TensorFlow implementation of the Lorentz Boost Network from arXiv:1812.09722 [hep-ex].

import tensorflow as tf
from lbn import LBN
# initialize the LBN, set 10 combinations and pairwise boosting
lbn = LBN(10, boost_mode=LBN.PAIRS)
# create a feature tensor based on input four-vectors
features = lbn(four_vectors)
# use the features as input for a subsequent, application-specific network
...

Or with TensorFlow 2 and Keras:

import tensorflow as tf
from lbn import LBN, LBNLayer
# start a sequential model
model = tf.keras.models.Sequential()
# add the LBN layer
input_shape = (6, 4)
model.add(LBNLayer(input_shape, 10, boost_mode=LBN.PAIRS))
# add a dense layer
model.add(tf.keras.layers.Dense(1024))
# continue builing and training the model
...

For more examples on how to set up the LBN with TensorFlow (eager mode and autograph / tf.function ) and Keras, see this gist.

Via pip:

pip install lbn

NumPy and TensorFlow are the only dependencies. Both TensorFlow v1 and v2 are supported.

Tests should be run for Python 2 and 3 and for TensorFlow 1 and 2. The following commands assume you are in the root directory of the LBN respository:

python -m unittest test
# or via docker
for tag in 1.15.2 1.15.2-py3 2.1.0 2.2.0; do
    docker run --rm -v `pwd`:/root/lbn -w /root/lbn tensorflow/tensorflow:$tag python -m unittest test
done

If you like to contribute, we are happy to receive pull requests. Just make sure to add new test cases and run the tests. Also, please use a coding style that is compatible with our .flake8 config.

• Original source hosted on GitHub
• Report issues, questions, feature requests on GitHub Issues