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2d802f0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 | import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
import os
# --- Improved CNN Architecture ---
class GenderCNN(nn.Module):
def __init__(self):
super(GenderCNN, self).__init__()
self.conv_layers = nn.Sequential(
# Block 1
nn.Conv2d(3, 32, kernel_size=3, padding=1),
nn.ReLU(),
nn.BatchNorm2d(32),
nn.MaxPool2d(2, 2),
# Block 2
nn.Conv2d(32, 64, kernel_size=3, padding=1),
nn.ReLU(),
nn.BatchNorm2d(64),
nn.MaxPool2d(2, 2),
# Block 3 (New Layer for better features)
nn.Conv2d(64, 128, kernel_size=3, padding=1),
nn.ReLU(),
nn.BatchNorm2d(128),
nn.MaxPool2d(2, 2)
)
self.fc_layers = nn.Sequential(
nn.Flatten(),
# Input to linear: 128 * (128/2/2/2) * (128/2/2/2) = 128 * 16 * 16
nn.Linear(128 * 16 * 16, 256),
nn.ReLU(),
nn.Dropout(0.5), # Add dropout to prevent overfitting
nn.Linear(256, 1),
nn.Sigmoid()
)
def forward(self, x):
x = self.conv_layers(x)
x = self.fc_layers(x)
return x
def main():
print("--- Starting Improved CNN Training Loop ---")
# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Using device: {device}")
# Set Paths - USER MUST ENSURE DATA IS IN THESE FOLDERS
# Defaulting to a local 'dataset' folder in the current directory
data_dir = 'dataset'
train_path = os.path.join(data_dir, 'train')
if not os.path.exists(train_path):
print(f"ERROR: Training path not found at {train_path}")
print("Please place your images in: dataset/train/Male and dataset/train/Female")
return
# Hyperparameters
epochs = 10
batch_size = 32
learning_rate = 0.001
# Transforms
transform = transforms.Compose([
transforms.Resize((128, 128)),
transforms.RandomHorizontalFlip(), # Data augmentation
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
# Data Loader
train_dataset = datasets.ImageFolder(root=train_path, transform=transform)
dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
print(f"Dataset found with classes: {train_dataset.classes}")
# Note: Ensure Male is index 1 or adapt the mapping. Usually ImageFolder sorts alphabetically.
# Female: 0, Male: 1 matches the original logic.
# Initialize Model, Loss, and Optimizer
model = GenderCNN().to(device)
criterion = nn.BCELoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
# Training Loop
model.train()
for epoch in range(epochs):
epoch_loss = 0
correct = 0
total = 0
for images, labels in dataloader:
images = images.to(device)
labels = labels.float().unsqueeze(1).to(device)
optimizer.zero_grad()
outputs = model(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
epoch_loss += loss.item()
predictions = (outputs > 0.5).float()
correct += (predictions == labels).sum().item()
total += labels.size(0)
avg_loss = epoch_loss / len(dataloader)
accuracy = (correct / total) * 100
print(f"Epoch [{epoch+1}/{epochs}] - Loss: {avg_loss:.4f} - Accuracy: {accuracy:.2f}%")
# Save Model
os.makedirs('models', exist_ok=True)
torch.save(model.state_dict(), 'models/gender_model.pth')
print("\nTraining Complete! Model saved to models/gender_model.pth")
if __name__ == '__main__':
main()
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