Timeline for How to determine bias in simple neural network
Current License: CC BY-SA 3.0
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Jun 1, 2018 at 12:30 | comment | added | Martian | My understanding for your case is to set bias as - average(input_values). When doing the learning, calculate the average first and set that as bias of the input layer. | |
Jul 3, 2017 at 14:19 | comment | added | seanv507 | So your single neuron network can never recreate the linear function y= x if you use a sigmoid. given this is just a test you should just create targets y=sigmoid (a x + b.bias) where you fix a and b and check you can recover the weights a and b by gradient descent. if you wanted to recreate the identify function, either you need an extra linear output neuron or you change to use linear neuron. | |
Jul 3, 2017 at 12:17 | comment | added | ljeabmreosn | @seanv507 okay, so in the future when my network contains more layers, should I add a bias term to each layer? Also, does the value of the bias term matter in this case (and its initial weight)? | |
Jul 3, 2017 at 8:44 | comment | added | Thomas Wagenaar | you have to backpropagate the bias value as well | |
Jul 3, 2017 at 7:06 | comment | added | seanv507 | you need a weight for the bias term too ( which gets adjusted by backprop too) | |
Jul 3, 2017 at 4:11 | review | First posts | |||
Jul 3, 2017 at 5:32 | |||||
Jul 3, 2017 at 4:07 | history | asked | ljeabmreosn | CC BY-SA 3.0 |