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)