alzheimers_nn/config.toml

[paths]
#mri_data = '/data/data_wnx1/_Data/AlzheimersDL/CNN+RNN-2class-1cnn+data/PET_volumes_customtemplate_float32/'
#xls_data = '/export/home/nschense/alzheimers/alzheimers_nn/LP_ADNIMERGE.csv'

mri_data = '../data/PET_volumes_customtemplate_float32/'
xls_data = '../data/LP_ADNIMERGE.csv'

#CHANGE THESE BEFORE RUNNING
model_output = '../models/'

[training]
device = 'mps' 
runs = 50 # Number of models
max_epochs = 30 # Epochs per model

[dataset]
excluded_ids = [91, 108, 268, 269, 272, 279, 293, 296, 307] # Invalid images to exclude from the dataset
validation_split = 0.4 #Splits the dataset into the train and validation/test set, 50% each for validation and test
#|---TEST---|---VALIDATION---|---TRAIN---|
#|splt*0.5  | split*0.5      | 1-split   |

[model]
name = 'cnn-50x30' # Ensemble name - will be folder for saved ensemble
image_channels = 1
clin_data_channels = 2

[hyperparameters]
batch_size = 32
learning_rate = 0.0001
droprate = 0.5

[operation]
silent = false
exclude_blank_ids = false # Determines if invalid IDs are excluded or not

[ensemble]
name = 'cnn-50x30'
prune_threshold = 0.0 # Any models with accuracy below this threshold will be pruned, set to 0 to disable pruning
individual_id = 1     # The id of the individual model to be used for the ensemble
run_models = false    # If true, the ensemble will run the models to generate the predictions, otherwise will load from file
excluded_ids = []     # List of data ids to be excluded from the ensemble