pytorch-10.卷积神经网络

示例：

import torch
in_channels, out_channels = 5,10
width , height = 100,100
kernel_size = 3
batch_size = 1
 
input = torch.randn(batch_size,in_channels,width,height)
 
conv_layer = torch.nn.Conv2d(in_channels,out_channels,kernel_size=kernel_size)
 
output = conv_layer(input)
 
print(input.shape)
print(output.shape)
print(conv_layer.weight.shape)

 

---

padding示例：

如padding=1表示输入时，图像外面再补一圈0像素点

import torch
 
input = [
    3,4,5,6,7,
    2,4,6,8,2,
    1,6,7,8,4,
    9,7,4,6,2,
    3,7,5,4,1
]
input = torch.Tensor(input).view(1,1,5,5) #1,1,5,5 batch,channel,W,H
 
conv_layer = torch.nn.Conv2d(1,1,kernel_size=3,padding=1,bias=False)  #padding=1图像外面再补一圈0像素点
 
kernel = torch.Tensor([1,2,3,4,5,6,7,8,9]).view(1,1,3,3) #1,1,3,3 output_channel, input_channel,W,H
conv_layer.weight.data = kernel.data
 
output = conv_layer(input)
print(output)

 

---

stride 步长示例：

#步长
import torch
 
input = [
    3,4,5,6,7,
    2,4,6,8,2,
    1,6,7,8,4,
    9,7,4,6,2,
    3,7,5,4,1
]
input = torch.Tensor(input).view(1,1,5,5) #1,1,5,5 batch,channel,W,H
 
conv_layer = torch.nn.Conv2d(1,1,kernel_size=3,stride=2,bias=False)  #padding=1图像外面再补一圈0像素点
 
kernel = torch.Tensor([1,2,3,4,5,6,7,8,9]).view(1,1,3,3) #1,1,3,3 output_channel, input_channel,W,H
conv_layer.weight.data = kernel.data
 
output = conv_layer(input)
print(output)

---

 MaxPooling

# MaxPooling
import torch
 
input = [
    3,4,5,6,
    2,4,6,8,
    1,6,7,8,
    9,7,4,6
]
 
input = torch.Tensor(input).view(1,1,4,4)
 
maxpooling_layer = torch.nn.MaxPool2d(kernel_size=2)  #kernel_size=2默认步长为2
 
output = maxpooling_layer(input)
print(output)
 
input2 = [
    3,4,5,6,7,8,
    2,4,6,8,5,6,
    1,6,7,8,3,9,
    9,7,4,6,5,5,
    6,2,8,4,7,6,
    8,7,8,3,9,8
]
 
input2 = torch.Tensor(input2).view(1,1,6,6)
 
maxpooling_layer = torch.nn.MaxPool2d(kernel_size=3) 
 
output2 = maxpooling_layer(input2)
print(output2)

---

 

 用GPU跑的代码：

import torch
from torchvision import transforms
from torchvision import datasets
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch.optim as optim
 
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(train_dataset,shuffle = False,batch_size=batch_size)
 
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(10,20,kernel_size=5)  # input_channel , output_channel
        self.pooling = torch.nn.MaxPool2d(2)
        self.fc = torch.nn.Linear(320,10)
 
    def forward(self,x):
        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)
        x = self.fc(x)
        return x   #最后一层不做激活，不进行非线性变换
 
model = Net()
device = torch.device("cuda:0"if torch.cuda.is_available() else "cpu")
model.to(device)
 
criterion = torch.nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(),lr=0.01,momentum=0.5)
 
def train(epoch):
    running_loss = 0.0
    for batch_idx,data in enumerate(train_loader,0):
        inputs, target = data
        inputs, target = inputs.to(device),target.to(device) #送到GPU
        optimizer.zero_grad()
 
        #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
 
def test():
    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_loader:
            images,labels = data
            images, labels = images.to(device), labels.to(device)  # 送到GPU
            outputs = model(images)
            _,predicted = torch.max(outputs.data,dim=1)  #dim=1维度1，行是第0个维度，列是第1个维度
            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()

结果：

[1,  300]loss:0.568
[1,  600]loss:0.186
[1,  900]loss:0.138
Accuracy on test set:96 %
[2,  300]loss:0.108
[2,  600]loss:0.101
[2,  900]loss:0.091
Accuracy on test set:97 %
[3,  300]loss:0.076
[3,  600]loss:0.077
[3,  900]loss:0.073
Accuracy on test set:97 %
[4,  300]loss:0.065
[4,  600]loss:0.061
[4,  900]loss:0.062
Accuracy on test set:98 %
[5,  300]loss:0.056
[5,  600]loss:0.055
[5,  900]loss:0.052
Accuracy on test set:98 %
[6,  300]loss:0.049
[6,  600]loss:0.053
[6,  900]loss:0.047
Accuracy on test set:98 %
[7,  300]loss:0.046
[7,  600]loss:0.044
[7,  900]loss:0.044
Accuracy on test set:98 %
[8,  300]loss:0.043
[8,  600]loss:0.041
[8,  900]loss:0.042
Accuracy on test set:98 %
[9,  300]loss:0.038
[9,  600]loss:0.037
[9,  900]loss:0.037
Accuracy on test set:99 %
[10,  300]loss:0.033
[10,  600]loss:0.039
[10,  900]loss:0.033
Accuracy on test set:99 %

Process finished with exit code 0