Learn more about this project from this blog post:
This project provides a implementation for a Restricted Boltzmann Machine and a Deep Belief Network
It uses the MNIST handwritten dataset to illistrate an example RBM and DBN.
From source build the project with maven:
- mvn deploy
This will build a single jar and download the mnist dataset.
- java -jar target/rbm-dbn-mnist-0.0.1.jar
Runs the app. shows the usage screen
Usage: [rbm minst-labels.gz minst-images.gz] [dbn minst-images.gz minst-labels.gz dbn.bin] [gen dbn.bin]
- java -jar target/rbm-dbn-mnist-0.0.1.jar rbm target/minst/train-labels-idx1-ubyte.gz target/minst/train-images-idx3-ubyte.gz
Trains a single RBM with 100 hidden nodes. Each of the hidden nodes weights are rendered alongside the test digit in blue.
- java -jar target/rbm-dbn-mnist-0.0.1.jar dbn target/minst/train-labels-idx1-ubyte.gz target/minst/train-images-idx3-ubyte.gz /tmp/dbn.bin
Trains a Deep Belief Network made up of three RBMs. It learns to match pictures of digits with their corresponding label. It takes about 10m to train but once it's done it has ~95% accuracy rate. The trained DBN is saved to a file.
- java -jar target/rbm-dbn-mnist-0.0.1.jar gen /tmp/dbn.bin
Takes the trained DBN from step 4. and reverses the flow, generating a visual image of a digit from a digit label.
Copyright 2013 T Jake Luciani firstname.lastname@example.org
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The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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