PyToune 0.5

A Keras-like framework and utilities for PyTorch.

# PyToune: Deep Learning framework for [PyTorch](http://pytorch.org/)

[](http://www.gnu.org/licenses/gpl-3.0)
[](https://travis-ci.org/GRAAL-Research/pytoune)

## Here is PyToune.

PyToune is a Keras-like framework for [PyTorch](https://pytorch.org/) and handles much of the boilerplating code needed to train neural networks.

Use PyToune to:
- Train models easily.
- Use callbacks to save your best model, perform early stopping and much more.

Read the documentation at [PyToune.org](https://pytoune.org).

PyToune is compatible with the __latest version of PyTorch__ and __Python >= 3.5__.

### Cite
```
@Misc{frederikParadisPyToune,
author={Paradis, Fr{\'e}d{\'e}rik and Garneau, Nicolas},
title = {{PyToune}: Keras-like framework for {PyTorch}},
year = {2018--},
url = "\url{http://pytoune.org}"
}
```


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## Getting started: few seconds to PyToune

The core data structure of PyToune is a ``Model``, a way to train your own [PyTorch](https://pytorch.org/docs/master/nn.html) neural networks.

How PyToune works is that you create your [PyTorch](https://pytorch.org/docs/master/nn.html) module (neural network) as usual but when comes the time to train it you feed it into the PyToune Model, which handles all the steps, stats and callbacks, similar to what [Keras](https://keras.io) does.

Here is a simple example:

```python
# Import the PyToune Model and define a toy dataset
from pytoune.framework import Model
import torch
import numpy as np

num_features = 20
num_classes = 5

num_train_samples = 800
train_x = np.random.randn(num_train_samples, num_features).astype('float32')
train_y = np.random.randint(num_classes, size=num_train_samples).astype('int64')

num_valid_samples = 200
valid_x = np.random.randn(num_valid_samples, num_features).astype('float32')
valid_y = np.random.randint(num_classes, size=num_valid_samples).astype('int64')

num_test_samples = 200
test_x = np.random.randn(num_test_samples, num_features).astype('float32')
test_y = np.random.randint(num_classes, size=num_test_samples).astype('int64')
```

Create yourself a [PyTorch](https://pytorch.org/docs/master/nn.html) network;

```python
pytorch_module = torch.nn.Linear(num_features, num_classes)
```

You can now use PyToune's model to train your network easily;

```python
model = Model(pytorch_module, 'sgd', 'cross_entropy', metrics=['accuracy'])
model.fit(
train_x, train_y,
validation_x=valid_x,
validation_y=valid_y,
epochs=5,
batch_size=32
)
```

This is really similar to the ``model.compile`` function as in [Keras](https://keras.io);

```python
# Keras way to compile and train
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
model.fit(train_x, train_y, epochs=5, batch_size=32)
```

You can evaluate the performances of your network using the ``evaluate`` method of PyToune's model;

```python
loss_and_metrics = model.evaluate(test_x, test_y)
```

Or only predict on new data;

```python
predictions = model.predict(test_x)
```

As you can see, PyToune is inspired a lot by the friendliness of [Keras](https://keras.io). See the PyToune documentation at [PyToune.org](https://pytoune.org) for more.


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## Installation

Before installing PyToune, you must have the latest version of [PyTorch](https://pytorch.org/) in your environment.

- **Install the stable version of PyToune:**

```sh
pip install pytoune
```

- **Install the latest version of PyToune:**

```sh
pip install -U git+https://github.com/GRAAL-Research/pytoune.git
```

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## Why this name, PyToune?

PyToune (or pitoune in Québécois) used to be wood logs that flowed through the rivers. It was an efficient way to travel large pieces of wood across the country. We hope that PyToune will make your [PyTorch](https://pytorch.org) neural networks training flow easily just like the "pitounes" used to.


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