Questions tagged [sigmoid]

For questions about the sigmoid functions (in particular, the logistic functions) and the consequences of using them as activation functions in neural networks.

Filter by
Sorted by
Tagged with
-1
votes
0answers
27 views

I'm designing a Neural Network in Matlab using the Sigmoid Function to complete a specific goal that should be trivial- and I am stuck

For a graduate school project, I'm crafting the Neural Netowrk that I breifly talked about in the title. I've been working on it for a while. This might sound contradictory, but I'm very happy with ...
3
votes
0answers
37 views

Why does sigmoid saturation prevent signal flow through the neuron?

As per these slides on page 35: Sigmoids saturate and kill gradients. when the neuron's activation saturates at either tail of 0 or 1, the gradient at these regions is almost zero. the gradient and ...
2
votes
0answers
26 views

In the Binary Flower Pollination Algorithm (using the sigmoid function), is it possible that no feature is selected?

I'm trying to use the Binary Flower Pollination Algorithm (BFPA) for feature selection. In the BFPA, the sigmoid function is used to compute a binary vector that represents whether a feature is ...
1
vote
2answers
78 views

How to use sigmoid as transfer function when input is not (0,1) range in ANN?

I am building my first ANN from scratch. I know that I need a transfer function and I want to use the sigmoid function as my teacher recommended that. That function can be between 0 and 1, but my ...
1
vote
2answers
57 views

Should I use additional empty category in some categorical problems?

I try to create autonomous car using keyboard data so this is a multi class classification problem. I have keys W,A,S and D. So I have four categories. My model should decide what key should be ...
0
votes
1answer
86 views

Which kind of data does sigmoid kernel performance well?

While I was playing with some hyperparameters, I came to a wired situation. My dataset is IRIS dataset to be specific. SVM algorithm has some hyperparameters that we can tune, such as Kernels, and C ...
2
votes
1answer
50 views

Accuracy dropped when I ran the program the second time

I was following a tutorial about Feed Forward Networks and wrote this code for a simple FFN : ...
3
votes
1answer
111 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....
0
votes
1answer
78 views

Is my backpropagation code correct? [closed]

I am trying to implement the back-propagation algorithm for the following neural network. ...
8
votes
1answer
578 views

Can neural networks with a sigmoid as the activation function of the output layer approximate continuous functions?

Neural networks are commonly used for classification tasks, in fact from this post it seems like that's where they shine brightest. However, when we want to classify using neural networks, we often ...
1
vote
0answers
35 views

Do L2 regularization and input normalization depend on sigmoid activation functions?

Following the online courses with Andrew Ng, he talks about L2 regularization (a.k.a. weight decay) and input normalization. Now, the argument is that L2 regularization make the weights smaller, ...
2
votes
1answer
46 views

How can I train a neural network for another input set, without losing the learning of the previous input set?

I read this tutorial about backpropagation. So using this backpropagation we are training the neural network repeatedly for one input set, say [2,4], until we reach 100% accuracy of getting 1 as ...
0
votes
1answer
215 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 ...
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 (...
2
votes
2answers
142 views

Why do non-linear activation functions not require a specific non-linear relation between its inputs and outputs?

A linear activation function (or none at all) should 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
544 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 "...
8
votes
3answers
995 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 ...