Can we use tanh in output layer?

Yes pls u can use tanh to handle the signal.. yes I used [0.1,0.9] range for normalization, but the sigmoid bounded every output and all of my outputs with sigmoid will be given equal to 0.5, when I replace Tanh all outputs are given in the range [0.1,0.9].

Why are the layers other than input and output called as hidden layers?

Hidden layers reside in-between input and output layers and this is the primary reason why they are referred to as hidden. The word “hidden” implies that they are not visible to the external systems and are “private” to the neural network. There could be zero or more hidden layers in a neural network.

Why do we use activation functions in each hidden layer?

Activation functions are a critical part of the design of a neural network. The choice of activation function in the hidden layer will control how well the network model learns the training dataset. The choice of activation function in the output layer will define the type of predictions the model can make.

Does tanh as output activation work with cross entropy loss?

Yes we can, as long as we use some normalizor (e.g. softmax) to ensure that the final output values are in between 0 and 1 and add up to 1.

Can you use tanh for binary classification?

Tanh can be used in binary classification between two classes. When using tanh, remember to label the data accordingly with [-1,1]. Sigmoid function is another logistic function like tanh. If the sigmoid function inputs are restricted to real and positive values, the output will be in the range of (0,1).

Why are hidden layers called hidden?

There is a layer of input nodes, a layer of output nodes, and one or more intermediate layers. The interior layers are sometimes called “hidden layers” because they are not directly observable from the systems inputs and outputs.

Why use hidden layers in neural networks?

In artificial neural networks, hidden layers are required if and only if the data must be separated non-linearly. Looking at figure 2, it seems that the classes must be non-linearly separated. A single line will not work. As a result, we must use hidden layers in order to get the best decision boundary.

Why we use ReLU in hidden layers?

One reason you should consider when using ReLUs is, that they can produce dead neurons. That means that under certain circumstances your network can produce regions in which the network won’t update, and the output is always 0.

Why the hidden layers in a neural network must have a non-linear activation function?

Non-linearity is needed in activation functions because its aim in a neural network is to produce a nonlinear decision boundary via non-linear combinations of the weight and inputs.

Why do we use ReLU in hidden layers?

Computing the ReLU. ReLU is very simple to calculate, as it involves only a comparison between its input and the value 0. It also has a derivative of either 0 or 1, depending on whether its input is respectively negative or not.

Is the tanh function better than the sigmoid function for neural networks?

This makes the tanh function almost always better as an activation function (for hidden layers) rather than the sigmoid function. To prove this myself (at least in a simple case), I coded a simple neural network and used sigmoid, tanh and relu as activation functions, then I plotted how the error value evolved and this is what I got.

What are some examples of hidden layers in neural networks?

For example, a hidden layer functions that are used to identify human eyes and ears may be used in conjunction with subsequent layers to identify faces in images. While the functions to identify eyes alone are not enough to independently recognize objects, they can function jointly within a neural network.

Can tanh and sigmoid be used as activation function for hidden layer?

Sigmoid and tanh should not be used as activation function for the hidden layer. This is because of the vanishing gradient problem, i.e., if your input is on a higher side (where sigmoid goes flat) then the gradient will be near zero.

Why do we use Hyper-tangent functions for activation function of hidden layer?

Im personally studying theories of neural network and got some questions. In many books and references, for activation function of hidden layer, hyper-tangent functions were used. Books came up with really simple reason that linear combinations of tanh functions can describe nearly all shape of functions with given error.