AdaBnn

Code related to thesis work: “AdaBnn: Binarized Neural Networks trained with adaptive structural learning” [pdf]

This repository contains currently two colaboratory notebooks:

• English
• Spanish

That document experiments made with an experimental Keras based implementation of the AdaNet algorithm presented in “AdaNet: Adaptive Structural Learning of Artificial Neural Networks” at ICML 2017, for learning the structure of a neural network as an ensemble of subnetworks. Also, AdaBnn is presented as a modification of AdaNet that imposed binary constraints on running time to try to increase performance in terms of time and as a way of regularization based on “Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1“.

Also, separated code is included containing Adanet and AdaBnn implementations with its documentation.

Some findings

According to the experiments provided in the notebooks:

• Binarizing the weights of the network, in the case of adaptive structural learning, have similar effects of having a high mutation rate in genetic algorithms, the pattern of learning is much more hard to follow between iterations, doesn’t hold incremental performance in T iterations.
• Adam optimization it’s more appropiate for such AdaBnn structure in most cases as well as fewer iterations (T parameter on the paper).
• Currently Binarizing AdaNet doesn’t hold that much of improvement but it could be a start point for adding constraints for weights/activations as regularization method for adaptive structural learning.

Futher work

Further work may include the binarization process as part of the convolution sub network, which was the initial proposal of (M Courbariaux ,2016).

Example

After importing dependencies and declaring each model (proposed implementation of AdaNet and AdaBnn) they can be trained and used with:

epochs = 25 
B = 150 # neurons on each layer of the sub network  
T = 2 # number of iterations of the adanet algorithm (maximum complexity of the model)
conf = dict({
    'network': {
        'activation': BinaryRelu, 
        'output_activation': 'sigmoid',# softmax for multiple clases
        'optimizer': keras.optimizers.Adam(lr=0.0001),#Adam
        'loss': 'binary_crossentropy' # F for adanet traditional
        },
    'training':{
        'batch_size': 100,
        'epochs': epochs
        },
    'adabnn':{
        'B': B,
        'T': T,
        'delta': 1.01,
        'seed': 42
        }
    })
model, rend = AdaBnn(x_train,y_train,conf,verbose=0) # rend will have accuracy/loss at each iteration T
training_results = model.evaluate(x_test,y_test,verbose=0)

Citing this Work

If you use these implementations for academic research, please cite the following:

@misc{gonzalez2018adabnn,
  author    = {Wilmer Gonzalez},
  title     = {AdaBnn: Binarized Neural Networks trained with adaptive structural learning},
  year      = {2018},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/wilmeragsgh/adabnn}},
}

License

Included functions and notebooks are released under the Apache License 2.0.