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import torch
from torch import nn
class BasicCNN(nn.Module):
def __init__(self, num_classes):
super().__init__()
self.features = nn.Sequential(
nn.Conv2d(1, 32, kernel_size=3, stride=1),
nn.BatchNorm2d(32),
nn.ReLU(inplace=True),
nn.Conv2d(32, 64, kernel_size=3, stride=1),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.Conv2d(64, 128, kernel_size=3, stride=1),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True)
)
self.adaptive = nn.AdaptiveAvgPool2d((3, 3))
self.fc_layers = nn.Sequential(
nn.Linear(3 * 3 * 128, 128),
nn.ReLU(inplace=True),
nn.Linear(128, 128),
nn.ReLU(inplace=True)
)
self.fc3 = nn.Linear(128, num_classes)
self._initialize_weights()
# Defining the forward pass
def forward(self, x):
x = self.features(x)
x = self.adaptive(x)
x = torch.flatten(x, 1)
x = self.fc_layers(x)
x = self.fc3(x)
return x
def _initialize_weights(self):
for layer in self.features:
if isinstance(layer, nn.Conv2d):
nn.init.kaiming_normal_(layer.weight, mode='fan_out', nonlinearity='relu')
nn.init.constant_(layer.bias, 0)
elif isinstance(layer, nn.BatchNorm2d):
nn.init.constant_(layer.weight, 1)
nn.init.constant_(layer.bias, 0)
for layer in self.fc_layers:
if isinstance(layer, nn.Linear):
nn.init.kaiming_normal_(layer.weight, mode='fan_out', nonlinearity='relu')
nn.init.constant_(layer.bias, 0)
nn.init.normal_(self.fc3.weight, 0, 0.01)
class HeatCNN(nn.Module):
def __init__(self, num_classes):
super().__init__()
self.features = nn.Sequential(
nn.Conv2d(1, 32, kernel_size=3, stride=1),
nn.BatchNorm2d(32),
nn.ReLU(inplace=True),
nn.Conv2d(32, 64, kernel_size=3, stride=1),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.Conv2d(64, 128, kernel_size=3, stride=1),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True)
)
self.adaptive = nn.AdaptiveAvgPool2d((3, 3))
self.fc_layers = nn.Sequential(
nn.Linear(3 * 3 * 128, 64),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(64, 64),
nn.ReLU(inplace=True),
nn.Dropout(),
)
self.fc3 = nn.Linear(64, num_classes)
self._initialize_weights()
# Defining the forward pass
def forward(self, x):
x = self.features(x)
x = self.adaptive(x)
x = torch.flatten(x, 1)
x = self.fc_layers(x)
x = self.fc3(x)
return x
def _initialize_weights(self):
for layer in self.features:
if isinstance(layer, nn.Conv2d):
nn.init.kaiming_normal_(layer.weight, mode='fan_out', nonlinearity='relu')
nn.init.constant_(layer.bias, 0)
elif isinstance(layer, nn.BatchNorm2d):
nn.init.constant_(layer.weight, 1)
nn.init.constant_(layer.bias, 0)
for layer in self.fc_layers:
if isinstance(layer, nn.Linear):
nn.init.kaiming_normal_(layer.weight, mode='fan_out', nonlinearity='relu')
nn.init.constant_(layer.bias, 0)
nn.init.normal_(self.fc3.weight, 0, 0.01)
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