config.yaml

# File I/O
data_folder: '/media/nnelsen/SharedHDD2TB/datasets/eit/'
SAVE_STR: "paper_sweep"
SAVE_AFTER: 10     # save to disk after this many epochs
FLAG_save_model: true

# Sample size
N_val: 100
N_test: 400
N_max: 10000

# Resolution subsampling
sub_in: 4       # input subsample factor (power of two) from s_max_out = 512
sub_out: 2      # output subsample factor (power of two) from s_max_out = 256
sub_in_test: 2
sub_out_test: 1

# FNO
modes1: 12         # default: 12
modes2: 12         # default: 12
width: 48          # default: 48
width_final: 256   # default: 256
act: 'relu'        # default: 'relu' for rough outputs, 'gelu' for smooth outputs
n_layers: 2        # default: 2

# Training, evaluation, and testing
batch_size: 32
epochs: 250
learning_rate: 8.0e-3
weight_decay: 1.0e-4
scheduler_step: 50
scheduler_gamma: 0.5
scheduler_iters: null
scheduler_patience: 5
scheduler_name: 'CosineAnnealingLR'        # 'CosineAnnealingLR' or 'StepLR'
FLAG_reduce: false                         # use ReduceLROnPlateau
FLAG_BEST: true                            # evaluate on best model if true; else eval on last epoch
FLAG_MEAN_REDUCTION: true                  # more stable to choice of batch size
FLAG_SHUFFLE: true
train_loss_str: "L1"
eval_loss_str_list: ["L1", "L2", "Ratio"]
noise_distribution: "gaussian"             # "gaussian" or "uniform"