pytorchCNNCIFAR10数据集识别

作者：挖掘机销售mv | 来源：互联网 | 2023-08-25 10:08

尝试使用深层结构进行CIFAR10的识别importtorchimporttorchvisionimporttorchvision.transformsastransform

尝试使用深层结构进行CIFAR10的识别

import torch import torchvision import torchvision.transforms as transformsBATCH_SIZE &＃61; 64 EPOCHES &＃61; 50 NUM_WORKERS &＃61; 4 LEARNING_RATE &＃61; 0.005# 数据转换 transform &＃61; transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])# 加载训练数据和测试数据 trainset &＃61; torchvision.datasets.CIFAR10(root&＃61;&＃39;./data&＃39;, train&＃61;True,download&＃61;True, transform&＃61;transform) trainloader &＃61; torch.utils.data.DataLoader(trainset, batch_size&＃61;BATCH_SIZE,shuffle&＃61;True, num_workers&＃61;NUM_WORKERS)testset &＃61; torchvision.datasets.CIFAR10(root&＃61;&＃39;./data&＃39;, train&＃61;False,download&＃61;True, transform&＃61;transform) testloader &＃61; torch.utils.data.DataLoader(testset, batch_size&＃61;BATCH_SIZE,shuffle&＃61;False, num_workers&＃61;NUM_WORKERS)# 类别标签 classes &＃61; (&＃39;plane&＃39;, &＃39;car&＃39;, &＃39;bird&＃39;, &＃39;cat&＃39;,&＃39;deer&＃39;, &＃39;dog&＃39;, &＃39;frog&＃39;, &＃39;horse&＃39;, &＃39;ship&＃39;, &＃39;truck&＃39;)

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下面定义网络

import torch.nn as nn import torch.nn.functional as F# 参考https://www.jianshu.com/p/016a23bc6554 class Net(nn.Module):def __init__(self):super(Net, self).__init__()self.conv1 &＃61; nn.Conv2d(3, 16, 3, padding&＃61;1)self.conv2 &＃61; nn.Conv2d(16, 32, 3, padding&＃61;1)self.conv3 &＃61; nn.Conv2d(32, 64, 3, padding&＃61;1)self.conv4 &＃61; nn.Conv2d(64, 128, 3, padding&＃61;1)self.conv5 &＃61; nn.Conv2d(128, 256, 3, padding&＃61;1)self.MaxPool &＃61; nn.MaxPool2d(2, 2)self.AvgPool &＃61; nn.AvgPool2d(4, 4)self.fc1 &＃61; nn.Linear(256, 128)self.fc2 &＃61; nn.Linear(128, 64)self.fc3 &＃61; nn.Linear(64, 32)self.fc4 &＃61; nn.Linear(32, 10)def forward(self, x):x &＃61; F.relu(self.conv1(x)) # (3,32,32) -> (16,32,32)x &＃61; self.MaxPool(F.relu(self.conv2(x))) # (16,32,32) -> (32,16,16)x &＃61; F.relu(self.conv3(x)) # (32,16,16) -> (64,16,16)x &＃61; self.MaxPool(F.relu(self.conv4(x))) # (64,16,16) -> (128,8,8)x &＃61; self.MaxPool(F.relu(self.conv5(x))) # (128,8,8) -> (256,4,4)x &＃61; self.AvgPool(x) # (256,1,1)x &＃61; x.view(-1, 256) # (256)x &＃61; self.fc3(self.fc2(self.fc1(x))) # (32)x &＃61; self.fc4(x) # (10)return xnet &＃61; Net() if torch.cuda.is_available():net &＃61; net.cuda() print(net)

Net((conv1): Conv2d(3, 16, kernel_size&＃61;(3, 3), stride&＃61;(1, 1), padding&＃61;(1, 1))(conv2): Conv2d(16, 32, kernel_size&＃61;(3, 3), stride&＃61;(1, 1), padding&＃61;(1, 1))(conv3): Conv2d(32, 64, kernel_size&＃61;(3, 3), stride&＃61;(1, 1), padding&＃61;(1, 1))(conv4): Conv2d(64, 128, kernel_size&＃61;(3, 3), stride&＃61;(1, 1), padding&＃61;(1, 1))(conv5): Conv2d(128, 256, kernel_size&＃61;(3, 3), stride&＃61;(1, 1), padding&＃61;(1, 1))(MaxPool): MaxPool2d(kernel_size&＃61;2, stride&＃61;2, padding&＃61;0, dilation&＃61;1, ceil_mode&＃61;False)(AvgPool): AvgPool2d(kernel_size&＃61;4, stride&＃61;4, padding&＃61;0)(fc1): Linear(in_features&＃61;256, out_features&＃61;128, bias&＃61;True)(fc2): Linear(in_features&＃61;128, out_features&＃61;64, bias&＃61;True)(fc3): Linear(in_features&＃61;64, out_features&＃61;32, bias&＃61;True)(fc4): Linear(in_features&＃61;32, out_features&＃61;10, bias&＃61;True) )

我们先看看一个batch的数据

import matplotlib.pyplot as plt import numpy as npdataiter &＃61; iter(trainloader) image, label &＃61; dataiter.next() print(image.shape) print(label.shape)def imshow(img):img &＃61; img / 2 &＃43; 0.5 # 反标准化 npimg &＃61; img.numpy()plt.imshow(np.transpose(npimg, (1, 2, 0)))plt.show()imshow(torchvision.utils.make_grid(image))# 打印标签&＃xff0c;一行八个&＃xff0c;打印八行 for i in range(8):print(" ".join("%5s" % classes[label[i*8&＃43;j]] for j in range(8)))

torch.Size([64, 3, 32, 32]) torch.Size([64])

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-7dfZrbuJ-1594882315368)(output_5_1.png)]

bird dog truck cat car dog horse shipdeer ship dog bird car cat plane deerdeer deer car truck plane dog deer shipbird bird horse truck truck ship deer dog truck frog plane car bird cat car planeship horse plane truck car deer horse ship horse truck ship ship dog dog deer ship plane frog dog bird plane bird ship ship

使用交叉熵作为损失函数&＃xff0c;Adam函数为优化函数

import torch.optim as optim from torch.autograd import Variable criterion &＃61; torch.nn.CrossEntropyLoss() optimizer &＃61; torch.optim.Adam(net.parameters(), lr&＃61;LEARNING_RATE)

定义训练函数和测试函数

def train(path):losses &＃61; []acces &＃61; []test_acc &＃61; []print("------train start------")for epoch in range(1, EPOCHES&＃43;1):train_loss &＃61; 0train_acc &＃61; 0for i, data in enumerate(trainloader, 0):inputs, labels &＃61; dataif torch.cuda.is_available():inputs &＃61; Variable(inputs).cuda()labels &＃61; Variable(labels).cuda()else:inputs &＃61; Variable(inputs)labels &＃61; Variable(labels)# 前向传播optimizer.zero_grad()outputs &＃61; net(inputs)loss &＃61; criterion(outputs, labels)loss.backward()optimizer.step()# 计算损失值train_loss &＃43;&＃61; loss.item()# 计算训练的时候得到的准确率_, pred &＃61; outputs.max(1)num_correct &＃61; (pred&＃61;&＃61;labels).sum().item()acc &＃61; num_correct/BATCH_SIZEtrain_acc &＃43;&＃61; acc# 一轮训练完了&＃xff0c;添加数据losses.append(train_loss/len(trainloader))acces.append(train_acc/len(trainloader))test_acc.append(test(path))print("epoch: {}, Train Loss: {:.6f}, Train acc: {:.6f}, Test acc: {:.6f}".format(epoch, losses[epoch-1], acces[epoch-1], test_acc[epoch-1]))torch.save(net.state_dict(), path)print(&＃39;Finished Training&＃39;)return losses, acces, test_acc

def test(path):print("------test start------")test_acc &＃61; 0for i, data in enumerate(testloader, 0):inputs, labels &＃61; dataif torch.cuda.is_available():inputs &＃61; Variable(inputs).cuda()labels &＃61; Variable(labels).cuda()else:inputs &＃61; Variable(inputs)labels &＃61; Variable(labels)# 前向传播optimizer.zero_grad()outputs &＃61; net(inputs)# 计算准确率_, pred &＃61; outputs.max(1)acc &＃61; (pred&＃61;&＃61;labels).sum().item()/BATCH_SIZEtest_acc &＃43;&＃61; accprint("test acc: {:.6f}".format(test_acc/len(testloader)))return test_acc/len(testloader)

losses &＃61; [] acces &＃61; [] test_acc &＃61; [] path &＃61; "CIFAR10_Deep.pth" losses, acces, test_acc &＃61; train(path)

------train start------ ------test start------ test acc: 0.346338 epoch: 1, Train Loss: 1.823356, Train acc: 0.271439, Test acc: 0.346338 ------test start------ test acc: 0.471537 epoch: 2, Train Loss: 1.486024, Train acc: 0.434043, Test acc: 0.471537 ------test start------ test acc: 0.532245 epoch: 3, Train Loss: 1.279489, Train acc: 0.527354, Test acc: 0.532245 ------test start------ test acc: 0.568969 epoch: 4, Train Loss: 1.168447, Train acc: 0.574628, Test acc: 0.568969 ------test start------ test acc: 0.604100 epoch: 5, Train Loss: 1.096343, Train acc: 0.603201, Test acc: 0.604100 ------test start------ test acc: 0.615844 epoch: 6, Train Loss: 1.052584, Train acc: 0.624181, Test acc: 0.615844 ------test start------ test acc: 0.609375 epoch: 7, Train Loss: 1.002270, Train acc: 0.639466, Test acc: 0.609375 ------test start------ test acc: 0.627986 epoch: 8, Train Loss: 0.965407, Train acc: 0.653613, Test acc: 0.627986 ------test start------ test acc: 0.648487 epoch: 9, Train Loss: 0.935815, Train acc: 0.664762, Test acc: 0.648487 ------test start------ test acc: 0.647492 epoch: 10, Train Loss: 0.898331, Train acc: 0.679208, Test acc: 0.647492 ------test start------ test acc: 0.659136 epoch: 11, Train Loss: 0.866001, Train acc: 0.691196, Test acc: 0.659136 ------test start------ test acc: 0.667396 epoch: 12, Train Loss: 0.836056, Train acc: 0.704364, Test acc: 0.667396 ------test start------ test acc: 0.670482 epoch: 13, Train Loss: 0.810909, Train acc: 0.712036, Test acc: 0.670482 ------test start------ test acc: 0.661027 epoch: 14, Train Loss: 0.785331, Train acc: 0.721947, Test acc: 0.661027 ------test start------ test acc: 0.676254 epoch: 15, Train Loss: 0.752643, Train acc: 0.733696, Test acc: 0.676254 ------test start------ test acc: 0.667297 epoch: 16, Train Loss: 0.720532, Train acc: 0.743606, Test acc: 0.667297 ------test start------ test acc: 0.673069 epoch: 17, Train Loss: 0.702551, Train acc: 0.750959, Test acc: 0.673069 ------test start------ test acc: 0.678344 epoch: 18, Train Loss: 0.663924, Train acc: 0.763807, Test acc: 0.678344 ------test start------ test acc: 0.671477 epoch: 19, Train Loss: 0.648946, Train acc: 0.770480, Test acc: 0.671477 ------test start------ test acc: 0.671079 epoch: 20, Train Loss: 0.625400, Train acc: 0.777873, Test acc: 0.671079 ------test start------ test acc: 0.668690 epoch: 21, Train Loss: 0.591782, Train acc: 0.789502, Test acc: 0.668690 ------test start------ test acc: 0.678045 epoch: 22, Train Loss: 0.569181, Train acc: 0.796875, Test acc: 0.678045 ------test start------ test acc: 0.667496 epoch: 23, Train Loss: 0.545593, Train acc: 0.804867, Test acc: 0.667496 ------test start------ test acc: 0.648288 epoch: 24, Train Loss: 0.521233, Train acc: 0.814218, Test acc: 0.648288 ------test start------ test acc: 0.660231 epoch: 25, Train Loss: 0.500628, Train acc: 0.821032, Test acc: 0.660231 ------test start------ test acc: 0.671576 epoch: 26, Train Loss: 0.495962, Train acc: 0.823030, Test acc: 0.671576 ------test start------ test acc: 0.668193 epoch: 27, Train Loss: 0.466635, Train acc: 0.833300, Test acc: 0.668193 ------test start------ test acc: 0.646198 epoch: 28, Train Loss: 0.445843, Train acc: 0.839934, Test acc: 0.646198 ------test start------ test acc: 0.656449 epoch: 29, Train Loss: 0.434789, Train acc: 0.845269, Test acc: 0.656449 ------test start------ test acc: 0.659236 epoch: 30, Train Loss: 0.399209, Train acc: 0.859055, Test acc: 0.659236 ------test start------ test acc: 0.659236 epoch: 31, Train Loss: 0.405278, Train acc: 0.855319, Test acc: 0.659236 ------test start------ test acc: 0.664013 epoch: 32, Train Loss: 0.383631, Train acc: 0.864110, Test acc: 0.664013 ------test start------ test acc: 0.639829 epoch: 33, Train Loss: 0.368625, Train acc: 0.869266, Test acc: 0.639829 ------test start------ test acc: 0.654956 epoch: 34, Train Loss: 0.376511, Train acc: 0.865589, Test acc: 0.654956 ------test start------ test acc: 0.654260 epoch: 35, Train Loss: 0.337547, Train acc: 0.878976, Test acc: 0.654260 ------test start------ test acc: 0.651174 epoch: 36, Train Loss: 0.354066, Train acc: 0.873481, Test acc: 0.651174 ------test start------ test acc: 0.627986 epoch: 37, Train Loss: 0.321658, Train acc: 0.884531, Test acc: 0.627986 ------test start------ test acc: 0.652269 epoch: 38, Train Loss: 0.336692, Train acc: 0.881694, Test acc: 0.652269 ------test start------ test acc: 0.630573 epoch: 39, Train Loss: 0.303990, Train acc: 0.892363, Test acc: 0.630573 ------test start------ test acc: 0.653463 epoch: 40, Train Loss: 0.306856, Train acc: 0.891824, Test acc: 0.653463 ------test start------ test acc: 0.656748 epoch: 41, Train Loss: 0.302821, Train acc: 0.893143, Test acc: 0.656748 ------test start------ test acc: 0.654061 epoch: 42, Train Loss: 0.292990, Train acc: 0.896739, Test acc: 0.654061 ------test start------ test acc: 0.656250 epoch: 43, Train Loss: 0.284619, Train acc: 0.899157, Test acc: 0.656250 ------test start------ test acc: 0.653762 epoch: 44, Train Loss: 0.301842, Train acc: 0.894581, Test acc: 0.653762 ------test start------ test acc: 0.647393 epoch: 45, Train Loss: 0.249959, Train acc: 0.913183, Test acc: 0.647393 ------test start------ test acc: 0.646994 epoch: 46, Train Loss: 0.288888, Train acc: 0.899177, Test acc: 0.646994 ------test start------ test acc: 0.633459 epoch: 47, Train Loss: 0.275196, Train acc: 0.904572, Test acc: 0.633459 ------test start------ test acc: 0.650577 epoch: 48, Train Loss: 0.263865, Train acc: 0.908148, Test acc: 0.650577 ------test start------ test acc: 0.640326 epoch: 49, Train Loss: 0.264790, Train acc: 0.907809, Test acc: 0.640326 ------test start------ test acc: 0.649781 epoch: 50, Train Loss: 0.236096, Train acc: 0.917439, Test acc: 0.649781 Finished Training

x &＃61; np.arange(1, 1&＃43;EPOCHES) plt.plot(x, losses) plt.title("train losses") plt.xlabel("epoch") plt.ylabel("loss") plt.grid()

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-bhqzedda-1594882315376)(output_12_0.png)]

plt.plot(x, acces, label&＃61;"train acc") plt.plot(x, test_acc, label&＃61;"test acc") plt.title("accuracy") plt.xlabel("epoch") plt.ylabel("acc") plt.grid() plt.legend() plt.show()

在这里插入图片描述

可以发现&＃xff0c;虽然训练准确率很高&＃xff0c;但是在测试集上的准确率一直徘徊在0.65左右上升不了了
我们拿测试集中的8个数据测试一下结果

dataiter &＃61; iter(testloader) image, label &＃61; dataiter.next() print(image.shape) print(label.shape) imshow(torchvision.utils.make_grid(image[:8]))image &＃61; Variable(image).cuda() output &＃61; net(image) _,pred &＃61; output.max(1) print("lables: "&＃43;" ".join(classes[label[i]]for i in range(8))) print("prediction: "&＃43;" ".join(classes[pred[i]] for i in range(8)))

torch.Size([64, 3, 32, 32]) torch.Size([64])

在这里插入图片描述

lables: cat ship ship plane frog frog car frog prediction: dog car ship ship frog frog car bird

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这个家伙很懒，什么也没留下！

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