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Implementation of "Parameter Continuation Methods for the Optimization of Deep Neural Networks" accepted at ICMLA 19. https://ieeexplore.ieee.org/abstract/document/8999318

digitalcommons.wpi.edu/etd-theses/1256/
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7 Commits
arc
arc
 
 
utils
utils
 
 
README.md
README.md
 
 
ae.py
ae.py
 
 
example.json
example.json
 
 
mlp.py
mlp.py
 
 
npc.py
npc.py
 
 
npc_adap_mlp.py
npc_adap_mlp.py
 
 
npc_sigmoid_grad.py
npc_sigmoid_grad.py
 
 
npcs.py
npcs.py
 
 
ops2.py
ops2.py
 
 

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File ae.py contains the modal and TF graph.

File npc.py contains Natural parameter continuation (NPC) method (without secant).

File main.py contains NPCS method with secant.

File ops2.py contains basic operations.

File example.json has list of hyperparameter to tune.

Folder csmote has the code for Continuous-SMOTE.

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Implementation of "Parameter Continuation Methods for the Optimization of Deep Neural Networks" accepted at ICMLA 19. https://ieeexplore.ieee.org/abstract/document/8999318

digitalcommons.wpi.edu/etd-theses/1256/

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training deep-learning optimization continuation secant adam-optimizer autoen continat

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