Design and Analysis of FuzzyReLU Activation Function to Expanding Neural Network Capabilities
Keywords:
Fuzzy ReLU, ReLU, Recurrent Neural Network, General Adversarial Network, Graph Neural NetworkAbstract
FuzzyReLU, an innovative algorithm that introduces a variation of the ReLU activation function, seamlessly integrates into diverse neural network architectures, including the Recurrent Neural Network (RNN), General Adversarial Network (GAN), and Graph Neural Network (GNN). This integration involves adding dense layers with FuzzyReLU activation to sequential models in the case of RNN and GAN, as well as incorporating FuzzyReLU into the generator and discriminator models of GAN. Furthermore, FuzzyReLU is applied to the GraphSAGE layers in GNN, utilizing the StellarGraph library for predictions on graph-structured data. These implementations showcase the versatility of FuzzyReLU across neural network architectures, enabling effective utilization in decision-based deep learning tasks. Importantly, FuzzyReLU outperforms the original ReLU, resulting in up to a 3% improvement in performance across various problem domains.
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