# Tag Info

## Hot answers tagged activation-functions

Accepted

### What activation function does the human brain use?

The thing you were reading about is known as the action potential. It is a mechanism that governs how information flows within a neuron. It works like this: Neurons have an electrical potential, ...
• 1,991
Accepted

### How to choose an activation function for the hidden layers?

It seems to me that you already understand the shortcomings of ReLUs and sigmoids (like dead neurons in the case of plain ReLU). You may want to look at ELU (exponential linear units) and SELU (self-...
• 933

### What are the advantages of ReLU vs Leaky ReLU and Parametric ReLU (if any)?

Combining ReLU, the hyper-parameterized1 leaky variant, and variant with dynamic parametrization during learning confuses two distinct things: The comparison between ReLU with the leaky variant is ...
• 7,375
Accepted

### Are softmax outputs of classifiers true probabilities?

The answer is both yes, and no. Or, to put it another way, the answer depends on what exactly you mean by "represent probabilities", and there is a valid sense in which the answer is yes, ...
• 266
Accepted

### What does it mean for a neuron in a neural network to be activated?

A neuron is said activated when its output is more than a threshold, generally 0. For examples : $$y = Relu(a) > 0$$ when a = w^Tx+b > 0 \end{...
• 515

### What is the purpose of an activation function in neural networks?

If you only had linear layers in a neural network, all the layers would essentially collapse to one linear layer, and, therefore, a "deep" neural network architecture effectively wouldn't be deep ...

### Are softmax outputs of classifiers true probabilities?

Excellent question. The simple answer is no. Softmax actually produces uncalibrated probabilities. That is, they do not really represent the probability of a prediction being correct. What usually ...
• 1,112

### Why is the derivative of the activation functions in neural networks important?

Consider a dataset $\mathcal{D}=\{x^{(i)},y^{(i)}:i=1,2,\ldots,N\}$ where $x^{(i)}\in\mathbb{R}^3$ and $y^{(i)}\in\mathbb{R}$ $\forall i$ The goal is to fit a function that best explains our dataset....
• 131
Accepted

### Do neurons of a neural network model a linear relationship?

In a neural network (NN), a neuron can act as a linear operator, but it usually acts as a non-linear one. The usual equation of a neuron $i$ in layer $l$ of an NN is o_i^l = \sigma(\mathbf{x}_i^l \...
• 37k

### Why is no activation function used at the final layer of super-resolution models?

I am not into the field of super-resolution, but I think this question applies to general neural network construction. Usually, you try to solve a classification problem or a regression problem with ...
Accepted

### Why do ResNets avoid the vanishing gradient problem?

Before proceeding, it's important to note that ResNets, as pointed out here, were not introduced to specifically solve the VGP, but to improve learning in general. In fact, the authors of ResNet, in ...
• 37k

### Why do activation functions need to be differentiable in the context of neural networks?

No, it is not necessary that an activation function is differentiable. In fact, one of the most popular activation functions, the rectifier, is non-differentiable at zero! This can create problems ...
• 161

• 37k

### Can LSTM model use ReLU or LeakyReLU as the activation funtion?

Yes an LSTM can use any of these. There are no hard rules of which to use. That is why they all exist. Some rules of thumb are: Relu is the cheapest computationally. Almost always worth trying first. ...
• 2,005
Accepted

### Can LSTM model use ReLU or LeakyReLU as the activation funtion?

Yes, you can use ReLU or LeakyReLU in an LSTM model. There aren't hard rules for choosing activation functions. Run your model with each activation function and pick the best performing one. See the ...
• 1,678