What other activation functions could we use instead of sigmoid?
The hyperbolic tangent activation function is also referred to simply as the Tanh (also “tanh” and “TanH“) function. It is very similar to the sigmoid activation function and even has the same S-shape. The function takes any real value as input and outputs values in the range -1 to 1.
Why sigmoid or TanH is not preferred to be used as the activation function in the hidden layer of the neural network?
The sigmoid and hyperbolic tangent activation functions cannot be used in networks with many layers due to the vanishing gradient problem. The rectified linear activation function overcomes the vanishing gradient problem, allowing models to learn faster and perform better.
Which is the best activation function to use?
Activation functions must be chosen by looking at the range of the input values. However, ReLU or PReLU is a good starting point with sigmoid or softmax to the output layer. Activation function with learnable parameters is often implemented as separate layers.
Why do we need activation functions in network theory?
It is often termed as a squashing function as well. It aims to introduce non-linearity in the input space. The non-linearity is where we get the wiggle and the network learns to capture complicated relationships.
Why do we use non linear activation functions?
Non-linear functions address the problems of a linear activation function: They allow back-propagation because they have a derivative function which is related to the inputs. They allow “stacking” of multiple layers of neurons to create a deep neural network.
How are activation functions implemented with learnable parameters?
Activation function with learnable parameters is often implemented as separate layers. This is more intuitive since the parameters are learned through the same back-propagation algorithm. Hope you enjoyed reading this article.