Analyzing Network Output - Part 1, Training and Saving

Posted on Thu 12 April 2018 in Tutorial by Corey Adams
Tagged with MNIST, training, saving, minibatching, batch norm

I train a very simple and basic mnist classification network with a lot of overkill: I use minibatching, batch normalization, and save the network weights to disk. This tutorial can be done on a CPU. In Part 2, I restore the model, run on the validation set, and analyze the results.


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Analyzing Network Output - Part 2, Restoring and Analyzing

Posted on Thu 12 April 2018 in Tutorial by Corey Adams
Tagged with MNIST, retoring, analysis, minibatching, batch norm

See Part 1 first! There, I trained an mnist classifier. Here, I restore the trained network, run the network on the validation script, and do some analysis on the output. This is meant as a template for new users to see "how do I actually use a trained network?" Lots of this information exists elsewhere too, I've just tried to consolidate the basics.


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Tutorials on Colaboratory

Posted on Fri 02 March 2018 in tutorial by Kazuhiro Terao
Tagged with python tutorial, DL tutorial, colaboratory, tensorflow

List of tutorials covering basics of tensorflow, slim, image classification and semantic segmentation using MNIST images. All notebooks can be run on free Google colaboratory with GPU. No need to own your own GPU machine!


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PyTorch/LArCV Classification Example with Data Set (v0.1.0)

Posted on Tue 09 January 2018 in tutorial by Taritree
Tagged with resnet, pytorch, classification, example

An example of training a classification network on the 5-particle LArCV training data using pytorch.


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Getting started: paper readings

Posted on Tue 19 December 2017 in tutorial by Kazuhiro Terao
Tagged with paper

Useful readings for (new) group members.


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Tutorials

Posted on Tue 19 December 2017 in tutorial by Kazuhiro Terao
Tagged with python tutorial, larcv tutorial, public data

List of tutorials covering software installations, training a network using (public) larcv data files, and developing your own code in larcv.


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