What are the commonly used activation functions ? When are they used.

The commonly used loss functions are Linear : g(x) = x. This is the simplest activation function. However it cannot model complex decision boundaries. A deep network with linear activations can be shown incapable of handling non-linear decision boundaries. Sigmoid :   This is a common activation function in the last layer of the neural network…

I have designed a 2 layered deep neural network for a classifier with 2 units in the hidden layer. I use linear activation functions with a sigmoid at the final layer. I use a data visualization tool and see that the decision boundary is in the shape of a sine curve. I have tried to train with 200 data points with known class labels and see that the training error is too high. What do I do ?

Increase number of units in the hidden layer Increase number of hidden layers  Increase data set size Change activation function to tanh Try all of the above The answer is d. When I use a linear activation function, the deep neural network is realizing a linear combination of linear  functions which leads to modeling only…