MNIST Digit Recognition

Fully connected neural networks for handwritten digit classification using MNIST data

Description

Layers : 3 ( 200, 200, 10 )
Optimization : AMSGrad
Weight Initialization : He-normal
Regularization : L2
Activation : ( elu, elu, softmax )
Cost Function : Categorical cross-entropy
Total epochs : 100
Batch size : 250

Performance

Here are some performance stats using various optimizers:

Optimization	Training Accuracy	Testing Accuracy
Momentum	0.586	0.584
RMSProp	0.9870	0.9679
AMSGrad	0.9975	0.9743
ADAM(TensorFlow)	0.9967	0.9735

Notes

• The latest commit has only AMSGrad. RMSProp and Momentum are present in previous commits.
• The tensorflow code was for me to see the kind of performance I should be replicating. It uses AdamOptimization and not AMSGrad.
• The performance stats aren't an average over x attempts, its just what I got running them once. Thus, they're not absolute or conclusive of anything and are subject to randoms. This point is specifically directed at my AMSGrad implementation vs Tensorflow's ADAM.

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This was a fun exercise for me to see the impact of various optimizers and needless to say, I was absolutely blown away by the boost in performance caused by RMSProp and AMSGrad.
As another fun exercise, I have updated the repository with the tenserflow eager implementation of equivalent ADAM code.