deepaugment 0.7.0

Discover augmentation strategies tailored for your data

# DeepAugment

<img width="400" alt="concise_workflow" src="https://user-images.githubusercontent.com/14996155/52543808-6d47a400-2d61-11e9-8df7-8271872ba0ad.png">

[](https://opensource.org/licenses/MIT) [](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.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?

<img width="600" alt="simplified_workflow" src="https://user-images.githubusercontent.com/14996155/52587711-797a4280-2def-11e9-84f8-2368fd709ab9.png">

DeepAugment consists three main components: Controller, Augmenter, and Child model. Controller samples new augmentation policies ()

### What is an augmentation policy?

A policy describes the augmentation will be applied on a dataset. Each policy consists variables for two augmentation types, their magnitude and the portion of the data to be augmented. An example policy is as following:
<img width="400" alt="example policy" src="https://user-images.githubusercontent.com/14996155/52595719-59ed1500-2e03-11e9-9a40-a79462006924.png">

There are currently 20 types of augmentation techniques (above, right) that each aug. type variable can take. All techniques are (this list might grow in later versions):
```
AUG_TYPES = [ "crop", "gaussian-blur", "rotate", "shear", "translate-x", "translate-y", "sharpen", "emboss", "additive-gaussian-noise", "dropout", "coarse-dropout", "gamma-contrast", "brighten", "invert", "fog", "clouds", "add-to-hue-and-saturation", "coarse-salt-pepper", "horizontal-flip", "vertical-flip"]
```


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, [e-mail]