deeplearning4j/deeplearning4j-examples

Increasing errors with MultiLayerNetworkExternalErrors.java Example

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#914 opened on Oct 18, 2019

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Description

Hi,

I am trying to using train a simple 2 layer network on a simple synthetic dataset. With two features X1 and X2. and lables: y = X1^2 + X2^2

I am calculating the error for each epoch manually and using the errors to call backprop, similar to the MultiLayerNetworkExternalErrors.java example

The prediction error is increasing with each epoch, I would have expected decreasing errors with on this simple dataset. Could someone help me with if the issue is with how I am using dl4j methods ?

Code: https://gist.github.com/Saurabh7/9b5ea7def2a167903e7d206e272e2662 Data: https://gist.github.com/Saurabh7/982fb4ddfbd58bd866782ea31b95aef1

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Research direction
Inspect the training loop; ensure the error calculation is correct and gradients are applied properly. Check learning rate and gradient clipping.
Tech stack
java
Domain
machine learning
Issue type
Bug
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
2
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1-3 hours
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Clear
Prerequisites
JavaNeural network basics
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60