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keras-resnet is the Keras package for deep residual networks. It’s fast and flexible.

A tantalizing preview of keras-resnet simplicity:

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

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

>>> y = keras_resnet.ResNet50(x)

>>> y = keras.layers.Flatten()(y.output)

>>> y = keras.layers.Dense(classes, activation="softmax")(y)

>>> model = keras.models.Model(x, y)

>>> 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)

>>>, training_y)


Installation couldn’t be easier:

$ pip install keras-resnet


  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.

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