System and method of using fractional adaptive linear unit as activation in artifacial neural network

One or more non-transitory computer-readable media storing instructions executable to perform operations for deep learning based on a trainable activation function, the operations comprising:

• accessing a neural network comprising an input layer, a plurality of hidden layers, and an output layer;
• adding the trainable activation function to one or more hidden layers of the plurality of hidden layers, the trainable activation function comprising a tunable parameter, the tunable parameter representing a fractional derivative order in the trainable activation function;
• and
• training the neural network by inputting a plurality of training samples into the neural network, wherein training the neural network comprises:
• determining a value of the tunable parameter based on the plurality of training samples.

An apparatus for deep learning based on a trainable activation function, the apparatus comprising:

• a computer processor for executing computer program instructions;
• and
• a non-transitory computer-readable memory storing computer program instructions executable by the computer processor to perform operations comprising:
• accessing a neural network comprising an input layer, a plurality of hidden layers, and an output layer,
• adding the trainable activation function to one or more hidden layers of the plurality of hidden layers, the trainable activation function comprising a tunable parameter, the tunable parameter representing a fractional derivative order in the trainable activation function, and
• training the neural network by inputting a plurality of training samples into the neural network, wherein training the neural network comprises:
• determining a value of the tunable parameter based on the plurality of training samples.