default.conf

# saving
trained_model_path = trained_models
save_trained = True
n_saved = 2
load_name = capsnet__3.pth
load_model = False
save_best = True
score_file_name = best_acc
model_name = capsnet
exp_name = test_experiment

# batch related
epochs = 100
batch_size = 128
drop_last = True
shuffle = True
seed = 42
debug = False
valid_size = 0.1

# logging
print_time = True
log_file = False
log_file_name = None
use_visdom = True
start_visdom = True

# training
early_stop = False
cudnn_benchmark = True
learning_rate = 0.001           # source code uses a exponential_decay, not an option in torch's Adam https://discuss.pytorch.org/t/adaptive-learning-rate/320/9
dataset = mnist

# margin loss
use_recon = True    # use reconstruction in the loss, author code suggest no reconstruction for cifar10
alpha = 0.0005      # recon scalar
m_plus = 0.9        # presences factor
m_min = 0.1         # absence factor

# network
stdev_W = 0.1
architecture = 32,8;10,16
routing_iters = 3
bias_routing = False        # author  code has bias True