generate.py

from core import simpleNN
import torch
import torch.nn.functional as F
import numpy as np
import matplotlib.pyplot as plt
import torchvision.transforms as transforms
from torchvision import datasets
def dataset_loader():
    transform = transforms.Compose([
        transforms.ToTensor(),transforms.Normalize((0.5,), (0.5,))
    ])
    train_loader = torch.utils.data.DataLoader(datasets.MNIST('./data', train=True, download=True,
                       transform=transform), batch_size=64, shuffle=True)
    data_iterator = iter(train_loader)
    images, _ = next(data_iterator) # İlk batch'in resimlerini al
    return images

def generate(model, device, images):
    model.eval()
    with torch.no_grad():
        outputs = model(images.to(device))
        _, predicted = torch.max(outputs, 1)
        labels = predicted.cpu().numpy()
        images = images.cpu().numpy()
        plt.figure(figsize=(10, 5))
        for i in range(10):
            plt.subplot(2, 5, i + 1)
            plt.imshow(images[i].reshape(28, 28), cmap='gray')
            plt.title(f'Tahmin: {labels[i]}')
            plt.axis('off')
        plt.show()
            


model_path='mnist_model.pth'
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = simpleNN().to(device)
try:
    model.load_state_dict(torch.load(model_path, map_location=device))
except FileNotFoundError:
    print(f"Model dosyası '{model_path}' bulunamadı. Lütfen önce modeli eğitin ve kaydedin.")
generate(model,device,images=dataset_loader())