def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
logging.info('print this')
params = FLAGS.flag_values_dict()
tf.set_random_seed(params['seed'])
plt.rcParams['savefig.format'] = params['mpl_format']
params['results_dir'] = utils.make_subdir(params['results_dir'],
params['expname'])
params['figdir'] = utils.make_subdir(params['results_dir'], 'figs')
params['sampledir'] = utils.make_subdir(params['figdir'], 'samples')
params['ckptdir'] = utils.make_subdir(params['results_dir'], 'ckpts')
params['logdir'] = utils.make_subdir(params['results_dir'], 'logs')
params['tensordir'] = utils.make_subdir(params['results_dir'], 'tensors')
itr_train, itr_valid, itr_test = dataset_utils.load_dset_unsupervised()
conv_dims = [int(x) for x in params['conv_dims'].split(',')]
conv_sizes = [int(x) for x in params['conv_sizes'].split(',')]
vae = VAE(conv_dims, conv_sizes)
train_vae(vae, itr_train, itr_valid, params)
test_vae(vae, itr_test, params)
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
logging.info('print this')
params = FLAGS.flag_values_dict()
plt.rcParams['savefig.format'] = params['mpl_format']
params['results_dir'] = utils.make_subdir(params['results_dir'],
params['expname'])
params['figdir'] = utils.make_subdir(params['results_dir'], 'figs')
params['sampledir'] = utils.make_subdir(params['figdir'], 'samples')
params['ckptdir'] = utils.make_subdir(params['results_dir'], 'ckpts')
params['logdir'] = utils.make_subdir(params['results_dir'], 'logs')
params['tensordir'] = utils.make_subdir(params['results_dir'], 'tensors')
ood_classes = [int(x) for x in params['ood_classes'].split(',')]
# assume we train on all non-OOD classes
n_classes = 10
all_classes = range(n_classes)
ind_classes = [x for x in all_classes if x not in ood_classes]
(itr_train, itr_valid, itr_test,
itr_test_ood) = dataset_utils.load_dset_ood_unsupervised(
ind_classes, ood_classes)
conv_dims = [int(x) for x in params['conv_dims'].split(',')]
conv_sizes = [int(x) for x in params['conv_sizes'].split(',')]
vae = VAE(conv_dims, conv_sizes)
run_vae_mnist.train_vae(vae, itr_train, itr_valid, params)
run_vae_mnist.test_vae(vae, itr_test, params)
params['tensordir'] = utils.make_subdir(params['results_dir'],
'ood_tensors')
run_vae_mnist.test_vae(vae, itr_test_ood, params)