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AdaptiveLRF

This is the implementation of AdaptiveLRF [1]. This code was written based on keras/tensorflow and tested on tensorflow 1.15.0.

How to use?

The code contains just a class named AdaptiveLRF. This class extends Callback (a class in keras library). Then, what just you need to do is add a instance of this class in your callback list in fit function.

Create an instance

The constructor parameter is in the following:

  1. A trainable model. The acceptable type is keras.models.Model.
  2. The number of the classes. The acceptable type is int.
  3. A part of the input of the train samples. The acceptable type is numpy.ndarray.
  4. A part of the output of the train samples. The acceptable type is numpy.ndarray.
  5. The batch size which the conditional analysis is done on this size of the batch. The acceptable type is int.
  6. The value of k. This value was described in the paper.
  7. The number of patients after overfitting.

An small example

model.compile(...)
reg = AdaptiveLRF(model, 10, ...)
model.fit(..., callbacks=[reg])

Notice, you have to give the validation or test data to the fit function!

Reference

[1] Mohammad Mahdi Bejani and Mehdi Ghatee, Adaptive Low-Randk Factorization to Regularize Shallow and Deep Neural Networks, ArXiv, 2020