def define_cifar_flags(): common.define_keras_flags(dynamic_loss_scale=False) flags_core.set_defaults(data_dir='/tmp/cifar10_data/cifar-10-batches-bin', model_dir='/tmp/cifar10_model', epochs_between_evals=10, batch_size=128)
def define_imagenet_keras_flags(): common.define_keras_flags(model=True, optimizer=True, pretrained_filepath=True) common.define_pruning_flags() flags_core.set_defaults() flags.adopt_module_key_flags(common)
strategy, runnable.train, runnable.evaluate, global_step=runnable.global_step, steps_per_loop=steps_per_loop, train_steps=per_epoch_steps * train_epochs, checkpoint_manager=checkpoint_manager, summary_interval=summary_interval, eval_steps=eval_steps, eval_interval=eval_interval) time_callback.on_train_begin() resnet_controller.train(evaluate=not flags_obj.skip_eval) time_callback.on_train_end() stats = build_stats(runnable, time_callback) return stats def main(_): model_helpers.apply_clean(flags.FLAGS) with logger.benchmark_context(flags.FLAGS): stats = run(flags.FLAGS) logging.info('Run stats:\n%s', stats) if __name__ == '__main__': logging.set_verbosity(logging.INFO) common.define_keras_flags() app.run(main)
def setUpClass(cls): # pylint: disable=invalid-name super(CtlImagenetTest, cls).setUpClass() common.define_keras_flags()