The range of the tanh function is from (-1 to 1). Tanh is also like logistic sigmoid but better. Tanh or hyperbolic tangent Activation Function The softmax function is a more generalized logistic activation function which is used for multiclass classification. The logistic sigmoid function can cause a neural network to get stuck at the training time. The function is monotonic but function’s derivative is not. The function is differentiable.That means, we can find the slope of the sigmoid curve at any two points. Therefore, it is especially used for models where we have to predict the probability as an output.Since probability of anything exists only between the range of 0 and 1, sigmoid is the right choice. The main reason why we use sigmoid function is because it exists between (0 to 1). The Sigmoid Function curve looks like a S-shape. The Nonlinear Activation Functions are mainly divided on the basis of their range or curves– 1. Monotonic function: A function which is either entirely non-increasing or non-decreasing. change in x-axis.It is also known as slope. The main terminologies needed to understand for nonlinear functions are:ĭerivative or Differential: Change in y-axis w.r.t. It makes it easy for the model to generalize or adapt with variety of data and to differentiate between the output. Nonlinearity helps to makes the graph look something like this The Nonlinear Activation Functions are the most used activation functions. It doesn’t help with the complexity or various parameters of usual data that is fed to the neural networks. Therefore, the output of the functions will not be confined between any range. Why derivative/differentiation is used ?Īs you can see the function is a line or linear.ReLU (Rectified Linear Unit) Activation Function
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