L-VAE-MNIST.txt

# python code/generation/train.py -c generation/config/L-VAE/L-VAE-MNIST.txt

# Output settings
exp_name = L-VAE-MNIST
#output_dir = generation/output

# General settings
device = cuda:0
dtype = float32
seed = 1
#load_checkpoint = generation/output/L-VAE-MNIST

# General training hyperparameters
num_epochs = 100
batch_size = 100
lr = 5e-4
weight_decay = 0
optimizer = RiemannianAdam
use_lr_scheduler = True
lr_scheduler_step = 50
lr_scheduler_gamma = 0.1

# KL Loss hyperparameters
kl_coeff = 0.312

# General validation/testing hyperparameters
batch_size_test = 128
calc_fid_val = True
batch_size_fid_inception = 512

# General VAE hyperparameters
model = L-VAE
enc_layers = 4
dec_layers = 3
z_dim = 2
initial_filters = 64

# Hyperbolic manifold settings
#learn_K = True

# HVAE
enc_K = 1.0
dec_K = 1.0

# Dataset settings
dataset = MNIST