alnn_rewrite/config.toml

# Pipline Scenario to run
# "scen_train_all" - Train and evaluate model, with noise
# "scen_load_all"  - Load and evaluate model, with noise
# "scen_load_eval" - Load and evaluate model, without noise
# "scen_analyze"   - Analyze existing evaluation files only (no training/eval)
scenario = "scen_train_all"


[run]
# Script/automation options. Leave both false for normal interactive use.
autorun = false          # start the pipeline automatically once the config loads
autoquit = false         # exit the app when the pipeline finishes (0/1 return code)
# In autorun, allow overwriting PROTECTED outputs (models/evaluations) without a
# prompt. Not needed for "scen_analyze", which only writes regeneratable analysis
# artifacts and therefore never triggers the overwrite guard.
force_overwrite = false

[data]
mri_files_path = "../data/PET_volumes_customtemplate_float32/"
xls_file_path = "../data/LP_ADNIMERGE.csv"
seed = 42
data_splits = [0.7, 0.2, 0.1] # train, validation, test
image_channels = 1
clin_data_channels = 2
num_classes = 2 # AD, NL


[training]
device = "cuda:0" # "cpu", "cuda", "mps"
batch_size = 32
ensemble_size = 30
droprate = 0.05
learning_rate = 0.0001
num_epochs = 25
deterministic = false # force deterministic cuDNN kernels (slower, exact reproduction)


[evaluation]
# Gaussian noise standard deviations applied to images during noisy evaluation
# (step 6). 0.0 is the clean baseline and should be kept first.
noise_levels = [0.0, 0.02, 0.05, 0.1, 0.2]
# Monte-Carlo forward passes used to estimate Bayesian predictive/model
# uncertainty (step 5/6). Ignored for the deterministic ensemble.
mc_passes = 30