mnist_rbm's Introduction
The first major win was to use the probability vectors for the hidden units as the representation the SVM is learned on. With 100 units, the RBM features had an accuracy of about 90-91% with an SVM versus 87-88% for just SVM. So clearly it learned and did something positive. This is when training the SVM on the entire dataset. If I restricted the labeled data to train on, I observed the RBM added more benefit. In terms of estimating the change in free energy, you can approach it two ways: (1) Approximate F(v_pos) by using a sampled hidden vector. Hinton says in the negative phase you might as well use the hidden probabilities to estimate F(v_neg). Hinton says don't use the hidden probabilities for the positive phase. (See below: he doesn't exactly say this). (2) Just exactly compute F(v_pos), F(v_neg), disregarding the hidden vectors you generated. Number two is what I do, and it is exactly what is suggested by Murphy's Machine Learning book algorithm 27.3. Note that the gradient of `F(v_pos) - F(v_neg)` is equal to the outer product of `v_pos` and `E(h|v)` minus `v_neg` and `E(h|v_neg)`. This is the contrastive divergence gradient. I misunderstood CD to be proposing the first approach. As a practical matter, on the small dataset I am working with, both approaches seem to work fine. It does feel like approach #1 is probably a consistent, if high variance, way of doing things. But I haven't proven that... Thank god this nightmare is over!
mnist_rbm's People
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
๐ Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. ๐๐๐
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google โค๏ธ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.