Inception Moudel
1、卷积核超参数选择困难，自动找到卷积的最佳组合。
2、1x1卷积核，不同通道的信息融合。使用1x1卷积核虽然参数量增加了，但是能够显著的降低计算量(operations)
3、Inception Moudel由4个分支组成，要分清哪些是在Init里定义，哪些是在forward里调用。4个分支在dim=1(channels)上进行concatenate。24+16+24+24 = 88
4、GoogleNet的Inception(Pytorch实现)
代码说明：1、先使用类对Inception Moudel进行封装
2、先是1个卷积层(conv,maxpooling,relu)，然后inceptionA模块(输出的channels是24+16+24+24=88)，接下来又是一个卷积层(conv,mp,relu),然后inceptionA模块，最后一个全连接层(fc)。
3、1408这个数据可以通过x = x.view(in_size, -1)后调用x.shape得到。
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='../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) # 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) # 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: %d %% ' % (100*correct/total)) if __name__ == '__main__': for epoch in range(10): train(epoch) test()
视频中截图：
说明：1、要解决的问题：梯度消失
2、跳连接，H(x) = F(x) + x,张量维度必须一样，加完后再激活。不要做pooling，张量的维度会发生变化。
代码说明：
1、先是1个卷积层(conv,maxpooling,relu)，然后ResidualBlock模块，接下来又是一个卷积层(conv,mp,relu),然后esidualBlock模块模块，最后一个全连接层(fc)。
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='../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) # 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) # 暂时不知道1408咋能自动出来的 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: %d %% ' % (100*correct/total)) if __name__ == '__main__': for epoch in range(10): train(epoch) test()
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