《PyTorch深度学习实践9》——卷积神经网络-高级篇（Advanced-Convolution Neural Network）

一、 $1 * 1$ 卷积

       由下面两张图，可以看出 $1 * 1$ 卷积可以显著降低计算量。
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       通常 $1 * 1$ 卷积还有以下功能：
       一是用于信息聚合，同时增加非线性， $1 * 1$ 卷积可以看作是对所有通道的信息进行线性加权，即信息聚合，同时，在卷积之后可以使用非线性激活，可以一定程度地增加模型的表达能力；二是用于通道数的变换，可以增加或者减少输出特征图的通道数。

二、Inception Module

Inception V1首次使用了并行的结构。每个Inception块使用多个大小不同的卷积核，与传统的堆叠式网络每层只用一个尺寸的卷积核的结构完全不同。
Inception块的多个不同的卷积核可以提取到不同类型的特征，同时，每个卷积核的感受野也不一样，因此可以获得多尺度的特征，最后再将这些特征拼接起来。同时，为了降低计算成本，可以使用 $1 * 1$ 卷积进行降维。
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import torch
import torch.nn as nn
from torchvision import transforms
from torchvision import datasets
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch.optim as optim

# prepare dataset
batch_size = 64
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])  # 归一化,均值和方差

train_dataset = datasets.MNIST(root='', train=True, download=True, transform=transform)
train_loader = DataLoader(train_dataset, shuffle=True, batch_size=batch_size)
test_dataset = datasets.MNIST(root='', train=False, download=True, transform=transform)
test_loader = DataLoader(test_dataset, shuffle=False, batch_size=batch_size)


# design model using class
class InceptionA(nn.Module):
    def __init__(self, in_channels):
        super(InceptionA, self).__init__()
        self.branch1x1 = nn.Conv2d(in_channels, 16, kernel_size=1)

        self.branch5x5_1 = nn.Conv2d(in_channels, 16, kernel_size=1)
        self.branch5x5_2 = nn.Conv2d(16, 24, kernel_size=5, padding=2)

        self.branch3x3_1 = nn.Conv2d(in_channels, 16, kernel_size=1)
        self.branch3x3_2 = nn.Conv2d(16, 24, kernel_size=3, padding=1)
        self.branch3x3_3 = nn.Conv2d(24, 24, kernel_size=3, padding=1)

        self.branch_pool = nn.Conv2d(in_channels, 24, kernel_size=1)

    def forward(self, x):
        branch1x1 = self.branch1x1(x)

        branch5x5 = self.branch5x5_1(x)
        branch5x5 = self.branch5x5_2(branch5x5)

        branch3x3 = self.branch3x3_1(x)
        branch3x3 = self.branch3x3_2(branch3x3)
        branch3x3 = self.branch3x3_3(branch3x3)

        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
        branch_pool = self.branch_pool(branch_pool)

        outputs = [branch1x1, branch5x5, branch3x3, branch_pool]
        return torch.cat(outputs, dim=1)  #沿着channel拼接。 b,c,w,h  c对应的是dim=1

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(88, 20, kernel_size=5)  # 88 = 24x3 + 16

        self.incep1 = InceptionA(in_channels=10)  # 与conv1 中的10对应
        self.incep2 = InceptionA(in_channels=20)  # 与conv2 中的20对应

        self.mp = nn.MaxPool2d(2)
        self.fc = nn.Linear(1408, 10)

    def forward(self, x):
        in_size = x.size(0)
        x = F.relu(self.mp(self.conv1(x)))
        x = self.incep1(x)
        x = F.relu(self.mp(self.conv2(x)))
        x = self.incep2(x)
        x = x.view(in_size, -1)
        x = self.fc(x)

        return x

model = Net()

# construct loss and optimizer
criterion = torch.nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)

# training cycle forward, backward, update
def train(epoch):
    running_loss = 0.0
    for batch_idx, data in enumerate(train_loader, 0):
        inputs, target = data
        optimizer.zero_grad()

        outputs = model(inputs)
        loss = criterion(outputs, target)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if batch_idx % 300 == 299:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, batch_idx + 1, running_loss / 300))
            running_loss = 0.0

def test():
    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_loader:
            images, labels = data
            outputs = model(images)
            _, predicted = torch.max(outputs.data, dim=1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    print('accuracy on test set: %.2f %% ' % (100 * correct / total))

if __name__ == '__main__':
    for epoch in range(10):
        train(epoch)
        test()

[1,   300] loss: 0.797
[1,   600] loss: 0.168
[1,   900] loss: 0.130
accuracy on test set: 97.21 % 
[2,   300] loss: 0.106
[2,   600] loss: 0.096
[2,   900] loss: 0.088
accuracy on test set: 97.55 % 
[3,   300] loss: 0.080
[3,   600] loss: 0.071
[3,   900] loss: 0.068
accuracy on test set: 98.22 % 
[4,   300] loss: 0.058
[4,   600] loss: 0.059
[4,   900] loss: 0.061
accuracy on test set: 98.34 % 
[5,   300] loss: 0.051
[5,   600] loss: 0.057
[5,   900] loss: 0.048
accuracy on test set: 98.51 % 
[6,   300] loss: 0.048
[6,   600] loss: 0.043
[6,   900] loss: 0.047
accuracy on test set: 98.92 % 
[7,   300] loss: 0.040
[7,   600] loss: 0.044
[7,   900] loss: 0.038
accuracy on test set: 98.81 % 
[8,   300] loss: 0.034
[8,   600] loss: 0.041
[8,   900] loss: 0.037
accuracy on test set: 98.76 % 
[9,   300] loss: 0.032
[9,   600] loss: 0.035
[9,   900] loss: 0.034
accuracy on test set: 98.82 % 
[10,   300] loss: 0.031
[10,   600] loss: 0.033
[10,   900] loss: 0.031
accuracy on test set: 98.96 %

三、Simple Residual Network

残差网络从一定程度上解决了模型退化问题(由于优化困难而导致，随着网络的加深，训练集的准确率反而下降了)，它在一个块的输入和输出之间引入一条直接的通路，称为跳跃连接。
跳跃连接的引入使得信息的流通更加顺畅：一是在前向传播时，将输入与输出的信息进行融合，能够更有效的利用特征；二是在反向传播时，总有一部分梯度通过跳跃连接反传到输入上，这缓解了梯度消失的问题。
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import torch
import torch.nn as nn
from torchvision import transforms
from torchvision import datasets
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch.optim as optim

# prepare dataset
batch_size = 64
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])  # 归一化,均值和方差

train_dataset = datasets.MNIST(root='', train=True, download=True, transform=transform)
train_loader = DataLoader(train_dataset, shuffle=True, batch_size=batch_size)
test_dataset = datasets.MNIST(root='', train=False, download=True, transform=transform)
test_loader = DataLoader(test_dataset, shuffle=False, batch_size=batch_size)

# design model using class
class ResidualBlock(nn.Module):
    def __init__(self, channels):
        super(ResidualBlock, self).__init__()
        self.channels = channels
        self.conv1 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)

    def forward(self, x):
        y = F.relu(self.conv1(x))
        y = self.conv2(y)
        return F.relu(x + y)

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 16, kernel_size=5)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=5)  # 88 = 24x3 + 16

        self.rblock1 = ResidualBlock(16)
        self.rblock2 = ResidualBlock(32)

        self.mp = nn.MaxPool2d(2)
        self.fc = nn.Linear(512, 10)

    def forward(self, x):
        in_size = x.size(0)

        x = self.mp(F.relu(self.conv1(x)))
        x = self.rblock1(x)
        x = self.mp(F.relu(self.conv2(x)))
        x = self.rblock2(x)

        x = x.view(in_size, -1)
        x = self.fc(x)
        return x

model = Net()

# construct loss and optimizer
criterion = torch.nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)

# training cycle forward, backward, update
def train(epoch):
    running_loss = 0.0
    for batch_idx, data in enumerate(train_loader, 0):
        inputs, target = data
        optimizer.zero_grad()

        outputs = model(inputs)
        loss = criterion(outputs, target)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if batch_idx % 300 == 299:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, batch_idx + 1, running_loss / 300))
            running_loss = 0.0

def test():
    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_loader:
            images, labels = data
            outputs = model(images)
            _, predicted = torch.max(outputs.data, dim=1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    print('accuracy on test set: %.2f %% ' % (100 * correct / total))

if __name__ == '__main__':
    for epoch in range(10):
        train(epoch)
        test()