视频地址10.卷积神经网络（基础篇）_哔哩哔哩_bilibili

网络中全部用的线形层串行连接起来，我们叫做全连接网络。输入与输出任意两节点间都有权重，这样的线形层叫做全连接层

卷积神经网络的基本特征，先特征提取再进行分类 

 3通道的图像，与3个卷积核做内积得到3个新的3*3矩阵，并对应位置相加

import torch
import torch
from torchvision import transforms  # 处理数据的一个工具
from torchvision import datasets
from torch.utils.data import DataLoader
import torch.nn.functional as F  # 激活函数使用relu
import torch.optim as optim  # 优化器batch_size = 64
transform = transforms.Compose([transforms.ToTensor(),  # 先把图像转化为c*w*h的张量transforms.Normalize((0.1307,), (0.3081,))  # 归一化，里面分别是均值，标准差，把0~255的像素转化为0~1
])train_dataset = datasets.MNIST(root='../dataset/mnist/', train=True, download=True, transform=transform)
train_loader = DataLoader(train_dataset, shuffle=True, batch_size=batch_size)test_dataset = datasets.MNIST(root='../dataset/mnist/', train=False, download=True, transform=transform)
test_loader = DataLoader(test_dataset, shuffle=False, batch_size=batch_size)class Net(torch.nn.Module):def __init__(self):super(Net, self).__init__()self.convl = torch.nn.Conv2d(1, 10, kernel_size=5)self.conv2 = torch.nn.Conv2d(10, 20, kernel_size=5)self.pooling = torch.nn.MaxPool2d(2)self.fc = torch.nn.Linear(320, 10)def forward(self, x):# Flatten data from (n, 1, 28, 28) to (n, 784)batch_size = x.size(0)x = F.relu(self.pooling(self.conv1(x)))x = F.relu(self.pooling(self.conv2(x)))x = x.view(batch_size, -1)  # flattenx = self.fc(x)return xmodel = Net()
device = torch.device("cuda:O" if torch.cuda.is_available() else "cpu")
model.to(device)  # 将所有模块的参数和缓冲区转换为CUDA张量。
criterion = torch.nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)  # 冲量设置为0.5来优化训练过程def train(epoch):running_loss = 0.0for batch_idx, data in enumerate(train_loader, 0):inputs, target = datainputs, target = inputs.to(device), target.to(device)  # 将每一步的输入和目标发送给GPU。optimizer.zero_grad()# forward + backward + updateoutputs = model(inputs)loss = criterion(outputs, target)loss.backward()optimizer.step()running_loss += loss.item()  # 用item避免构建计算图if batch_idx % 300 == 299:print('[%d, %5d] loss: %ds. 3f' % (epoch + 1, batch_idx + 1, running_loss / 300))running_loss = 0.0def test():correct = 0total = 0with torch.no_grad():  # 不需要计算梯度for data in test_loader:images, labels = dataimages, labels = images.to(device), labels.to(device)outputs = model(images)_, predicted = torch.max(outputs.data, dim=1)  # 寻找以第一维度的每行最大值，以及最大值下标total += labels.size(0)  # 加上这个批量的总数correct += (predicted == labels).sum().item()  # 计算预测对的数量print('Accuracy on test set: %d %%' % (100 * correct / total))if __name__ == '__main__':for epoch in range(10):train(epoch)test()

 GoogLeNet的Inception模块，思路就是我们不知道多大的卷积核更好，我们就多选择几种卷积核然后concatenate进行沿通道拼接。

下面是此模型的主要代码

import torch
from torch import nn
import torch.nn.functional as Fclass InceptionA(nn.Module):def __init__(self, in_channels):super(InceptionA, self).__init__()self.branch1x1 = nn.Conv2d(in_channels, 16, kernel_size=1)self.branch5x5_1nn.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.branchlx1(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)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)self.incep1 = InceptionA(in_channels=10)self.incep2 = InceptionA(in_channels=20)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

 在卷积层数过多时，会出现梯度消失的情况，需要用残差网络来解决这个问题

class ResidualBlock(nn.Module):def __init__(self, channels):super(ResidualBlock, self).__init__()self.channels = channelsself.convl = 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) #把计算值y和原始值x相加