add input pipeline #1
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add get_d_prime add get_scalar_performance_matrices_2classes once and for all
…ur_classifier # Conflicts: # src/Hpopt.py # src/argument.py # src/classifier.py # src/cluster.py # src/cluster.sh # src/cluster_test.sh # src/dataio.py # src/exp_parameters.json # src/generate_json_for_cluster.py # src/model_parameters.json # src/plot.py # src/plot_aucs_offline.py # src/procedure.py # src/utils.py
add option from --jobid slurm submitting job plan to use noise-aug by default
try with markus .sh
add resplit dataset (except lout40-5) into 5 folds, patient-wise,
add merge 9 folds and resplit patient-wise
add yaml files changed parameter files to yaml
add randDA for noisy mnist add delete folder function seems all work. Too good with 80% noise
distill + DA with mnist works. only put file names in yaml
changed certain_file names shorter
move platform option to utils when load_all_params_yaml
patient-wise performance is done
add test with folder or specific pretrained dir
add args.lb_ind in load_noisy_mnist_data check sample in training compute simplified results_dir string
separate files for different plotting cases
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add the dataio. You can tell me whether it is alright and how to improve it. Glad to work with you on this project.