Cifar100#1
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…model using PyTorch, feat: Add implementation of VGG16 (not tested)
flatten fully-connected layer input add return to VGG16 forward pass add metaparameters to cifar100 vgg16 runner
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Pull Request Overview
This PR adds a machine learning project focused on CIFAR-100 classification using PyTorch, along with code formatting and configuration improvements.
- Implements a CIFAR-100 ResNet50 transfer learning solution with data augmentation, early stopping, and fine-tuning
- Standardizes code formatting across existing files using Black formatter
- Adds development environment configuration and pre-commit hooks
Reviewed Changes
Copilot reviewed 11 out of 25 changed files in this pull request and generated 2 comments.
Show a summary per file
| File | Description |
|---|---|
| CIFAR100_ResNET.ipynb | New CIFAR-100 classification notebook with ResNet50 transfer learning |
| pyproject.toml | Code quality configuration for Black and Flake8 |
| .pre-commit-config.yaml | Pre-commit hooks for code formatting and linting |
| .vscode/settings.json | VS Code Python formatting and linting settings |
| first_tries*.py, MNIST_a.py, CIFAR10_a.ipynb, tf.ipynb, torch.ipynb | Code formatting standardization (quotes, spacing, line breaks) |
| mnist_config.py | Removed empty line at beginning |
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Comments suppressed due to low confidence (1)
CIFAR100_ResNET.ipynb:1
- Line 165 is incorrect. You're setting model.fc to a boolean value instead of making the parameters trainable. Should use
model.fc.requires_grad_(True)on each parameter of the layer, or better yet, iterate through the parameters:for param in model.fc.parameters(): param.requires_grad = True
{
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| "editor.wordBasedSuggestions": "off", | ||
| "editor.formatOnType": true, | ||
| "editor.inlineSuggest.suppressSuggestions": false, | ||
| "editor.defaultFormatter": "ms-python.black-formatter", |
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Remove the trailing comma after the last property in the JSON object. Trailing commas are not valid in JSON and may cause parsing errors.
Suggested change
| "editor.defaultFormatter": "ms-python.black-formatter", | |
| "editor.defaultFormatter": "ms-python.black-formatter" |
| " n_samples = len(test_loader.dataset)\n", | ||
| " for images, labels in test_loader:\n", | ||
| " images, labels = images.to(device), labels.to(device)\n", | ||
| " outputs = model(images)\n", |
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This should use load_model instead of model since you're evaluating the loaded model, not the original training model. The line should be outputs = load_model(images)
Suggested change
| " outputs = model(images)\n", | |
| " outputs = load_model(images)\n", |
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