1. The problem with a step function is that it does not allow multi-value outputs—for example, it cannot support classifying the inputs into one of several categories.

2. It also doesn't reflect the confidence of each prediction (as opposed to a logistic function that reflects confidence by giving a probability of how close you are to one of the two outputs).

3. It is not recommended to use in hidden layer because it's doesn't have derivate learning value

Problem with Binary Step Functions

A binary step function is a threshold-based activation function. If the input value is above or below a certain threshold, the neuron is activated and sends exactly the same signal to the next layer.

University of Michigan - Ann Arbor

There are 12 types of activation functions:

- Binary Step
- Linear
- Sigmoid
- Tanh
- ReLU
- Leaky ReLU
- Parameterised ReLU
- Exponential Linear Unit
- Swish
- Softmax

https://www.analyticsvidhya.com/blog/2020/01/fundamentals-deep-learning-activation-functions-when-to-use-them/

Types of Activation Functions

A helpful website that introduces neural networks:
https://missinglink.ai/guides/neural-network-concepts/

Neural Network Reference

Binary Step Function

A linear activation function takes the form: $y = cx$.

It takes the inputs, multiplied by the weights for each neuron, and creates an output signal proportional to the input. In one sense, a linear function is better than a step function because it allows multiple outputs, not just yes and no.


Linear Activation Function

Modern neural network models use non-linear activation functions. They allow the model to create complex mappings between the network’s inputs and outputs, which are essential for learning and modeling complex data, such as images, video, audio, and data sets which are non-linear or have high dimensionality.

There are 8 common non-linear activation functions:
 - Sigmoid / Logistic
 - TanH / Hyperbolic Tangent
 - ELU (Exponential Linear Unit)
 - ReLU (Rectified Linear Unit)
 - Leaky ReLU
 - Parametric ReLU
 - Softmax
 - Swish

Non-Linear Activation Functions

Sigmoid 
Pros: useful for binary classification, output layer 
Cons: shifted from center

Tanh 
Pros: has centering effect, almost always superior to sigmoid 
Cons: complex in comparison to ReLU 

ReLU
Pros: simpler, default
Cons: Derivative is 0 when z is negative --> leaky reLU

Pros and Cons of each Activation Function

Activation functions are a critical part of the design of a neural network- A hidden layer in a neural network is a layer that receives input from another layer (such as another hidden layer or an input layer) and provides output to another layer (such as another hidden layer or an output layer).

There are perhaps three activation functions you may want to consider for use in hidden layers; they are:

- Rectified Linear Activation (ReLU)
The rectified linear activation function calculated as max(0.0, x)
It is common because it is both simple to implement and effective at overcoming the limitations of other previously popular activation functions, such as Sigmoid and Tanh.


- Logistic (Sigmoid)
The sigmoid activation function is also called the logistic function, and calculated as 1.0 / (1.0 + e^-x)
It is used in the logistic regression classification algorithm.

- Hyperbolic Tangent (Tanh)
The function takes any real value as input and outputs values in the range -1 to 1. The larger the input (more positive), the closer the output value will be to 1.0, whereas the smaller the input (more negative), the closer the output will be to -1.0.
IT is calculated as (e^x – e^-x) / (e^x + e^-x)

It is common practive to choose activation function based on the type of model you work on.
Multilayer Perceptron (MLP): ReLU activation function.
Convolutional Neural Network (CNN): ReLU activation function.
Recurrent Neural Network: Tanh and/or Sigmoid activation function.



How to Choose an Activation Function for Deep Learning

Which of the following are activation functions?

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Related

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