最简单朴素的CNN模型

  1. import torch.nn as nn
  2. import torch.nn.functional as F
  3. device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
  4. class CNNNet(nn.Module):
  5.     def __init__(self):
  6.         super(CNNNet, self).__init__()
  7.         self.conv1 = nn.Conv2d(in_channels=3,out_channels=16,kernel_size=5,stride=1)
  8.         self.pool1 = nn.MaxPool2d(kernel_size=2,stride=2)
  9.         self.conv2 = nn.Conv2d(in_channels=16,out_channels=36,kernel_size=3,stride=1)
  10.         self.pool2 = nn.MaxPool2d(kernel_size=2,stride=2)
  11.         #self.aap = nn.AdaptiveAvgPool2d(1)
  12.         self.fc1 = nn.Linear(1296,128)
  13.         self.fc2 = nn.Linear(128,10)
  14.         #self.fc3 = nn.Linear(36,10)
  15.     def forward(self,x):
  16.         x = self.pool1(F.relu(self.conv1(x)))
  17.         x = self.pool2(F.relu(self.conv2(x)))
  18.         #x = self.aap(x)
  19.         #x = x.view(x.shape[0],-1)
  20.         #x = self.fc3(x)
  21.         x = x.view(-1,36*6*6)
  22.         #print("x.shape:{}".format(x.shape))
  23.         x = F.relu(self.fc2(F.relu(self.fc1(x))))
  24.         return x
  26. model = CNNNet()
  27. model = model.to(device)
  28. print('--------------查看网络结构-----------')
  29. print(model)