DCA_validator.py

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

# Definícia konvolučnej neurónovej siete
class ConvNet(nn.Module):
    def __init__(self):
        super(ConvNet, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(1, 32, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2))
        self.layer2 = nn.Sequential(
            nn.Conv2d(32, 64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2))
        self.drop_out = nn.Dropout()
        self.fc1 = nn.Linear(7 * 7 * 64, 1000)
        self.fc2 = nn.Linear(1000, 2)

    def forward(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        out = out.reshape(out.size(0), -1)
        out = self.drop_out(out)
        out = self.fc1(out)
        return self.fc2(out)

# Definícia transformácií pre obrázky
transform = transforms.Compose([
    transforms.Grayscale(),
    transforms.Resize((28, 28)),
    transforms.ToTensor(),
])

# Načítanie datasetu
train_data = datasets.ImageFolder(root='train_data_directory', transform=transform)
valid_data = datasets.ImageFolder(root='valid_data_directory', transform=transform)

train_loader = DataLoader(dataset=train_data, batch_size=16, shuffle=True)
valid_loader = DataLoader(dataset=valid_data, batch_size=16, shuffle=False)

# Inicializácia modelu, stratovej funkcie a optimalizátora
model = ConvNet()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# Trénovanie modelu
num_epochs = 10

for epoch in range(num_epochs):
    for images, labels in train_loader:
        outputs = model(images)
        loss = criterion(outputs, labels)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

    # Overenie modelu na validačných dátach
    total = 0
    correct = 0
    for images, labels in valid_loader:
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
    print(f'Epoch {epoch+1}, Accuracy: {correct / total}')