Cookiecutter structured version of exercises from day 1.
src/make_dataset.py downloads and transforms the MNIST dataset. The files are saved to data/processes while the raw
data has been manually moved to data/raw from the data/processed/MNIST folder.
The model is defined in the model.py file.
src/models/main.py acts as a driver script to both train and evaluate the model. This is done by adding the keywords train or evaluate. E.g.: python main.py train or python main.py evaluate.
When training, the flag --lr= can be specified to define the learning rate (default 3e-4). --num_epochs specifies the total number of epoch for training (default 10).
Upon completion a checkpoint is saved as models/checkpoint.pth. The training learning curve per batch and per epoch is saved under reports/figures/01_final_exercise_training_losses.png.
When evaluating, the --load_model_from= flag specifies which model to load (default models/checkpoint.pth). The accuracy is printed when finished.
src/models/predict_model.py computes and predicts the labels of an either a folder of images or a pickle-file. Currently, 10 random samples are generated for demonstration purposes.
src/visualization/visualize.py computes a t-SNE embedding of the 128 first test images (easily adjustable) as extracted from the penultimate layer of the trained model. The output is saved as reports/figures/02_tSNE.png.
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
Project based on the cookiecutter data science project template. #cookiecutterdatascience