Contents

- 1 What activation function is better for the hidden layers?
- 2 Where is softmax activation function used?
- 3 How to choose last layer activation and loss function?
- 4 Which is the last activation function of a neural network?
- 5 Which is an example of a linear activation function?
- 6 Which is combination of loss and activation functions should be used?

The rectified linear activation function, or ReLU activation function, is perhaps the most common function used for hidden layers. 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.

## Where is softmax activation function used?

The softmax function is used as the activation function in the output layer of neural network models that predict a multinomial probability distribution. That is, softmax is used as the activation function for multi-class classification problems where class membership is required on more than two class labels.

## How to choose last layer activation and loss function?

Last layer use ” softmax ” activation, which means it will return an array of 10 probability scores (summing to 1). Each score will be the probability that the current digit image belongs to one of our 10 digit classes.

## Which is the last activation function of a neural network?

No matter how many layers we have, if all are linear in nature, the final activation function of last layer is nothing but just a linear function of the input of first layer. Range :-inf to +inf; Uses : Linear activation function is used at just one place i.e. output layer.

## Which is an example of a linear activation function?

VARIANTS OF ACTIVATION FUNCTION :-. 1). Linear Function :-. Equation : Linear function has the equation similar to as of a straight line i.e. y = ax. No matter how many layers we have, if all are linear in nature, the final activation function of last layer is nothing but just a linear function of the input of first layer. Range : -inf to +inf.

## Which is combination of loss and activation functions should be used?

The purpose of this post is to provide guidance on which combination of final-layer activation function and loss function should be used in a neural network depending on the business goal. This post assumes that the reader has knowledge of activation functions.