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publicmatt
2024-04-06 13:02:31 -07:00
commit 24a0c6196f
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{{cookiecutter.project_slug}}/{{cookiecutter.module_name}}/model/__init__.py Normal file
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{{cookiecutter.project_slug}}/{{cookiecutter.module_name}}/model/cnn.py Normal file
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from torch import nn
# the VGG11 architecture
class VGG11(nn.Module):
def __init__(self, in_channels, num_classes=1000):
super(VGG11, self).__init__()
self.in_channels = in_channels
self.num_classes = num_classes
# convolutional layers
self.conv_layers = nn.Sequential(
nn.Conv2d(self.in_channels, 64, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(64, 128, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(128, 256, kernel_size=3, padding=1),
nn.ReLU(),
nn.Conv2d(256, 256, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(256, 512, kernel_size=3, padding=1),
nn.ReLU(),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.ReLU(),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
)
# fully connected linear layers
self.linear_layers = nn.Sequential(
nn.Linear(in_features=512 * 7 * 7, out_features=4096),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(in_features=4096, out_features=4096),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(in_features=4096, out_features=self.num_classes),
)
def forward(self, x):
x = self.conv_layers(x)
# flatten to prepare for the fully connected layers
x = x.view(x.size(0), -1)
x = self.linear_layers(x)
return x
class VGG16(nn.Module):
def __init__(self, num_classes=10):
super(VGG16, self).__init__()
self.layer1 = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(),
)
self.layer2 = nn.Sequential(
nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
)
self.layer3 = nn.Sequential(
nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(),
)
self.layer4 = nn.Sequential(
nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
)
self.layer5 = nn.Sequential(
nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(),
)
self.layer6 = nn.Sequential(
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(),
)
self.layer7 = nn.Sequential(
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
)
self.layer8 = nn.Sequential(
nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(),
)
self.layer9 = nn.Sequential(
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(),
)
self.layer10 = nn.Sequential(
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
)
self.layer11 = nn.Sequential(
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(),
)
self.layer12 = nn.Sequential(
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(),
)
self.layer13 = nn.Sequential(
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
)
self.fc = nn.Sequential(
nn.Dropout(0.5), nn.Linear(7 * 7 * 512, 4096), nn.ReLU()
)
self.fc1 = nn.Sequential(nn.Dropout(0.5), nn.Linear(4096, 4096), nn.ReLU())
self.fc2 = nn.Sequential(nn.Linear(4096, num_classes))
def forward(self, x):
out = self.layer1(x)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = self.layer5(out)
out = self.layer6(out)
out = self.layer7(out)
out = self.layer8(out)
out = self.layer9(out)
out = self.layer10(out)
out = self.layer11(out)
out = self.layer12(out)
out = self.layer13(out)
out = out.reshape(out.size(0), -1)
out = self.fc(out)
out = self.fc1(out)
out = self.fc2(out)
return out

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{{cookiecutter.project_slug}}/{{cookiecutter.module_name}}/model/linear.py Normal file
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from torch import nn
class DNN(nn.Module):
def __init__(self, in_size, hidden_size, out_size):
super().__init__()
# Define the activation function and the linear functions
self.act = nn.ReLU()
self.in_linear = nn.Linear(in_size, hidden_size)
self.out_linear = nn.Linear(hidden_size, out_size)
def forward(self, x):
# Send x through first linear layer and activation function
x = self.act(self.in_linear(x))
# Return x through the out linear function
return self.out_linear(x)