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Questions tagged [activation-function]

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6
votes
4answers
120 views

Is it suitable to find inverse of last layer's activation function and apply it on the target output?

I have a neural network with the following structure: I am expecting specific outputs from the neural network which are the target values for my training. Let's say the target values are 0.8 for the ...
2
votes
1answer
26 views

Is the cube root function suitable as a n activation function?

I am trying to design a neural network on Python. Instead of the sigmoid function which has a limited range, I am thinking of using the cube root function which has the following graph: Is this ...
3
votes
1answer
19 views

Is there any problem in training a supervised model with loss function as negative log likelihood loss without using softmax or log softmax?

I am trying to train a supervised model where the output from the model is output of a linear function(WX + b). Kindly note that I'm not using any softmax or log_softmax on the result of the linear. I ...
2
votes
3answers
88 views

Activation functions

A linear activation function (or none at all) can only be used when the relation between input and output is linear. Why doesn't the same rule apply for other activation functions? For example; why ...
0
votes
1answer
36 views

Target values of 0.1 for 0 and 0.9 for 1 for sigmoid

I recently read an article about neural networks saying that, when using sigmoid as activation function, it's advised to use 0.1 as target value instead of 0, and 0.9 instead of 1. This was to avoid "...
7
votes
2answers
75 views

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

I just stumbled upon the concept of neuron coverage, which is the ratio of activated neurons and total neurons in a neural network. But what does it mean for a neuron to be "activated"? I know what ...
4
votes
1answer
190 views

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

I think that the advantage of using Leaky ReLU instead of ReLU is that in this way we cannot have vanishing gradient. Parametric ReLU has the same advantage with the only difference that the slope of ...
3
votes
2answers
69 views

ANNs with multiple activation outputs

Interested to know if there was any use or interest in activation functions with more than one output value to the next column instead of single firing. I'm interested to know if this would have any ...