model1.py

import os
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import transforms, datasets
from torch.utils.data import DataLoader

# Define data transformations
data_transforms = transforms.Compose([
    transforms.Resize((224, 224)),  # Resize images to 224x224
    transforms.ToTensor(),           # Convert images to tensors
    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])  # Normalize pixel values
])

# Load the dataset with custom dataset class
class CustomDataset(datasets.ImageFolder):
    def __getitem__(self, index):
        path, _ = self.samples[index]
        denomination = os.path.basename(os.path.dirname(path))
        return super().__getitem__(index), denomination

train_data = CustomDataset(root='C:\\Users\\rjsli\\Desktop\\hackathon\\Train', transform=data_transforms)
train_loader = DataLoader(train_data, batch_size=32, shuffle=True)

# Define your neural network architecture
class SimpleClassifier(nn.Module):
    def __init__(self, num_classes):
        super(SimpleClassifier, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
        )
        self.classifier = nn.Sequential(
            nn.Linear(64 * 112 * 112, num_classes),
        )

    def forward(self, x):
        x = self.features(x)
        x = torch.flatten(x, 1)
        x = self.classifier(x)
        return x

# Instantiate the model
num_classes = len(train_data.classes)
model = SimpleClassifier(num_classes)

# Define loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# Training loop
num_epochs = 6
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

for epoch in range(num_epochs):
    model.train()  # Set model to training mode
    running_loss = 0.0
    for (inputs, labels), denomination in train_loader:
        inputs, labels = inputs.to(device), labels.to(device)
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item() * inputs.size(0)
    epoch_loss = running_loss / len(train_data)
    print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {epoch_loss:.4f}')

# Once training is complete, you can save the model for later use
torch.save(model.state_dict(), 'model.pth')