Artificial Intelligence
Sponsored by
Sponsored logo

Questions tagged [relu]

Ask Question

For questions about the rectified linear unit (ReLU) or rectifiers, which is a widely used activation function in neural networks.

29 questions
Newest
Active
Bountied
Unanswered
Filter by
Sorted by
Tagged with
1
vote
0answers
28 views

What are the benefits of using ELU over other activation functions in CNNs?

I have come up with some examples of CNNs (segmentation CNNs) that use ELU (exponential linear unit) as an activation function. What are the benefits of this activation function over others, such as ...
0
votes
1answer
33 views

How is the performance of a model affected by adding a ReLU to fully connected layers?

How significant is adding a ReLU to fully connected (FC) layers? Is it necessary, or how is the performance of a model affected by adding ReLU to FC layers?
2
votes
0answers
53 views

How to decide if gradients are vanishing?

I am trying to debug a convolutional neural network. I am seeing gradients close to zero. How can I decide whether these gradients are vanishing or not? Is there some threshold to decide on vanishing ...
0
votes
0answers
22 views

Random Initializations with ReLU gives puzzling results

this may sound naive, but I’m getting a really puzzling result. I was experimenting with MNIST on vanilla MLP (784, 256, 128, 10) with ...
0
votes
2answers
55 views

Is it possible to have a negative output using only ReLU activation functions, but not in the final layer?

I know that if you use an ReLU activation function at a node in the neural network, the output of that node will be non-negative. I am wondering if it is possible to have a negative output in the ...
3
votes
1answer
68 views

Can residual neural networks use other activation functions different from ReLU?

In many diagrams, as seen below, residual neural networks are only depicted with ReLU activation functions, but can residual NNs also use other activation functions, such as the sigmoid, hyperbolic ...
2
votes
0answers
75 views

Should batch normalisation be applied before or after ReLU?

I know that there has been some discussion about this (e.g. here and here), but I can't seem to find consensus. The crucial thing that I haven't seen mentioned in these discussions is that applying ...
1
vote
1answer
67 views

If features are always positives, why do we use RELU activation functions?

When does it happen that a layer (either first or hidden) outputs negative values in order to justify the use of RELU? As far as I know, features are never negative or converted to negative in any ...
2
votes
1answer
48 views

Does net with ReLU not learn when output < 0?

The derivative of ReLU is 0 if its output is lower than 0 - $d ReLU(x)/dReLU$ is $0$ if $x < 0$. Let's denote some net's output by $Out$, so if this net's last layer is ReLU then we get that $dOut/...
3
votes
1answer
87 views

Neural network doesn't seem to converge with ReLU but it does with Sigmoid?

I'm not really sure if this is the sort of question to ask on here, since it is less of a general question about AI and more about the coding of it, however I thought it wouldn't fit on stack overflow....
1
vote
0answers
35 views

Why do DeconvNet use ReLU in the backward pass?

Why does DeconvNet (Zeiler, 2014) use ReLU in the backward pass (after unpooling)? Are not the feature maps values already positive due to the ReLU in the forward pass? So, why do the authors apply ...
1
vote
0answers
54 views

Are PreLU and Leaky ReLU better than ReLU in the case of noisy labels?

Let's assume I want to build a semantic segmentation algorithm, based on Multires-UNET. My GT-masks are messy and generated by a GAN, but they are getting better and better over time. The goal is ...
2
votes
2answers
116 views

Is ReLU a non-linear activation function?

According to this blog post The purpose of an activation function is to add some kind of non-linear property to the function The sigmoid is typically used as an activation function of a unit of a ...
2
votes
2answers
67 views

How to determine the target value when using ReLU as activation function?

Consider the following simple neural network with only one neuron. The input is $x_1$ and $y_2$, where $-250 < x < 250$ and $-250 < y < 250$ The weights of the only neuron are $w_1$ and $...
0
votes
1answer
195 views

Network doesn't converge with ReLU or Leaky ReLU, but works well with sigmoid/tanh

I have these training data to separate, the classes are rather randomly scattered: My first attempt was using tf.nn.relu activation function, but output was stuck ...
4
votes
2answers
265 views

Is PReLU superfluous with respect to ReLU?

Why do people use the $PReLU$ activation? $PReLU[x] = ReLU[x] + ReLU[p*x]$ with the parameter $p$ typically being a small negative number. If a fully connected layer is followed by a at least two ...
1
vote
1answer
106 views

Dropout causes too much noise for network to train

I am using dropout of different values to train my network. The problem is, dropout is contributing almost nothing to training, either causing so much noise the error never changes, or seemingly ...
2
votes
0answers
77 views

How is the bias caused by a max pooling layer overcome?

I have constructed a CNN that utilizes max-pooling layers. I have found with these layers that, should I remove them, my network performs ideally with every output and gradient at each layer having a ...
3
votes
1answer
110 views

How are exploding numbers in a forward pass of a CNN combated?

Take AlexNet for example: In this case, only the activation function ReLU is used. Due to the fact ReLU cannot be saturated, it instead explodes, like in the following example: Say I have a weight ...
0
votes
2answers
503 views

How should the values of the filters of a CNN change?

I wrote a convolutional neural network for the MNIST dataset with Numpy from scratch. I am currently trying to understand every part and calculation. But one thing I noticed was the "just positive" ...
3
votes
1answer
287 views

How does backpropagation with unbounded activation functions such as ReLU work?

I am in the process of writing my own basic machine learning library in Python as an exercise to gain a good conceptual understanding. I have successfully implemented backpropagation for activation ...
2
votes
1answer
277 views

Is there a ReLU-like activation function that concatenates positive and negative values?

Is there a ReLU-like activation function that concatenates positive and negative values? What is its name? Apparently, it doubles the output dimension.
10
votes
1answer
1k views

What happens when I mix activation functions?

There are several activation functions, such as ReLU, sigmoid or $\tanh$. What happens when I mix activation functions? I recently found that Google has developed Swish activation function which is (...
11
votes
1answer
12k 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 ...
8
votes
2answers
4k views

Why do we prefer ReLU over linear activation functions?

The ReLU activation function is defined as follows $$y = \operatorname{max}(0,x)$$ And the linear activation function is defined as follows $$y = x$$ The ReLU nonlinearity just clips the values ...
10
votes
1answer
1k views

How exactly can ReLUs approximate non-linear and curved functions?

Currently, the most commonly used activation functions are ReLUs. So I answered this question What is the purpose of an activation function in neural networks? and, while writing the answer, it struck ...
1
vote
2answers
4k views

What is the derivative of the Leaky ReLU activation function?

I am implementing a feed-forward neural network with leaky ReLU activation functions and back-propagation from scratch. Now, I need to compute the partial derivatives, but I don't know what the ...
2
votes
2answers
206 views

Why do non-linear activation functions that produce values larger than 1 or smaller than 0 work?

Why do non-linear activation functions that produce values larger than 1 or smaller than 0 work? My understanding is that neurons can only produce values between 0 and 1, and that this assumption can ...
8
votes
3answers
955 views

Are ReLUs incapable of solving certain problems?

Background I've been interested in and reading about neural networks for several years, but I haven't gotten around to testing them out until recently. Both for fun and to increase my understanding, I ...