def main(_):
hparams = infogan_eval_lib.HParams(
FLAGS.checkpoint_dir, FLAGS.eval_dir, FLAGS.noise_samples,
FLAGS.unstructured_noise_dims, FLAGS.continuous_noise_dims,
FLAGS.max_number_of_evaluations,
FLAGS.write_to_disk)
infogan_eval_lib.evaluate(hparams, run_eval_loop=True)
def test_build_graph(self):
hparams = infogan_eval_lib.HParams(checkpoint_dir='/tmp/mnist/',
eval_dir='/tmp/mnist/',
noise_samples=6,
unstructured_noise_dims=62,
continuous_noise_dims=2,
classifier_filename=None,
max_number_of_evaluations=None,
write_to_disk=True)
infogan_eval_lib.evaluate(hparams, run_eval_loop=False)
def test_build_graph(self):
if tf.executing_eagerly():
return
hparams = infogan_eval_lib.HParams(checkpoint_dir='/tmp/mnist/',
eval_dir='/tmp/mnist/',
noise_samples=6,
unstructured_noise_dims=62,
continuous_noise_dims=2,
max_number_of_evaluations=None,
write_to_disk=True)
infogan_eval_lib.evaluate(hparams, run_eval_loop=False)
