Project 2 for CS 6375 - Machine Learning
- gh2.py (Provided by Professor) - Original file provided by the professor.
- our_mnist.py (Provided by Professor) - Modified MNIST data to only use 1500 pieces of data.
- script1.py - Modified gh2.py to use functions to dynamically create layers. Contains final parameters submitted for project.
- script1_checkLayerShapes.py - Given fixed parameters, iterate over different number of layers and number of nodes in each layer to determine optimal sizes of each for this specific problem.
- script1_automated.py - Iterate over different ranges of values for all hyperparameters and identify the optimal values for each hyperparameter.
- script2.py - Modified gh2.py to use functions to dynamically create layers. Contains final parameters submitted for project.
- script2_AdamcheckLayerShapes.py - Given fixed parameters, iterate over different number of layers and number of nodes in each layer to determine optimal sizes of each for this specific problem.
- script2_Adamautomated.py - Iterate over different ranges of values for all hyperparameters and identify the optimal values for each hyperparameter.
- OutputScript1AverageParametersTop5.xls - The top 5 average accuracies with corresponding parameters I achieved for Script 1 (Gradient Descent Optimizer). Average accuracies measured by running each configuration of hyperparameters 3 times and taking the average test accuracy.
- OutputScript1BestParametersTop5.xls - The top 5 best accuracies with corresponding parameters I achieved for Script 1 (Gradient Descent Optimizer).
- OutputScript2AverageParametersTop5.xls - The top 5 average accuracies with corresponding parameters I achieved for Script 2 (Adam Optimizer). Average accuracies measured by running each configuration of hyperparameters 3 times and taking the average test accuracy.
- OutputScript2BestParametersTop5.xls - The top 5 best accuracies with corresponding parameters I achieved for Script 2 (Adam Optimizer).