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Models.py
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Models.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
class Mnist_2NN(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(784, 200)
self.fc2 = nn.Linear(200, 200)
self.fc3 = nn.Linear(200, 10)
def forward(self, inputs):
tensor = F.relu(self.fc1(inputs))
tensor = F.relu(self.fc2(tensor))
tensor = self.fc3(tensor)
return tensor
class Mnist_CNN(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(in_channels=1, out_channels=32, kernel_size=5, stride=1, padding=2)
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=5, stride=1, padding=2)
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
self.fc1 = nn.Linear(7*7*64, 512)
self.fc2 = nn.Linear(512, 10)
def forward(self, inputs):
tensor = inputs.view(-1, 1, 28, 28)
tensor = F.relu(self.conv1(tensor))
tensor = self.pool1(tensor)
tensor = F.relu(self.conv2(tensor))
tensor = self.pool2(tensor)
tensor = tensor.view(-1, 7*7*64)
tensor = F.relu(self.fc1(tensor))
tensor = self.fc2(tensor)
return tensor
class Mnist_CNN_Simplified(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=5, stride=1, padding=2)
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
self.conv2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=5, stride=1, padding=2)
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
self.fc1 = nn.Linear(7*7*32, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, inputs):
tensor = inputs.view(-1, 1, 28, 28)
tensor = F.relu(self.conv1(tensor))
tensor = self.pool1(tensor)
tensor = F.relu(self.conv2(tensor))
tensor = self.pool2(tensor)
tensor = tensor.view(-1, 7*7*32)
tensor = F.relu(self.fc1(tensor))
tensor = self.fc2(tensor)
return tensor
class Cifar10_CNN(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
self.fc1 = nn.Linear(64 * 8 * 8, 512)
self.fc2 = nn.Linear(512, 10)
def forward(self, x):
x = x.view(-1, 3, 32, 32)
x = F.relu(self.conv1(x))
x = self.pool(x)
x = F.relu(self.conv2(x))
x = self.pool(x)
x = x.view(-1, 64 * 8 * 8) # Flatten the tensor for fully connected layers
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
class Cifar10_CNN_Simplified(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=1)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
self.fc1 = nn.Linear(32 * 8 * 8, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = x.view(-1, 3, 32, 32)
x = F.relu(self.conv1(x))
x = self.pool(x)
x = F.relu(self.conv2(x))
x = self.pool(x)
x = x.view(-1, 32 * 8 * 8) # Flatten the tensor for fully connected layers
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
class Cifar100_CNN(nn.Module):
def __init__(self):
super(Cifar100_CNN, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1)
self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
self.fc1 = nn.Linear(128 * 4 * 4, 512)
self.fc2 = nn.Linear(512, 100)
def forward(self, x):
x = F.relu(self.conv1(x))
x = self.pool(x)
x = F.relu(self.conv2(x))
x = self.pool(x)
x = F.relu(self.conv3(x))
x = self.pool(x)
x = x.view(-1, 128 * 4 * 4)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return 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):
out = F.relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
out += self.shortcut(x)
out = F.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, num_blocks, num_classes=100):
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 stride in strides:
layers.append(block(self.in_planes, planes, stride))
self.in_planes = planes * block.expansion
return nn.Sequential(*layers)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = F.avg_pool2d(out, 4)
out = out.view(out.size(0), -1)
out = self.linear(out)
return out
def Cifar100_ResNet():
return ResNet(BasicBlock, [2, 2, 2, 2], num_classes=100)