Keras package for deep residual networks
Clone or download
mbroisinBI and 0x00b1 Fixed bugs (#54)
* first commit test

* keras resnet test

* fixed bugs FPNs

* fixed bugs FPNs

* FPNs BatchNorm

* FPNs BatchNorm

* FPN

* fixed bugs

* fixed bugs
Latest commit c0a9b2a Dec 18, 2018

README.rst

Keras-ResNet

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

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

A tantalizing preview of Keras-ResNet simplicity:

>>> 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:

$ pip install keras-resnet

Contributing

  1. 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.
  2. Fork the repository on GitHub to start making your changes to the master branch (or branch off of it).
  3. Write a test which shows that the bug was fixed or that the feature works as expected.
  4. Send a pull request and bug the maintainer until it gets merged and published. :) Make sure to add yourself to AUTHORS.