add imbalanced classes video code and kaggle cat vs dog

ML/Pytorch/Basics/Imbalanced_classes/main.py

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+import torch
+import torchvision.datasets as datasets
+import os
+from torch.utils.data import WeightedRandomSampler, DataLoader
+import torchvision.transforms as transforms
+import torch.nn as nn
+
+# Methods for dealing with imbalanced datasets:
+# 1. Oversampling
+# 2. Class weighting
+
+def get_loader(root_dir, batch_size):
+    my_transforms = transforms.Compose(
+        [
+            transforms.Resize((224, 224)),
+            transforms.ToTensor(),
+        ]
+    )
+
+    dataset = datasets.ImageFolder(root=root_dir, transform=my_transforms)
+    class_weights = []
+    for root, subdir, files in os.walk(root_dir):
+        if len(files) > 0:
+            class_weights.append(1/len(files))
+
+    sample_weights = [0] * len(dataset)
+
+    for idx, (data, label) in enumerate(dataset):
+        class_weight = class_weights[label]
+        sample_weights[idx] = class_weight
+
+    sampler = WeightedRandomSampler(sample_weights, num_samples=
+                                    len(sample_weights), replacement=True)
+
+    loader = DataLoader(dataset, batch_size=batch_size, sampler=sampler)
+    return loader
+
+
+def main():
+    loader = get_loader(root_dir="dataset", batch_size=8)
+
+    num_retrievers = 0
+    num_elkhounds = 0
+    for epoch in range(10):
+        for data, labels in loader:
+            num_retrievers += torch.sum(labels==0)
+            num_elkhounds += torch.sum(labels==1)
+
+    print(num_retrievers)
+    print(num_elkhounds)
+
+if __name__ == "__main__":
+    main()
+