Keras: Deep Learning for humans

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

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Use Keras if you need a deep learning library that:

  • Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility).
  • Supports both convolutional networks and recurrent networks, as well as combinations of the two.
  • Runs seamlessly on CPU and GPU.

Keras is compatible with: Python 2.7-3.6.

Guiding principles

  • User friendliness. Keras is an API designed for human beings, not machines. It puts user experience front and center. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear and actionable feedback upon user error.

  • Modularity. A model is understood as a sequence or a graph of standalone, fully-configurable modules that can be plugged together with as little restrictions as possible. In particular, neural layers, cost functions, optimizers, initialization schemes, activation functions, regularization schemes are all standalone modules that you can combine to create new models.

  • Easy extensibility. New modules are simple to add (as new classes and functions), and existing modules provide ample examples. To be able to easily create new modules allows for total expressiveness, making Keras suitable for advanced research.

  • Work with Python. No separate models configuration files in a declarative format. Models are described in Python code, which is compact, easier to debug, and allows for ease of extensibility.

Getting started: 30 seconds to Keras

The core data structure of Keras is a model, a way to organize layers. The simplest type of model is the Sequential model, a linear stack of layers. For more complex architectures, you should use the Keras functional API, which allows to build arbitrary graphs of layers.

Here is the Sequential model:

from keras.models import Sequential

model = Sequential()

Stacking layers is as easy as .add():

from keras.layers import Dense

model.add(Dense(units=64, activation='relu', input_dim=100))
model.add(Dense(units=10, activation='softmax'))

Once your model looks good, configure its learning process with .compile():

model.compile(loss='categorical_crossentropy',
              optimizer='sgd',
              metrics=['accuracy'])

If you need to, you can further configure your optimizer. A core principle of Keras is to make things reasonably simple, while allowing the user to be fully in control when they need to (the ultimate control being the easy extensibility of the source code).

model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.SGD(lr=0.01, momentum=0.9, nesterov=True))

You can now iterate on your training data in batches:

# x_train and y_train are Numpy arrays --just like in the Scikit-Learn API.
model.fit(x_train, y_train, epochs=5, batch_size=32)

Alternatively, you can feed batches to your model manually:

model.train_on_batch(x_batch, y_batch)

Evaluate your performance in one line:

loss_and_metrics = model.evaluate(x_test, y_test, batch_size=128)

Or generate predictions on new data:

classes = model.predict(x_test, batch_size=128)

Building a question answering system, an image classification model, a Neural Turing Machine, or any other model is just as fast. The ideas behind deep learning are simple, so why should their implementation be painful?

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