Metadata-Version: 1.1
Name: deepaugment
Version: 0.4.0
Summary: Discover augmentation strategies tailored for your data
Home-page: https://github.com/barisozmen/deepaugment
Author: Baris Ozmen
Author-email: hbaristr@gmail.com
License: BSD
Download-URL: https://github.com/barisozmen/deepaugment/tarball/0.4.0
Description: # DeepAugment
        
        <img width="400" alt="concise_workflow" src="https://user-images.githubusercontent.com/14996155/52543808-6d47a400-2d61-11e9-8df7-8271872ba0ad.png">
        
        [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)
        
        DeepAugment discovers best augmentation strategies tailored for your images. It optimizes augmentation hyperparameters using Bayesian Optimization, which is widely used for hyperparameter tuning. The tool:
        - boosts deep learning model accuracy 5% compared to models not using augmentation.
        - saves times by automating the process
        
        
        Resources: [slides](https://docs.google.com/presentation/d/1toRUTT9X26ACngr6DXCKmPravyqmaGjy-eIU5cTbG1A/edit#slide=id.g4cc092dbc6_0_0)
        
        ## Installation/Usage
        ```console
        $ pip install deepaugment
        ```
        
        
        Simple usage (with any dataset)
        ```Python
        from deepaugment import DeepAugment
        
        deepaug = DeepAugment(my_data, my_labels)
        
        best_policies = deepaug.optimize(300)
        ```
        
        Simple usage (with cifar-10 dataset)
        ```Python
        deepaug = DeepAugment("cifar10")
        
        best_policies = deepaug.optimize(300)
        ```
        
        
        Advanced usage (by changing configurations, and with fashion-mnist dataset)
        ```Python
        from keras.datasets import fashion_mnist
        
        # my configuration
        my_config = {
            "model": "basiccnn",
            "method": "bayesian_optimization",
            "train_set_size": 2000,
            "opt_samples": 3,
            "opt_last_n_epochs": 3,
            "opt_initial_points": 10,
            "child_epochs": 50,
            "child_first_train_epochs": 0,
            "child_batch_size": 64
        }
        
        (x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()
        # X_train.shape -> (N, M, M, 3)
        # y_train.shape -> (N)
        deepaug = DeepAugment(data=x_train, labels=y_train, config=my_config)
        
        best_policies = deepaug.optimize(300)
        ```
        
        
        ## Results
        ### CIFAR-10 best policies tested on WRN-28-10 
        - Method: Wide-ResNet-28-10 trained with CIFAR-10 augmented images by best found policies, and with unaugmented images (everything else same).
        - Result: **5.2% accuracy increase** by DeepAugment
        
        <img src="https://user-images.githubusercontent.com/14996155/52544784-e0541900-2d67-11e9-93db-0b8b192f5b37.png" width="400"> <img src="https://user-images.githubusercontent.com/14996155/52545044-63c23a00-2d69-11e9-9879-3d7bcb8f88f4.png" width="400">
         
        ## How it works?
        
        ![alt text](/reports/figures/simplified_workflow.png "Workflow")
        
        DeepAugment working method can be dissected into three areas:
        1. Search space of augmentation
        2. Optimizer
        3. Child model
        
        ### 1. Search space of augmentation
        ### 2. Optimizer
        ### 3. Child model
        <img width="800" alt="child-cnn" src="https://user-images.githubusercontent.com/14996155/52545277-10e98200-2d6b-11e9-9639-48b671711eba.png">
        
        
        ### Repo Organization
        
            ├── 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.testrun.org
        
        
        --------
        
        <p><small>Project based on the <a target="_blank" href="https://drivendata.github.io/cookiecutter-data-science/">cookiecutter data science project template</a>. #cookiecutterdatascience</small></p>
        
        ## Contact
        Baris Ozmen, hbaristr@gmail.com
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
