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def train_model(model,train_loader,test_loader,epochs=10):
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(),lr=0.001,momentum=0.9)
scheduler = optim.lr_scheduler.StepLR(optimizer,step_size=4,gamma=0.5)
class CNN(nn.Module):
def __init__(self):
super(CNN,self).__init__()
self.conv1 = nn.Conv2d(3,9,3,padding=1)
self.conv2 = nn.Conv2d(9,27,3,padding=1)
self.conv3 = nn.Conv2d(27,81,3,padding=1)
self.conv4 = nn.Conv2d(81,81,3,padding=1)
self.pool = nn.MaxPool2d(2,2)
self.fc1 = nn.Linear(in_features=81*2*2,out_features=10)
def forward(self,x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = self.pool(F.relu(self.conv3(x)))
x = self.pool(F.relu(self.conv4(x)))
x=torch.flatten(x,1)
x = self.fc1(x)
return x
self.conv5 = nn.Conv2d(81, 81, 3, padding=1)
self.conv6 = nn.Conv2d(81, 81, 3, padding=1)
x=F.relu(self.conv5(x))
x=F.relu(self.conv6(x))
x = F.relu(self.conv4(x))
class CNN(nn.Module):
def __init__(self):
super(CNN,self).__init__()
self.pool = nn.MaxPool2d(2, 2)
self.conv1 = nn.Conv2d(3, 16, 3, padding=1)
self.bn1 = nn.BatchNorm2d(16)
self.conv2 = nn.Conv2d(16, 32, 3, padding=1)
self.bn2 = nn.BatchNorm2d(32)
self.conv3 = nn.Conv2d(32, 64, 3, padding=1)
self.bn3 = nn.BatchNorm2d(64)
self.conv4 = nn.Conv2d(64, 128, 3, padding=1)
self.bn4 = nn.BatchNorm2d(128)
self.conv5 = nn.Conv2d(128, 128, 3, padding=1)
self.bn5 = nn.BatchNorm2d(128)
self.conv6 = nn.Conv2d(128, 128, 3, padding=1)
self.bn6 = nn.BatchNorm2d(128)
self.fc1 = nn.Linear(128 * 4 * 4, 10)
def forward(self,x):
x = self.pool(F.relu(self.bn1(self.conv1(x))))
x = self.pool(F.relu(self.bn2(self.conv2(x))))
x = self.pool(F.relu(self.bn3(self.conv3(x))))
x = F.relu(self.bn4(self.conv4(x)))
x = F.relu(self.bn5(self.conv5(x)))
x = F.relu(self.bn6(self.conv6(x)))
x = torch.flatten(x, 1)
x = self.fc1(x)
return x
train_transform = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010))
])
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010))
])
self.drop1 = nn.Dropout2d(0.1)
self.drop2 = nn.Dropout2d(0.1)
self.drop3 = nn.Dropout2d(0.1)
self.drop_fc = nn.Dropout(0.5)
x = self.pool(F.relu(self.bn1(self.conv1(x))))
x = self.drop1(x)
x = self.pool(F.relu(self.bn2(self.conv2(x))))
x = self.drop2(x)
x = self.pool(F.relu(self.bn3(self.conv3(x))))
x = self.drop3(x)
x = torch.flatten(x, 1)
x = self.drop_fc(x)
x = self.fc1(x)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride,padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1,padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion * planes,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(self.expansion * planes)
)
def forward(self, x):
x = F.relu(self.bn1(self.conv1(x)))
x = self.bn2(self.conv2(x))
x += self.shortcut(x)
x = F.relu(x)
return x
class ResNet(nn.Module):
def __init__(self, block, num_blocks, num_classes=10):
super(ResNet, self).__init__()
self.in_planes = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
self.linear = nn.Linear(512 * block.expansion, num_classes)
def _make_layer(self, block, planes, num_blocks, stride):
strides = [stride] + [1]*(num_blocks-1)
layers = []
for s in strides:
layers.append(block(self.in_planes, planes, s))
self.in_planes = planes * block.expansion
return nn.Sequential(*layers)
def forward(self, x):
x = F.relu(self.bn1(self.conv1(x)))
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = F.avg_pool2d(x, 4)
x = x.view(x.size(0), -1)
x = self.linear(x)
return x
def ResNet18():
return ResNet(BasicBlock, [2,2,2,2])
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