pytorch深度学习-CNN Ressidual Net
[1, 300] loss: 0.730
[1, 600] loss: 0.221
[1, 900] loss: 0.148
Accuracy on test set: 96 %
[2, 300] loss: 0.124
[2, 600] loss: 0.103
[2, 900] loss: 0.093
Accuracy on test set: 97 %
[3, 300] loss: 0.084
[3, 600] loss: 0.078
[3, 900] loss: 0.074
Accuracy on test set: 97 %
[4, 300] loss: 0.069
[4, 600] loss: 0.067
[4, 900] loss: 0.060
Accuracy on test set: 98 %
[5, 300] loss: 0.056
[5, 600] loss: 0.055
[5, 900] loss: 0.057
Accuracy on test set: 98 %
[6, 300] loss: 0.051
[6, 600] loss: 0.047
[6, 900] loss: 0.050
Accuracy on test set: 98 %
[7, 300] loss: 0.044
[7, 600] loss: 0.043
[7, 900] loss: 0.044
Accuracy on test set: 98 %
[8, 300] loss: 0.042
[8, 600] loss: 0.042
[8, 900] loss: 0.040
Accuracy on test set: 98 %
[9, 300] loss: 0.037
[9, 600] loss: 0.039
[9, 900] loss: 0.036
Accuracy on test set: 98 %
[10, 300] loss: 0.035
[10, 600] loss: 0.033
[10, 900] loss: 0.036
Accuracy on test set: 98 %
Process finished with exit code 0
Ressidual Net残差网络代码
import torch
from torchvision import transforms
from torchvision import datasets
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch.nn as nn
import torch.optim as optim
#step1 准备数据集
batch_size = 64
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.137,),(0.3081,))
])
train_dataset = datasets.MNIST(root='../dataset/mnist',
train=True,
download=True,
transform=transform)
train_loder = DataLoader(train_dataset,
shuffle=True,
batch_size=batch_size)
test_dataset = datasets.MNIST(root='../dataset/mnist',
train=False,
download=True,
transform=transform)
test_loder = DataLoader(test_dataset,
shuffle=False,
batch_size=batch_size)
#step2 搭建网络
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) #先求和,后激活
class Net(nn.Module):
# convolution -> pooling -> inception
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1,16,kernel_size=5) #卷积层
self.conv2 = nn.Conv2d(16,32,kernel_size=5)
self.mp = nn.MaxPool2d(2)
self.rblock1 = ResidualBlock(16)
self.rblock2 = ResidualBlock(32)
self.fc = torch.nn.Linear(512,10) #full connecting 全连接层
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) # 88
x = x.view(in_size,-1)
x = self.fc(x)
return x
model = Net()
device = torch.device("cuda:0" if torch.cuda.is_available() else"cpu")
model.to(device)
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(),lr=0.01)
#step3 训练
def train(epoch):
running_loss = 0.0
for batch_idx,data in enumerate(train_loder,0):
inputs,target = data
inputs,target = inputs.to(device),target.to(device)
optimizer.zero_grad() #梯度清零
#forward + backward + update
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(): #不计算梯度
for data in test_loder:
inputs,target = data
inputs, target = inputs.to(device), target.to(device)
outputs = model(inputs)
_,predicted = torch.max(outputs.data,dim=1)
total += target.size(0)
correct += (predicted == target).sum().item()
print('Accuracy on test set: %d %% '%(100 * correct / total))
if __name__ == '__main__':
for epoch in range(10):
train(epoch)
test()