125 lines
3.8 KiB
YAML
125 lines
3.8 KiB
YAML
# .github/ISSUE_TEMPLATE/experiment.yml
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name: Experiment
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description: Document a new ML experiment
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title: "[EXP] "
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labels: ["experiment"]
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assignees:
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- ${{github.actor}}
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body:
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- type: markdown
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attributes:
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value: |
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Use this template to document a new experiment for your thesis.
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- type: textarea
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id: hypothesis
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attributes:
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label: Hypothesis
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description: What is the hypothesis you're testing with this experiment?
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placeholder: Using a deeper network with residual connections will improve accuracy on the imbalanced dataset without increasing overfitting
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validations:
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required: true
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- type: textarea
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id: background
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attributes:
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label: Background & Motivation
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description: Background context and why this experiment is important
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placeholder: Previous experiments showed promising results but suffered from overfitting. Recent literature suggests that...
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validations:
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required: true
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- type: textarea
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id: dataset
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attributes:
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label: Dataset
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description: What data will you use for this experiment?
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placeholder: |
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- Dataset: MNIST with augmentation
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- Preprocessing: Standardization + random rotation
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- Train/Test Split: 80/20
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- Validation strategy: 5-fold cross-validation
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validations:
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required: true
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- type: textarea
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id: methodology
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attributes:
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label: Methodology
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description: How will you conduct the experiment?
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placeholder: |
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1. Implement ResNet architecture with varying depths (18, 34, 50)
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2. Train with early stopping (patience=10)
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3. Compare against baseline CNN from experiment #23
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4. Analyze learning curves and performance metrics
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validations:
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required: true
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- type: textarea
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id: parameters
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attributes:
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label: Parameters & Hyperparameters
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description: List the key parameters for this experiment
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placeholder: |
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- Learning rate: 0.001 with Adam optimizer
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- Batch size: 64
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- Epochs: Max 100 with early stopping
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- Dropout rate: 0.3
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- L2 regularization: 1e-4
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validations:
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required: true
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- type: textarea
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id: metrics
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attributes:
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label: Evaluation Metrics
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description: How will you evaluate the results?
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placeholder: |
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- Accuracy
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- F1-score (macro-averaged)
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- ROC-AUC
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- Training vs. validation loss curves
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- Inference time
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validations:
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required: true
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- type: input
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id: notebook
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attributes:
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label: Notebook Location
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description: Where will the experiment notebook be stored?
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placeholder: notebooks/experiment_resnet_comparison.ipynb
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validations:
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required: false
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- type: textarea
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id: dependencies
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attributes:
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label: Dependencies
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description: What other issues or tasks does this experiment depend on?
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placeholder: |
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- Depends on issue #42 (Data preprocessing pipeline)
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- Requires completion of issue #51 (Baseline model)
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validations:
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required: false
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- type: textarea
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id: references
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attributes:
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label: References
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description: Any papers, documentation or other materials relevant to this experiment
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placeholder: |
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- He et al. (2016). "Deep Residual Learning for Image Recognition"
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- My previous experiment #23 (baseline CNN)
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validations:
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required: false
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- type: textarea
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id: notes
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attributes:
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label: Additional Notes
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description: Any other relevant information
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placeholder: This experiment may require significant GPU resources. Expected runtime is ~3 hours on Tesla V100.
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validations:
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required: false
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