Forward-Forward Algorithm on Morphological-Linear Neural Networks

This paper proposes using the Forward-Forward (FF) algorithm to train hybrid neural networks. We implement the FF algorithm in Morphological-Linear Neural Networks (MLNNs), which consist of two layers. The first layer is made up of morphological neurons and the second consists of perceptron-type neurons. For this we adapt the FF training algorithm in the calculation of the goodness and loss functions derived from the morphological neuron layer. The calculation of the goodness and the loss functions was not modified for the perceptron-type neuron layer. The experimental results show that it is possible to train morphological-linear neural networks with the FF algorithm, obtaining results similar to those obtained with the original FF algorithm. The comparison between the original training algorithm and the proposed one is carried out with low-dimensionality and binary classification datasets from the IMDB natural language processing dataset. Likewise, the ability of the FF algorithm is analyzed and compared in terms of its ability to disentangle patterns for classification tasks.