PyTorch是一个基于Python的库，提供了一个具有灵活易用的深度学习框架，是近年来最受欢迎的深度学习框架之一。
点击此处查看线上运行效果：https://www.kesci.com/home/project/5e0038642823a10036ae9ebf
1 初识PyTorch
1.1 张量
1.导入pytorch包
import torch
2.创建一个空的5x3张量
x = torch.empty(5, 3)
print(x)
3.创建一个随机初始化的5x3张量
x = torch.rand(5, 3)
print(x)
4.创建一个5x3的0张量，类型为long
x = torch.zeros(5, 3, dtype=torch.long)
print(x)
5.直接从数组创建张量
x = torch.tensor([5.5, 3])
print(x)
6.创建一个5x3的单位张量，类型为double
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
x = torch.randn_like(x, dtype=torch.float)
print(x)
8.打印一个张量的维度
print(x.size())
9.将两个张量相加
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
print(x[:, 1])
11.将一个4x4的张量resize成一个一维张量
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
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)
print(x)
print(x.item())
1.2 Numpy的操作
14.将张量装换成numpy数组
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
a.add_(1)
print(a)
print(b)
16.从numpy数组创建张量
import numpy as npa = 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
np.add(a, 1, out=a)
print(a)
print(b)
2 自动微分
2.1 张量的自动微分
18.新建一个张量，并设置requires_grad=True
x = torch.ones(2, 2, requires_grad=True)
print(x)
19.对张量进行任意操作（y = x + 2）
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
z = y * y * 3
out = z.mean()
print(z) # z多了MulBackward
print(out) # out多了MeanBackward
2.2 梯度
21.对out进行反向传播
out.backward()
22.打印梯度d(out)/dx
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）
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
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
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())
3 神经网络
这部分会实现LeNet5，结构如下所示
经典网络
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.将特征向量扁平成列向量
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
params = list(net.parameters())
# print(params)
print(len(params))
38.打印某一层参数的形状
print(params[0].size())
39.随机输入一个向量，查看前向传播输出
input = torch.randn(1, 1, 32, 32)
out = net(input)
print(out)
40.将梯度初始化
net.zero_grad()
41.随机一个梯度进行反向传播
out.backward(torch.randn(1, 10))
3.2 损失函数
42.用自带的MSELoss()定义损失函数
criterion = nn.MSELoss()
43.随机一个真值，并用随机的输入计算损失
target = torch.randn(10) # 随机真值
target = target.view(1, -1) # 变成列向量
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
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
loss.backward()
print('conv1.bias.grad after backward')
print(net.conv1.bias.grad)
3.3 更新权重
46.定义SGD优化器算法，学习率设置为0.01
import torch.optim as optim
optimizer = optim.SGD(net.parameters(), lr=0.01)
47.使用优化器更新权重
optimizer.zero_grad()
output = net(input)
loss = criterion(output, target)
loss.backward()
# 更新权重
optimizer.step()
4 训练一个分类器
4.1 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
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
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')
4.2 建立网络
这部分沿用前面的网络
net2 = Net()
4.3 定义损失函数和优化器
49.定义交叉熵损失函数
criterion2 = nn.CrossEntropyLoss()
50.定义SGD优化器算法，学习率设置为0.001，momentum=0.9
optimizer2 = optim.SGD(net2.parameters(), lr=0.001, momentum=0.9)
4.4训练网络
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')
4.5 使用模型预测
取一些数据
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
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
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))
4.6 存取模型
58.保存训练好的模型
PATH = './cifar_net.pth'torch.save(net.state_dict(), PATH)
59.读取保存的模型
pretrained_net = torch.load(PATH)
60.加载模型
net3 = Net()net3.load_state_dict(pretrained_net)
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