文章目录
- Inception-v1实现
- Skip Connect实现
Inception-v1实现
Inception-v1中使用了多个11卷积核，其作用：
(1)在大小相同的感受野上叠加更多的卷积核，可以让模型学习到更加丰富的特征。传统的卷积层的输入数据只和一种尺寸的卷积核进行运算，而Inception-v1结构是Network in Network(NIN)，就是先进行一次普通的卷积运算(比如55)，经过激活函数(比如ReLU)输出之后，然后再进行一次11的卷积运算，这个后面也跟着一个激活函数。11的卷积操作可以理解为feature maps个神经元都进行了一个全连接运算。
(2)使用1*1的卷积核可以对模型进行降维，减少运算量。当一个卷积层输入了很多feature maps的时候，这个时候进行卷积运算计算量会非常大，如果先对输入进行降维操作，feature maps减少之后再进行卷积运算，运算量会大幅减少。
import torch
import torch
from torchvision import transforms
from torchvision import datasets
from torch.utils.data import DataLoader
import torch.nn.functional as F
batch_size = 64
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize ((0.1307,), (0.3081,))
])
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 InceptionA (torch.nn.Module):
def __init__(self, in_channels):
super (InceptionA, self).__init__()
self.branch1x1 = torch.nn.Conv2d (in_channels,16, kernel_size=1)
self.branch5x5_1 = torch.nn.Conv2d (in_channels, 16, kernel_size=1)
self.branch5x5_2 = torch.nn.Conv2d (16, 24, kernel_size=5, padding=2)
self.branch3x3_1 = torch.nn.Conv2d (in_channels, 16, kernel_size=1)
self.branch3x3_2 = torch.nn.Conv2d (16, 24, kernel_size=3, padding=1)
self.branch3x3_3 = torch.nn.Conv2d (24, 24, kernel_size=3, padding=1)
self.branch_pool = torch.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)
class Net (torch.nn.Module):
def __init__(self):
super (Net, self).__init__()
self.conv1 = torch.nn.Conv2d (1, 10, kernel_size = 5)
self.conv2 = torch.nn.Conv2d (88, 20, kernel_size = 5)
self.incep1 = InceptionA (in_channels=10)
self.incep2 = InceptionA (in_channels=20)
self.mp = torch.nn.MaxPool2d (2)
self.fc = torch.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()
Skip Connect实现
跳连结构解决了梯度消失的问题
import torch
import torch
from torchvision import transforms
from torchvision import datasets
from torch.utils.data import DataLoader
import torch.nn.functional as F
batch_size = 64
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize ((0.1307,), (0.3081,))
])
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 ResidualBlock (torch.nn.Module):
def __init__(self, channels):
super (ResidualBlock, self).__init__()
self.channels = channels
self.conv1 = torch.nn.Conv2d (channels, channels, kernel_size = 3, padding = 1)
self.conv2 = torch.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 (torch.nn.Module):
def __init__(self):
super (Net, self).__init__()
self.conv1 = torch.nn.Conv2d (1, 16, kernel_size = 5)
self.conv2 = torch.nn.Conv2d (16, 32, kernel_size=5)
self.rblock1 = ResidualBlock(16)
self.rblock2 = ResidualBlock(32)
self.mp = torch.nn.MaxPool2d (2)
self.fc = torch.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 = torch.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()
