def visualize_model(model, num_images=9):
    was_training = model.training
    model.eval()
    images_handeled = 0
    fig = plt.figure(figsize=(4, 4))  # Figür boyutunu ayarlayın

    with torch.no_grad():
        for i, (inputs, labels) in enumerate(testloader):
            inputs = inputs.to(device)
            labels = labels.to(device)

            outputs = model(inputs)
            _, preds = torch.max(outputs, 1)

            for j in range(inputs.size()[0]):
                images_handeled += 1
                ax = plt.subplot(3, 3, images_handeled)  # 3x3'lük bir gridde subplot oluşturun
                ax.axis('off')
                ax.set_title('Actual: {} Predicted: {}'.format(class_names[labels[j].item()],class_names[preds[j]]))
                imshow(inputs.cpu().data[j])

                if images_handeled == num_images:
                    model.train(mode=was_training)
                    return
        model.train(mode=was_training)

# Modelinizi ve görüntü sayısını belirtin
visualize_model(model, num_images=9)