Source: Deep Learning on Medium

# Bye to Trial and Error Activation Functions of Neural Networks III: Proposed Jameel’s ANNAF Deterministic Criterion

“** You never change things by fighting the existing reality. To change something, build a new model that makes the existing model obsolete**.”

Buckminster Fuller

“** Your brain does not manufacture thoughts. Your thoughts shape neural networks**.”

Deepak Chopra

In my third article entitled “*Bye to Trial and Error Activation Functions of Neural Networks II: Proposed Jameel’s ANNAF Stochastic Criterion*”, a stochastic criterion was discussed. It would be recalled that eight (8) stocks prices activation functions were proposed. In this article, using “*Jameel’s ANNAF Deterministic Criterion*”, we can have up to at least **TWO THOUSAND (2000) ACTIVATION FUNCTIONS** **emanated** from our sample **AI-ML-PURIFIED DATA SET**.

# Proposed Jameel’s ANNAF Deterministic Criterion

**ANNAF** means Artificial Neural Network Activation Functions.

For any Neural Network that require **DETERMINISTIC ACTIVATION FUNCTIONS** can satisfy the following proposed criterion:

**(1)** The function **f(x)**shall be **EMANATED** from our referenced AI-ML-Purified Data Set. The essence of the function **f(x) **to be EMANATED from the referenced AI-ML-Purified Data is to build an incredible and sophisticated Activation Function(s) that has the BEST MATCH AND OR TUNE with our referenced AI-ML-Purified Data Set since the neural network is a system made to learn a function from data. The Activation Functions obtained from the referenced AI-ML-Purified Data can be used to build an extra-ordinary Neural Network Artificial Intelligence System.

**(2)** A curve fitting for Best Fitted Deterministic Function shall be carried out, the function **f(x)**whose:

(a) Rank is Unity (1)

(b) Fattiness Standard Error is smaller than any other on the list;

**(3)** The function **f(x) **shall be Nonlinear;

**(4)**The function **f(x) **shall have a Range;

**(5)** The function **f(x) **shall be Continuously Differentiable;

**(6)** The function **f(x) **shall be Monotonic;

**(7)** The function **f(x) **shall be Smooth Function with a Monotonic Derivative;

**(8)** The function **f(x) **shall Approximates Identity near the Origin.

If these failed Discard the 1st rated function **f(x)**, repeat (1) to (8) until the qualified Deterministic Activation Function is **EMANATED** from our referenced AI-ML-Purified Data.

**NOTE:** Deep Learning Artificial Neural Network’s Hidden and output Layers consist of at least one, two or more Best fitted Activation Functions **EMANATED** from our AI-Data Set, therefore, the **RANK: UNITY (ONE)** in (2)-(a) and Fattiness Standard Error (2)-(b) of the criterion means when a function whose Real “**Rank =1**” was chosen and it satisfied (1) to (8) then the next function on list whose Real “**Rank=2**” will assume “**New Rank=1**” and will be tested to satisfy all the eight (8) axioms until we have the required number of **BEST (EXCELLENT)** Activation Functions needed to carry out our Deep Learning Artificial Neural Network.