Files

Confusion Matrices
Convolution_Images
DensiTDA
Gudhi Shape Dataset
Medical Dataset
SavedAverageImages
dataset
saved_feature_maps
saved_feature_maps_0_abs
saved_feature_maps_0_dual_vr
saved_feature_maps_1000_abs
saved_feature_maps_1000_dual_vr
saved_feature_maps_dual_h0_gudhi_abs
saved_feature_maps_dual_h0_gudhi_vr
saved_feature_maps_dual_h0_medical_abs
saved_feature_maps_dual_h0_medical_vr
.DS_Store
Dataset.png
F1Score.png
GNNsAndTDA.yml
Generating Gudhi Dataset.ipynb
Precision.png
README.md
Recall.png
Streamline Gudhi Generating Dataset.ipynb
TestAccuracy-Uniform-Nonuniform-Gudhi-Medical.png
TestAccuracy-Uniform-Nonuniform-Gudhi.png
TestAccuracy-Uniform.png
TestLoss.png
TrainAccuracy.png
TrainLoss.png
ValidAccuracy.png
ValidLoss.png
example_network.ipynb
feature_map_extraction.ipynb
feature_map_extraction_uniform.ipynb
graphs.ipynb
gudhi_baseline_dnn.ipynb
gudhi_cnn.ipynb
gudhi_cnn_combined.ipynb
gudhi_cnn_combined.pdf
gudhi_cnn_combined_test_code.ipynb
gudhi_cnn_combined_test_code_medical.ipynb
gudhi_trained_gudhi_medical_val_test.html
gudhi_trained_gudhi_medical_val_test.ipynb
gudhi_trained_gudhi_test.ipynb
gudhi_trained_medical_test.ipynb
image_test.ipynb
image_test_average.ipynb
nonuniform_gudhi_trained_nonuniform_gudhi_val_test.ipynb
test_metrics_dual_h0_lap_abstract_nonunif_gudhi.csv
test_metrics_dual_h0_lap_abstract_unif_nonunif_gudhi.csv
test_metrics_dual_h0_lap_abstract_unif_nonunif_gudhi_medical.csv
test_metrics_dual_h0_lap_vr_nonunif_gudhi.csv
test_metrics_dual_h0_lap_vr_unif_nonunif_gudhi.csv
test_metrics_dual_h0_lap_vr_unif_nonunif_gudhi_medical.csv
test_metrics_dual_h1_lap_abstract_nonunif_gudhi.csv
test_metrics_dual_h1_lap_vr_nonunif_gudhi.csv
test_metrics_dual_lap_abstract_gudhi.csv
test_metrics_dual_lap_abstract_gudhi_medical.csv
test_metrics_dual_lap_abstract_medical.csv
test_metrics_dual_lap_vr_gudhi.csv
test_metrics_dual_lap_vr_gudhi_medical.csv
test_metrics_dual_lap_vr_medical.csv
test_metrics_single_h0_lap_nonunif_gudhi.csv
test_metrics_single_h0_lap_unif_nonunif_gudhi.csv
test_metrics_single_h0_lap_unif_nonunif_gudhi_medical.csv
test_metrics_single_h1_lap_nonunif_gudhi.csv
test_metrics_single_laplacian_gudhi.csv
test_metrics_single_laplacian_gudhi_medical.csv
test_metrics_single_laplacian_medical.csv
unif_nonunif_gudhi_trained_medical_unif_nonunif_gudhi_val_test.ipynb
unif_nonunif_gudhi_trained_unif_nonunif_gudhi_val_test.ipynb

test_metrics_dual_lap_vr_gudhi_medical.csv

Systemize training & testing for reproducibility, run new viz

Nov 26, 2024

9ad0a53 · Nov 26, 2024

1	Epoch	Test Loss	Test Accuracy	Precision	Recall	F1 Score	Train Loss	Train Accuracy	Valid Loss	Valid Accuracy
2	1	0.03025187437962807	0.7651245551601423	0.6918238993710691	0.8661417322834646	0.7692307692307693	1.1042798908345868	0.78625	1.0417192785276308	0.7793594306049823
3	2	0.04132729221808184	0.8042704626334519	0.702247191011236	0.984251968503937	0.819672131147541	0.0025560235051671045	0.966875	1.519207834887008	0.7864768683274022
4	3	0.05784431059135943	0.8078291814946619	0.7039106145251397	0.9921259842519685	0.8235294117647058	0.001015301592851756	0.988125	2.1347097768318943	0.7864768683274022
5	4	0.05626648656792008	0.8113879003558719	0.7078651685393258	0.9921259842519685	0.8262295081967214	0.0008537578823597869	0.985625	2.076968246353014	0.7864768683274022
6	5	0.06083538251079381	0.8113879003558719	0.7078651685393258	0.9921259842519685	0.8262295081967214	0.0006282332700720872	0.99625	2.234333569274491	0.7864768683274022
7	6	0.05487823347820821	0.8113879003558719	0.7078651685393258	0.9921259842519685	0.8262295081967214	0.0005256189236126829	0.991875	2.0138188979100153	0.7864768683274022
8	7	0.049105165552422884	0.8113879003558719	0.7078651685393258	0.9921259842519685	0.8262295081967214	0.0007039826440268371	0.98625	1.8258617056756823	0.7864768683274022
9	8	0.05854861010240931	0.8078291814946619	0.7062146892655368	0.984251968503937	0.8223684210526315	0.0004492844494370729	0.991875	2.1752127000488852	0.7864768683274022
10	9	0.05391974106631912	0.8042704626334519	0.7045454545454546	0.9763779527559056	0.8184818481848185	0.00033451567022666493	0.995625	1.9956622126794374	0.7864768683274022
11	10	0.04556369493708008	0.8149466192170819	0.7218934911242604	0.9606299212598425	0.8243243243243243	0.0013288046314983148	0.97875	1.7114814657122932	0.800711743772242