class ExpConfig(Config):
# dataset configuration
dataset = "machine-1-1"
x_dim = get_data_dim(dataset)
# model architecture configuration
use_connected_z_q = True
use_connected_z_p = True
# model parameters
z_dim = 3
rnn_cell = 'GRU' # 'GRU', 'LSTM' or 'Basic'
rnn_num_hidden = 500
window_length = 100
dense_dim = 500
posterior_flow_type = 'nf' # 'nf' or None
nf_layers = 20 # for nf
max_epoch = 10
train_start = 0
max_train_size = None # `None` means full train set
batch_size = 50
l2_reg = 0.0001
initial_lr = 0.001
lr_anneal_factor = 0.5
lr_anneal_epoch_freq = 40
lr_anneal_step_freq = 400
std_epsilon = 1e-4
# evaluation parameters
test_n_z = 1024
test_batch_size = 50
test_start = 0
max_test_size = None # `None` means full test set
valid_step_freq = 100
gradient_clip_norm = 10.
early_stop = True # whether to apply early stop method
# pot parameters
# recommend values for `level`:
# SMAP: 0.07
# MSL: 0.01
# SMD group 1: 0.0050
# SMD group 2: 0.0075
# SMD group 3: 0.0001
level = 0.01
# outputs config
save_z = False # whether to save sampled z in hidden space
get_score_on_dim = False # whether to get score on dim. If `True`, the score will be a 2-dim ndarray
save_dir = 'model'
restore_dir = None # If not None, restore variables from this dir
result_dir = 'result' # Where to save the result file
train_score_filename = 'train_score.pkl'
test_score_filename = 'test_score.pkl'
var_dict = get_variables_as_dict(model_vs)
saver = VariableSaver(var_dict, config.save_dir)
saver.save()
print('=' * 30 + 'result' + '=' * 30)
pprint(best_valid_metrics)
if __name__ == '__main__':
# get config obj
config = ExpConfig()
# parse the arguments
arg_parser = ArgumentParser()
register_config_arguments(config, arg_parser)
arg_parser.parse_args(sys.argv[1:])
config.x_dim = get_data_dim(config.dataset)
print_with_title('Configurations', pformat(config.to_dict()), after='\n')
# open the result object and prepare for result directories if specified
results = MLResults(config.result_dir)
results.save_config(config) # save experiment settings for review
results.make_dirs(config.save_dir, exist_ok=True)
with warnings.catch_warnings():
# suppress DeprecationWarning from NumPy caused by codes in TensorFlow-Probability
warnings.filterwarnings("ignore",
category=DeprecationWarning,
module='numpy')
main()