Hands-On Machine Learning with C#: Build smart, speedy, and reliable data-intensive applications using machine learning by R. Cole Matt
Author:R. Cole, Matt
Language: eng
Format: epub
Publisher: Packt Publishing
Published: 2018-05-24T16:00:00+00:00
// iterations
int i = 0;
// loop
while (!needToStop)
{
trainer.LearningRate = driftingLearningRate
* (iterations - i) / iterations + fixedLearningRate;
trainer.LearningRadius = (double)learningRadius *
(iterations - i) / iterations;
// run training epoch
trainer.RunEpoch(trainingSet);
// update map
UpdateMap(network);
// increase current iteration
i++;
// set current iteration's info
SetText(currentIterationBox, i.ToString());
// stop ?
if (i >= iterations)
break;
}
As we mentioned earlier, the LearningRate and LearningRadius continue to evolve through every iteration. This time, let's talk a bit about the RunEpoch method of the trainer. This method, although very simplistic, is designed to take a vector of input values and then return a learning error for that iteration (as you can now see, also sometimes called an epoch). It does this by calculating against each one of the input samples in the vector. The learning error is the absolute difference between the neurons' weights and inputs. The difference is measured according to the distance from the winning neuron. As mentioned earlier, we run this calculation against one learning iteration/epoch, find the winner, and update its weights (as well as neighbor weights). I should point out that when I say winner, I mean the neuron that has weights with values closest to the specified input vector, that is, the minimum distance from the network's input.
Next, we will highlight how we update the map itself; our calculated projects should match the initial input vector (points):
// get first layer
Layer layer = network.Layers[0];
Download
This site does not store any files on its server. We only index and link to content provided by other sites. Please contact the content providers to delete copyright contents if any and email us, we'll remove relevant links or contents immediately.
Algorithms of the Intelligent Web by Haralambos Marmanis;Dmitry Babenko(8293)
Test-Driven Development with Java by Alan Mellor(6679)
Data Augmentation with Python by Duc Haba(6582)
Principles of Data Fabric by Sonia Mezzetta(6345)
Learn Blender Simulations the Right Way by Stephen Pearson(6236)
Microservices with Spring Boot 3 and Spring Cloud by Magnus Larsson(6102)
Hadoop in Practice by Alex Holmes(5958)
Jquery UI in Action : Master the concepts Of Jquery UI: A Step By Step Approach by ANMOL GOYAL(5806)
RPA Solution Architect's Handbook by Sachin Sahgal(5501)
Big Data Analysis with Python by Ivan Marin(5338)
The Infinite Retina by Robert Scoble Irena Cronin(5199)
Life 3.0: Being Human in the Age of Artificial Intelligence by Tegmark Max(5142)
Pretrain Vision and Large Language Models in Python by Emily Webber(4299)
Infrastructure as Code for Beginners by Russ McKendrick(4061)
Functional Programming in JavaScript by Mantyla Dan(4037)
The Age of Surveillance Capitalism by Shoshana Zuboff(3946)
WordPress Plugin Development Cookbook by Yannick Lefebvre(3774)
Embracing Microservices Design by Ovais Mehboob Ahmed Khan Nabil Siddiqui and Timothy Oleson(3577)
Applied Machine Learning for Healthcare and Life Sciences Using AWS by Ujjwal Ratan(3553)
