TensorFlow implementation of the Lorentz Boost Network (LBN). https://arxiv.org/abs/1812.09722.
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.
Installation and dependencies
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
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