卷积神经网络
特性：
很多层: compositionality
卷积: locality + stationarity of images
池化: Invariance of object class to translations
1.加载数据：
input_size = 28*28 # MNIST上的图像尺寸是 28x28
output_size = 10 # 类别为 0 到 9 的数字，因此为十类
train_loader = torch.utils.data.DataLoader(
datasets.MNIST('./data', train=True, download=True,
transform=transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))])),
batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST('./data', train=False, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))])),
batch_size=1000, shuffle=True)
plt.figure(figsize=(8, 5))
for i in range(20):
plt.subplot(4, 5, i + 1)
image, _ = train_loader.dataset.__getitem__(i)
plt.imshow(image.squeeze().numpy(),'gray')
plt.axis('off');
显示部分图像
2.创建网络：
class FC2Layer(nn.Module):
def __init__(self, input_size, n_hidden, output_size):
# nn.Module子类的函数必须在构造函数中执行父类的构造函数
# 下式等价于nn.Module.__init__(self)
super(FC2Layer, self).__init__()
self.input_size = input_size
# 这里直接用 Sequential 就定义了网络，注意要和下面 CNN 的代码区分开
self.network = nn.Sequential(
nn.Linear(input_size, n_hidden),
nn.ReLU(),
nn.Linear(n_hidden, n_hidden),
nn.ReLU(),
nn.Linear(n_hidden, output_size),
nn.LogSoftmax(dim=1)
)
def forward(self, x):
# view一般出现在model类的forward函数中，用于改变输入或输出的形状
# x.view(-1, self.input_size) 的意思是多维的数据展成二维
# 代码指定二维数据的列数为 input_size=784，行数 -1 表示我们不想算，电脑会自己计算对应的数字
# 在 DataLoader 部分，我们可以看到 batch_size 是64，所以得到 x 的行数是64
# 大家可以加一行代码：print(x.cpu().numpy().shape)
# 训练过程中，就会看到 (64, 784) 的输出，和我们的预期是一致的
# forward 函数的作用是，指定网络的运行过程，这个全连接网络可能看不啥意义，
# 下面的CNN网络可以看出 forward 的作用。
x = x.view(-1, self.input_size)
return self.network(x)
class CNN(nn.Module):
def __init__(self, input_size, n_feature, output_size):
# 执行父类的构造函数，所有的网络都要这么写
super(CNN, self).__init__()
# 下面是网络里典型结构的一些定义，一般就是卷积和全连接
# 池化、ReLU一类的不用在这里定义
self.n_feature = n_feature
self.conv1 = nn.Conv2d(in_channels=1, out_channels=n_feature, kernel_size=5)
self.conv2 = nn.Conv2d(n_feature, n_feature, kernel_size=5)
self.fc1 = nn.Linear(n_feature*4*4, 50)
self.fc2 = nn.Linear(50, 10)
# 下面的 forward 函数，定义了网络的结构，按照一定顺序，把上面构建的一些结构组织起来
# 意思就是，conv1, conv2 等等的，可以多次重用
def forward(self, x, verbose=False):
x = self.conv1(x)
x = F.relu(x)
x = F.max_pool2d(x, kernel_size=2)
x = self.conv2(x)
x = F.relu(x)
x = F.max_pool2d(x, kernel_size=2)
x = x.view(-1, self.n_feature*4*4)
x = self.fc1(x)
x = F.relu(x)
x = self.fc2(x)
x = F.log_softmax(x, dim=1)
return x
训练以及测试函数：
# 训练函数
def train(model):
model.train()
# 主里从train_loader里，64个样本一个batch为单位提取样本进行训练
for batch_idx, (data, target) in enumerate(train_loader):
# 把数据送到GPU中
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
if batch_idx % 100 == 0:
print('Train: [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
def test(model):
model.eval()
test_loss = 0
correct = 0
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
test_loss += F.nll_loss(output, target, reduction='sum').item()
pred = output.data.max(1, keepdim=True)[1]
correct += pred.eq(target.data.view_as(pred)).cpu().sum().item()
test_loss /= len(test_loader.dataset)
accuracy = 100. * correct / len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
accuracy))
3.在CNN上训练
# Training settings
n_features = 6 # number of feature maps
model_cnn = CNN(input_size, n_features, output_size)
model_cnn.to(device)
optimizer = optim.SGD(model_cnn.parameters(), lr=0.01, momentum=0.5)
print('Number of parameters: {}'.format(get_n_params(model_cnn)))
train(model_cnn)
test(model_cnn)
4.在全连接网络上训练
n_hidden = 8 # number of hidden units
model_fnn = FC2Layer(input_size, n_hidden, output_size)
model_fnn.to(device)
optimizer = optim.SGD(model_fnn.parameters(), lr=0.01, momentum=0.5)
print('Number of parameters: {}'.format(get_n_params(model_fnn)))
train(model_fnn)
test(model_fnn)
Number of parameters: 6442
CIFAR数据集分类
import torch
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
# 使用GPU训练，可以在菜单 "代码执行工具" -> "更改运行时类型" 里进行设置
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64,
shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=8,
shuffle=False, num_workers=2)
classes = ('plane', 'car', 'bird', 'cat',
'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
def imshow(img):
plt.figure(figsize=(8,8))
img = img / 2 + 0.5 # 转换到 [0,1] 之间
npimg = img.numpy()
plt.imshow(np.transpose(npimg, (1, 2, 0)))
plt.show()
# 得到一组图像
images, labels = iter(trainloader).next()
# 展示图像
imshow(torchvision.utils.make_grid(images))
# 展示第一行图像的标签
for j in range(8):
print(classes[labels[j]])
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
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 = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
# 网络放到GPU上
net = Net().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)
for epoch in range(10): # 重复多轮训练
for i, (inputs, labels) in enumerate(trainloader):
inputs = inputs.to(device)
labels = labels.to(device)
# 优化器梯度归零
optimizer.zero_grad()
# 正向传播 + 反向传播 + 优化
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# 输出统计信息
if i % 100 == 0:
print('Epoch: %d Minibatch: %5d loss: %.3f' %(epoch + 1, i + 1, loss.item()))
for epoch in range(10): # 重复多轮训练
for i, (inputs, labels) in enumerate(trainloader):
inputs = inputs.to(device)
labels = labels.to(device)
# 优化器梯度归零
optimizer.zero_grad()
# 正向传播 + 反向传播 + 优化
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# 输出统计信息
if i % 100 == 0:
print('Epoch: %d Minibatch: %5d loss: %.3f' %(epoch + 1, i + 1, loss.item()))
# 得到一组图像
images, labels = iter(testloader).next()
# 展示图像
imshow(torchvision.utils.make_grid(images))
# 展示图像的标签
for j in range(8):
print(classes[labels[j]])
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