Keras-ResNet

.. image:: https://travis-ci.org/broadinstitute/keras-resnet.svg?branch=master :target: https://travis-ci.org/broadinstitute/keras-resnet

Keras-ResNet is the Keras package for deep residual networks. It's fast and flexible.

A tantalizing preview of Keras-ResNet simplicity:

.. code-block:: python

>>> import keras

>>> import keras_resnet.models

>>> shape, classes = (32, 32, 3), 10

>>> x = keras.layers.Input(shape)

>>> model = keras_resnet.models.ResNet50(x, classes=classes)

>>> model.compile("adam", "categorical_crossentropy", ["accuracy"])

>>> (training_x, training_y), (_, _) = keras.datasets.cifar10.load_data()

>>> training_y = keras.utils.np_utils.to_categorical(training_y)

>>> model.fit(training_x, training_y)

Installation

Installation couldn’t be easier:

.. code-block:: bash

$ pip install keras-resnet

Contributing

#. Check for open issues or open a fresh issue to start a discussion around a feature idea or a bug. There is a Contributor Friendly_ tag for issues that should be ideal for people who are not very familiar with the codebase yet. #. Fork the repository_ on GitHub to start making your changes to the master branch (or branch off of it). #. Write a test which shows that the bug was fixed or that the feature works as expected. #. Send a pull request and bug the maintainer until it gets merged and published. :) Make sure to add yourself to AUTHORS_.

.. _the repository: http://github.com/0x00b1/keras-resnet .. _AUTHORS: https://github.com/0x00b1/keras-resnet/blob/master/AUTHORS.rst .. _Contributor Friendly: https://github.com/0x00b1/keras-resnet/issues?direction=desc&labels=Contributor+Friendly&page=1&sort=updated&state=open