深度学习之 cnn 进行 CIFAR10 分类

import torchvision as tv

import torchvision.transforms as transforms

from torchvision.transforms import ToPILImage

show = ToPILImage()

import torch as t

import torch.nn as nn

import torch.nn.functional as F

transform = transforms.Compose([

    transforms.ToTensor(),

    transforms.Normalize((0.5,0.5,0.5), (0.5, 0.5, 0.5)),

])

# 下载数据

trainset = tv.datasets.CIFAR10(root=".",train=True, download=True, transform=transform)

trainloader = t.utils.data.DataLoader(trainset, batch_size=4,shuffle=True, num_workers=2)

testset = tv.datasets.CIFAR10('.', train=False, download=True, transform=transform)

testloader = t.utils.data.DataLoader(testset, batch_size=4,shuffle=False,num_workers=2)

classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')

# 网络

class Net(nn.Module):

    def __init__(self):

        super(Net, self).__init__()

        self.conv1 = nn.Conv2d(3, 6, 5)

        self.conv2 = nn.Conv2d(6, 16, 5)

        self.fc1 = nn.Linear(16 * 5 * 5, 120)

        self.fc2 = nn.Linear(120, 84)

        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):

        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))

        x = F.max_pool2d(F.relu(self.conv2(x)), 2)

        x = x.view(x.size()[0], -1)

        x = F.relu(self.fc1(x))

        x = F.relu(self.fc2(x))

        x = self.fc3(x)

        return x

net = Net()

from torch import optim

criterion = nn.CrossEntropyLoss()

optimizer = optim.SGD(net.parameters(), lr = 0.001, momentum=0.9)

from torch.autograd import Variable

for epoch in range(2):

    running_loss = 0.0

    for i,data in enumerate(trainloader, 0):

        inputs, labels = data

        inputs, labels = Variable(inputs), Variable(labels)

        optimizer.zero_grad()

        outputs = net(inputs)

        loss = criterion(outputs, labels)

        loss.backward()

        optimizer.step()

        running_loss += loss.data[0]

        if i % 2000 == 1999:

            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 2000))

            running_loss = 0.0

    print('Finished Training')

# 测试

correct = 0

total = 0

for data in testloader:

    images, labels = data

    outputs = net(Variable(images))

#     print(outputs.data)

    _, predicted = t.max(outputs.data, 1)

    print(outputs.data,_, predicted)

    total += labels.size(0)

    correct += (predicted == labels).sum()

print('10000张测式中: %d %%' % (100 * correct / total) )
巴特西

深度学习之 cnn 进行 CIFAR10 分类

深度学习之 cnn 进行 CIFAR10 分类

最新文章

热门文章
