52 lines
1.8 KiB
Python
52 lines
1.8 KiB
Python
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# Name: Peng Cheng
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# UIN: 674792652
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#
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# Code adapted from:
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# https://github.com/jameschengpeng/PyTorch-CNN-on-CIFAR10
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import torch
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import torchvision
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import torchvision.transforms as transforms
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import torch.nn as nn
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import torch.nn.functional as F
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transform_train = transforms.Compose([
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transforms.RandomCrop(32, padding=4),
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transforms.RandomHorizontalFlip(),
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transforms.ToTensor(),
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transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
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])
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transform_test = transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
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])
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class ConvNet(nn.Module):
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def __init__(self):
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super(ConvNet, self).__init__()
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self.conv1 = nn.Conv2d(in_channels=3, out_channels=48, kernel_size=(3,3), padding=(1,1))
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self.conv2 = nn.Conv2d(in_channels=48, out_channels=96, kernel_size=(3,3), padding=(1,1))
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self.conv3 = nn.Conv2d(in_channels=96, out_channels=192, kernel_size=(3,3), padding=(1,1))
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self.conv4 = nn.Conv2d(in_channels=192, out_channels=256, kernel_size=(3,3), padding=(1,1))
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self.pool = nn.MaxPool2d(2,2)
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self.fc1 = nn.Linear(in_features=8*8*256, out_features=512)
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self.fc2 = nn.Linear(in_features=512, out_features=64)
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self.Dropout = nn.Dropout(0.25)
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self.fc3 = nn.Linear(in_features=64, out_features=10)
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def forward(self, x):
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x = F.relu(self.conv1(x)) #32*32*48
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x = F.relu(self.conv2(x)) #32*32*96
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x = self.pool(x) #16*16*96
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x = self.Dropout(x)
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x = F.relu(self.conv3(x)) #16*16*192
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x = F.relu(self.conv4(x)) #16*16*256
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x = self.pool(x) # 8*8*256
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x = self.Dropout(x)
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x = x.view(-1, 8*8*256) # reshape x
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x = F.relu(self.fc1(x))
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x = F.relu(self.fc2(x))
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x = self.Dropout(x)
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x = self.fc3(x)
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return x
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