Created
May 6, 2026 11:56
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pytorch_binary_classification.py
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| from torchvision import datasets, transforms | |
| from torch.utils.data import DataLoader, random_split | |
| def main() -> None: | |
| mock_data = get_mock_data() | |
| train_df, test_df = split_train_test(mock_data) | |
| train_loader = DataLoader(train_df, batch_size=10, shuffle=False) | |
| test_loader = DataLoader(test_df, batch_size=10, shuffle=False) | |
| def transform(): | |
| pass | |
| def split_train_test(dataset) -> tuple[DataLoader, DataLoader]: | |
| train_df, test_df = random_split(dataset, [round(0.8*sample_size), round(0.2*sample_size)]) | |
| return train_df, test_df | |
| def get_mock_data(): | |
| sample_size = 100 | |
| mock_data = datasets.FakeData( | |
| size=sample_size, | |
| image_size=(3, 224, 224), | |
| num_classes=2, | |
| transform=transform() | |
| ) | |
| return mock_data | |
| if __name__ == "__main__": | |
| main() |
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