# Activation function and its effect on backpropagation

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There some basic principles of an artificial neuron. The content is mostly about the review of class “Optimal theories and methods”.

# Artificial neuron

An artificial neuron is a mathematical function conceived as a model of biological neurons. An artificial neuron is elementary units in an artificial neural network.

The artificial neuron receives one or more inputs and sums them to produce an output.

# Components

Neural networks generally consist of five components:

- A directed graph is known as the network topography.
- A state variable associated with each neuron.
- A real-valued weight associated with each connection.
- A real-valued bias associated with each neuron.
- A transfer function $f$ for each neuron

$$

\begin{cases}

u_i = \Sigma ^n{j=1} w{ij} s_j \\

x_i = u_i - \theta_i \\

v_i = f(x_i)

\end{cases}$$

where $i = 1,2,…,n$.

## Input and output

On the last image, $x_1, x_2, …, and x_m$ is input (some features), and $y_i$ is output.

## Weights

When $w_{ij}>0$ means activation, by contrast, $w_{ij}<0$ means deactivation. The values of weights can measure the degree of impact between nodes $i$ and $j$ in a different layer.

## Activation function/Transfer function

The activation function of a node defines the output of that node given an input or set of inputs. Only **nonlinear activation functions** allow such networks to compute nontrivial problems using only a small number of nodes, and such activation functions are called **nonlinearities**.

Utilizing nonlinear activation functions can avoid linear units can be simplified as one unit. Nonlinear activation function can also be used to solve non-linearly separable problems.

$\phi$ is the transfer function (commonly a threshold function).

### Logistic function

The logistic function is a classic activation function always used in **ANN**. There are **two types** of logistic function which are sigmoid function and hyperbolic tangent function (tanh).

#### Sigmoid function

The function is expressed as:

$$f_1 (x) = \frac{1}{1+exp(-ax)}$$

where $x=w_{ij}v_i+b_i$ and the slope parameter $a$.

Drawbacks:

**Vanishing gradient problem.**The gradient for the data fallen in the region of either 0 or 1 is almost zero. Data is not passed the neurons to the update the weights parameter during BP training.- The sigmoid activation function centre on 0.5. Thus, it slows down the learning process of the DNN.

#### tanh function

It is a rescaled and biased version of sigmoid function.

$$f_2 (x) = \frac{exp(2x) - 1}{exp(2x) + 1}$$

### Multistate activation function (MSAF)

MSAF combined with multiple logistic functions and a constant, $r_1$ to have additional output states.

$$f_3 (x) = \frac{1}{1+exp(-x-r_1)}+ \frac{1}{1+exp(-x)}$$

$$f_4 (x) = \frac{1}{1+exp(-x-r_1)}+ \frac{1}{1+exp(-x)}$$

### Rectified linear unit (ReLU) and leaky rectified linear unit (LReLU)

ReLU has become a default activation function in DNN because of its simplicity that shortens the training computational time.

Drawback:

- ReLU may not pre-defined boundary. Caused by the neuron that in the negative of the activation function that will no longer be activated throughout the training process.

LReLU can solve this problem.

$$f_5 (x) = \begin{cases}

x, & x\gt 0 \

0, & x\leq 0

\end{cases}$$

$$f_6 (x) = \begin{cases}

x, & x\gt 0 \

bx, & x\leq 0

\end{cases}$$

### Exponential linear unit

ELU **reduces bias shift problem,** which is defined as the change of a neuron’s mean value due to weights update. ELU has negative values which allow them to push mean unit activations closer to 0 like batch normalization, but with lower computation complexity. Compared to LReLU, ELU has a clear saturation plateau in its negative region.

$$f_7 (\alpha, x) = \begin{cases}

\alpha(e^x - 1), & x\lt 0 \

x, & x\ge 0

\end{cases}$$

### SUMMARY OF ACTIVATION FUNCTION

## Activation backpropagation

**Sigmoid function**

$$\frac{\partial{f_i (x)}{\pratial{x_j}} =

\begin{cases}

f_i(x) (1 - f_i (x)) & i = j \\

0 & i \neq j

\end{cases}$$

**Softmax function**

$$\frac{\partial{g_i (x)}{\pratial{x_j}} =

\begin{cases}

\frac{e^{x_i} (\Sigma^N_{j=1, j \neq i}) x_j}{(\Sigma^N_{j=1} e^{{x_j})}2} & i = j \\

\frac{e^{x_j + x_i}}{(\Sigma^N_{j=1} e^{{x_j})}2} & i \neq j

\end{cases}$$

**ReLu function**

$$\frac{\partial{z_i (x)}{\pratial{x_j}} =

\begin{cases}

a & x_i > 0 \\

0 & x_i < 0

\end{cases}$$

**In general, calculating the softmax is slower than the sigmoid and calculating ReLu is faster.**

## Bias

A bias unit is meant to allow units in your net to learn an appropriate threshold (i.e. after reaching a certain total input, start sending positive activation), since normally a positive total input means a positive activation.

As a switch quantity to control activation function, expressed as $\theta_i$

# Reference

[2] Montana, D.J., 1995. Neural network weight selection using genetic algorithms. *Intelligent Hybrid Systems*, *8*(6), pp.12-19.

[4] Lau, M.M. and Lim, K.H., 2018, December. Review of adaptive activation function in deep neural network. In *2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES)* (pp. 686-690). IEEE.

[5] Rasamoelina, A.D., Adjailia, F. and Sinčák, P., 2020, January. A Review of Activation Function for Artificial Neural Network. In *2020 IEEE 18th World Symposium on Applied Machine Intelligence and Informatics (SAMI)* (pp. 281-286). IEEE.

[6] Why the BIAS is necessary in ANN? Should we have separate BIAS for each layer?

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