Add image dataset loading and visualization

PythonApplication2.py

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+import torch
+from torchvision import transforms, datasets
+from torch.utils.data import DataLoader
+import matplotlib.pyplot as plt
+
+# Define transformations
+transform = transforms.Compose([
+ transforms.Resize((28, 28)), # Resize images to 28x28
+ transforms.ToTensor(), # Convert images to PyTorch tensors
+ transforms.Normalize((0.5,), (0.5,)) # Normalize images
+])
+
+# Load the dataset
+train_dataset = datasets.ImageFolder(root='data/train', transform=transform)
+test_dataset = datasets.ImageFolder(root='data/test', transform=transform)
+
+# Create data loaders
+train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
+test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)
+
+# Display some images from the dataset
+def show_images(loader):
+ data_iter = iter(loader)
+ images, labels = data_iter.next()
+ fig, axes = plt.subplots(1, 6, figsize=(12, 2))
+ for i in range(6):
+ ax = axes[i]
+ img = images[i].numpy().transpose((1, 2, 0))
+ img = (img * 0.5) + 0.5 # Unnormalize
+ ax.imshow(img)
+ ax.axis('off')
+ plt.show()
+
+show_images(train_loader)
+
+# Now you can use train_loader and test_loader in your training and evaluation loops