torch.no_grad():for X,y in data_iter:if isinstance(X,list):X = [x.to(device) for x in X]else:X = X.to(device)y = y.to(device)metric.add(accuracy(net(X),y),y.numel())return metric[0]/metric[1]def train(net, train_iter, test_iter, num_epochs, lr, device):"""用GPU训练模型(在第六章定义)。"""def init_weights(m):if type(m) == nn.Linear or type(m) == nn.Conv2d:nn.init.xavier_uniform_(m.weight)net.apply(init_weights)print('training on', device)net.to(device)optimizer = torch.optim.SGD(net.parameters(), lr=lr)loss = nn.CrossEntropyLoss()animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs],legend=['train loss', 'train acc', 'test acc'])timer, num_batches = d2l.Timer(), len(train_iter)for epoch in range(num_epochs):print("----------第{}轮开始训练------------".format(epoch+1))metric = Accumulator(3)net.train()for i, (X, y) in enumerate(train_iter):timer.start()optimizer.zero_grad()X, y = X.to(device), y.to(device)y_hat = net(X)l = loss(y_hat, y)l.backward()optimizer.step()with torch.no_grad():metric.add(l * X.shape[0], accuracy(y_hat, y), X.shape[0])timer.stop()train_l = metric[0] / metric[2]train_acc = metric[1] / metric[2]if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1:animator.add(epoch + (i + 1) / num_batches,(train_l, train_acc, None))test_acc = evaluate_accuracy_gpu(net, test_iter)animator.add(epoch + 1, (None, None, test_acc))print(f'loss {train_l:.3f}, train acc {train_acc:.3f}, 'f'test acc {test_acc:.3f}')print(f'{metric[2] * num_epochs / timer.sum():.1f} examples/sec 'f'on {str(device)}')if __name__ == '__main__':batch_size = 128lr = 0.1num_epochs = 10ratio = 4train_iter,test_iter = load_data_fashion_mnist(batch_size=batch_size,resize=224)train(net,train_iter=train_iter,test_iter=test_iter,num_epochs=num_epochs,lr=lr,device=try_gpu())d2l.plt.show()

总结：