Keras: Theano-based Deep Learning library
Keras is a minimalist, highly modular neural network library in the spirit of Torch, written in Python, that uses Theano under the hood for optimized tensor manipulation on GPU and CPU. It was developed with a focus on enabling fast experimentation.
Use Keras if you need a deep learning library that:
- allows for easy and fast prototyping (through total modularity, minimalism, and extensibility).
- supports both convolutional networks and recurrent networks, as well as combinations of the two.
- supports arbitrary connectivity schemes (including multi-input and multi-output training).
- runs seamlessly on CPU and GPU.
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.
Minimalism. Each module should be kept short and simple (<100 lines of code). Every piece of code should be transparent upon first reading. No black magic: it hurts iteration speed and ability to innovate.
Easy extensibility. New modules are dead simple to add (as new classes/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 (like in Caffe or PyLearn2). Models are described in Python code, which is compact, easier to debug, and allows for ease of extensibility.
Find the code on Github: fchollet/keras.
Keras is licensed under the MIT license.
Getting started: 30 seconds to Keras
Sequential model (a linear pile of layers):
from keras.models import Sequential model = Sequential()
Stacking layers is as easy as
from keras.layers.core import Dense, Activation model.add(Dense(output_dim=64, input_dim=100, init="glorot_uniform")) model.add(Activation("relu")) model.add(Dense(output_dim=10, init="glorot_uniform")) model.add(Activation("softmax"))
Once your model looks good, configure its learning process with
If you need to, you can further configure your optimizer. A core principle of Keras is make things 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).
from keras.optimizers import SGD model.compile(loss='categorical_crossentropy', optimizer=SGD(lr=0.01, momentum=0.9, nesterov=True))
You can now iterate on your training data in batches:
model.fit(X_train, Y_train, nb_epoch=5, batch_size=32)
Alternatively, you can feed batches to your model manually:
Evaluate your performance in one line:
objective_score = model.evaluate(X_test, Y_test, batch_size=32)
Or generate predictions on new data:
classes = model.predict_classes(X_test, batch_size=32) proba = model.predict_proba(X_test, batch_size=32)
Building a network of LSTMs, a deep CNN, a Neural Turing Machine, a word2vec embedder or any other model is just as fast. The ideas behind deep learning are simple, so why should their implementation be painful?
Have a look at the examples.
Keras uses the following dependencies:
- numpy, scipy
- HDF5 and h5py (optional, required if you use model saving/loading functions)
- Optional but recommended if you use CNNs: cuDNN.
Once you have the dependencies installed, clone the repo:
git clone https://github.com/fchollet/keras.git
Go to the Keras folder and run the install command:
cd keras sudo python setup.py install
You can also install Keras from PyPI:
sudo pip install keras
You can ask questions and join the development discussion on the Keras Google group.
Keras welcomes all contributions from the community.
- Keep a pragmatic mindset and avoid bloat. Only add to the source if that is the only path forward.
- New features should be documented. Make sure you update the documentation along with your Pull Request.
- The documentation for every new feature should include a usage example in the form of a code snippet.
- All changes should be tested. Make sure any new feature you add has a corresponding unit test.
- Please no Pull Requests about coding style.
- Even if you don't contribute to the Keras source code, if you have an application of Keras that is concise and powerful, please consider adding it to our collection of examples.
Why this name, Keras?
Keras (κέρας) means horn in Greek. It is a reference to a literary image from ancient Greek and Latin literature, first found in the Odyssey, where dream spirits (Oneiroi, singular Oneiros) are divided between those who deceive men with false visions, who arrive to Earth through a gate of ivory, and those who announce a future that will come to pass, who arrive through a gate of horn. It's a play on the words κέρας (horn) / κραίνω (fulfill), and ἐλέφας (ivory) / ἐλεφαίρομαι (deceive).
Keras was developed as part of the research effort of project ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System).
"Oneiroi are beyond our unravelling --who can be sure what tale they tell? Not all that men look for comes to pass. Two gates there are that give passage to fleeting Oneiroi; one is made of horn, one of ivory. The Oneiroi that pass through sawn ivory are deceitful, bearing a message that will not be fulfilled; those that come out through polished horn have truth behind them, to be accomplished for men who see them."
-- Homer, Odyssey 19. 562 ff (Shewring translation).