初识PyTorch 张量 1.导入pytorch包
2.创建一个空的5x3张量
1 2 x = torch.empty(5, 3) print(x)
3.创建一个随机初始化的5x3张量
1 2 x = torch.rand(5, 3) print(x)
4.创建一个5x3的0张量，类型为long
1 2 x = torch.zeros(5, 3, dtype=torch.long) print(x)
5.直接从数组创建张量
1 2 x = torch.tensor([5.5, 3]) print(x)
6.创建一个5x3的单位张量，类型为double
1 2 x = torch.ones(5, 3, dtype=torch.double) print(x)
7.从已有的张量创建相同维度的新张量，并且重新定义类型为float
1 2 x = torch.randn_like(x, dtype=torch.float) print(x)
8.打印一个张量的维度
9.将两个张量相加
1 2 3 4 5 6 7 8 9 10 11 12 13 14 y = torch.rand(5, 3) print(x + y) # 方法二 # print(torch.add(x, y)) # 方法三 # result = torch.empty(5, 3) # torch.add(x, y, out=result) # print(result) # 方法四 # y.add_(x) # print(y)
10.取张量的第一列
11.将一个4x4的张量resize成一个一维张量
1 2 3 x = torch.randn(4, 4) y = x.view(16) print(x.size(),y.size())
12.将一个4x4的张量，resize成一个2x8的张量
1 2 3 4 5 6 y = x.view(2, 8) print(x.size(),y.size()) # 方法二 z = x.view(-1, 8) # 确定一个维度，-1的维度会被自动计算 print(x.size(),z.size())
13.从张量中取出数字x = torch.randn(1)
1 2 print(x) print(x.item())
Numpy的操作 14.将张量装换成numpy数组
1 2 3 4 5 a = torch.ones(5) print(a) b = a.numpy() print(b)
15.将张量+1，并观察上题中numpy数组的变化
1 2 3 a.add_(1) print(a) print(b)
16.从numpy数组创建张量
1 2 3 4 5 import numpy as np a = np.ones(5) b = torch.from_numpy(a) print(a) print(b)
17.将numpy数组+1并观察上题中张量的变化
1 2 3 np.add(a, 1, out=a) print(a) print(b)
自动微分 张量的自动微分 18.新建一个张量，并设置requires_grad=True
1 2 x = torch.ones(2, 2, requires_grad=True) print(x)
19.对张量进行任意操作（y = x + 2）
1 2 3 y = x + 2 print(y) print(y.grad_fn) # y就多了一个AddBackward
20.再对y进行任意操作
1 2 3 4 5 z = y * y * 3 out = z.mean() print(z) # z多了MulBackward print(out) # out多了MeanBackward
梯度 21.对out进行反向传播
22.打印梯度d(out)/dx
1 print(x.grad) #out=0.25*Σ3(x+2)^2
23.创建一个结果为矢量的计算过程（y=x*2^n）
1 2 3 4 5 6 7 x = torch.randn(3, requires_grad=True) y = x * 2 while y.data.norm() < 1000: y = y * 2 print(y)
24.计算v = [0.1, 1.0, 0.0001]处的梯度
1 2 3 4 v = torch.tensor([0.1, 1.0, 0.0001], dtype=torch.float) y.backward(v) print(x.grad)
25.关闭梯度的功能
1 2 3 4 5 6 7 8 9 10 11 print(x.requires_grad) print((x ** 2).requires_grad) with torch.no_grad(): print((x ** 2).requires_grad) # 方法二 # print(x.requires_grad) # y = x.detach() # print(y.requires_grad) # print(x.eq(y).all())
神经网络 这部分以LeNet5为例，
定义网络 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 import torch import torch.nn as nn import torch.nn.functional as F class Net(nn.Module): def __init__(self): super(Net, self).__init__() # 26.定义①的卷积层，输入为32x32的图像，卷积核大小5x5卷积核种类6 self.conv1 = nn.Conv2d(3, 6, 5) # 27.定义③的卷积层，输入为前一层6个特征，卷积核大小5x5，卷积核种类16 self.conv2 = nn.Conv2d(6, 16, 5) # 28.定义⑤的全链接层，输入为16*5*5，输出为120 self.fc1 = nn.Linear(16 * 5 * 5, 120) # 6*6 from image dimension # 29.定义⑥的全连接层，输入为120，输出为84 self.fc2 = nn.Linear(120, 84) # 30.定义⑥的全连接层，输入为84，输出为10 self.fc3 = nn.Linear(84, 10) def forward(self, x): # 31.完成input-S2，先卷积+relu，再2x2下采样 x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2)) # 32.完成S2-S4，先卷积+relu，再2x2下采样 x = F.max_pool2d(F.relu(self.conv2(x)), 2) #卷积核方形时，可以只写一个维度 # 33.将特征向量扁平成行向量 x = x.view(-1, 16 * 5 * 5) # 34.使用fc1+relu x = F.relu(self.fc1(x)) # 35.使用fc2+relu x = F.relu(self.fc2(x)) # 36.使用fc3 x = self.fc3(x) return x net = Net() print(net)
37.打印网络的参数
1 2 3 params = list(net.parameters()) # print(params) print(len(params))
38.打印某一层参数的形状
39.随机输入一个向量，查看前向传播输出
1 2 3 input = torch.randn(1, 1, 32, 32) out = net(input) print(out)
40.将梯度初始化net.zero_grad()
1 out.backward(torch.randn(1, 10))
损失函数 42.用自带的MSELoss()定义损失函数
1 criterion = nn.MSELoss()
43.随机一个真值，并用随机的输入计算损失
1 2 3 4 5 6 7 target = torch.randn(10) # 随机真值 target = target.view(1, -1) # 变成行向量 output = net(input) # 用随机输入计算输出 loss = criterion(output, target) # 计算损失 print(loss)
44.将梯度初始化，计算上一步中loss的反向传播
1 2 3 4 net.zero_grad() print('conv1.bias.grad before backward') print(net.conv1.bias.grad)
45.计算43中loss的反向传播
1 2 3 4 loss.backward() print('conv1.bias.grad after backward') print(net.conv1.bias.grad)
更新权重 46.定义SGD优化器算法，学习率设置为0.01
1 2 import torch.optim as optim optimizer = optim.SGD(net.parameters(), lr=0.01)
47.使用优化器更新权重
1 2 3 4 5 6 7 optimizer.zero_grad() output = net(input) loss = criterion(output, target) loss.backward() # 更新权重 optimizer.step()
训练一个分类器 读取CIFAR10数据，做标准化 48.构造一个transform，将三通道(0,1)区间的数据转换成(-1,1)的数据
1 2 3 4 5 6 import torchvision import torchvision.transforms as transforms transform = transforms.Compose( [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
读取数据集：
1 2 3 4 5 6 7 trainset = cifar(root = './input/cifar10', segmentation='train', transforms=transform) testset = cifar(root = './input/cifar10', segmentation='test', transforms=transform) trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,shuffle=True, num_workers=2) testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,shuffle=False, num_workers=2) classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
建立网络 使用前面的网络：
定义损失函数和优化器 49.定义交叉熵损失函数
1 criterion2 = nn.CrossEntropyLoss()
50.定义SGD优化器算法，学习率设置为0.001，momentum=0.9
1 optimizer2 = optim.SGD(net2.parameters(), lr=0.001, momentum=0.9)
训练网络 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 作者：一两赘肉无 链接：https://zhuanlan.zhihu.com/p/99318332 来源：知乎 著作权归作者所有。商业转载请联系作者获得授权，非商业转载请注明出处。 for epoch in range(2): running_loss = 0.0 for i, data in enumerate(trainloader, 0): # 获取X,y对 inputs, labels = data # 51.初始化梯度 optimizer2.zero_grad() # 52.前馈 outputs = net2(inputs) # 53.计算损失 loss = criterion2(outputs, labels) # 54.计算梯度 loss.backward() # 55.更新权值 optimizer2.step() # 每2000个数据打印平均代价函数值 running_loss += loss.item() if i % 2000 == 1999: # print every 2000 mini-batches print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 2000)) running_loss = 0.0 print('Finished Training')
使用模型预测 读一些数据
1 2 3 4 5 6 dataiter = iter(testloader) images, labels = dataiter.next() # print images imshow(torchvision.utils.make_grid(images)) print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))
56.使用模型预测
1 2 3 4 5 6 outputs = net2(images) _, predicted = torch.max(outputs, 1) print('Predicted: ', ' '.join('%5s' % classes[predicted[j]] for j in range(4)))
57.在测试集上进行打分
1 2 3 4 5 6 7 8 9 10 11 12 correct = 0 total = 0 with torch.no_grad(): for data in testloader: images, labels = data outputs = net2(images) _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() print('Accuracy of the network on the 10000 test images: %d %%' % ( 100 * correct / total))
存取模型 58.保存训练好的模型
1 2 PATH = './cifar_net.pth' torch.save(net.state_dict(), PATH)
59.读取保存的模型
1 pretrained_net = torch.load(PATH)
60.加载模型
1 2 3 net3 = Net() net3.load_state_dict(pretrained_net)
