def run(hparams, run_dir): """Run train/eval/test.""" train_dir = os.path.join(run_dir, 'train') if FLAGS.mode == 'eval': eval_dir = os.path.join(run_dir, 'eval') if FLAGS.eval_dir: eval_dir = os.path.join(eval_dir, FLAGS.eval_dir) train_util.evaluate( train_dir=train_dir, eval_dir=eval_dir, examples_path=FLAGS.examples_path, num_batches=FLAGS.eval_num_batches, hparams=hparams) elif FLAGS.mode == 'test': checkpoint_path = (os.path.expanduser(FLAGS.checkpoint_path) if FLAGS.checkpoint_path else tf.train.latest_checkpoint(train_dir)) tf.logging.info('Testing with checkpoint: %s', checkpoint_path) test_dir = os.path.join(run_dir, 'test') train_util.test( checkpoint_path=checkpoint_path, test_dir=test_dir, examples_path=FLAGS.examples_path, num_batches=FLAGS.eval_num_batches, hparams=hparams) elif FLAGS.mode == 'train': train_util.train( train_dir=train_dir, examples_path=FLAGS.examples_path, hparams=hparams, checkpoints_to_keep=FLAGS.checkpoints_to_keep, num_steps=FLAGS.num_steps) else: raise ValueError('Invalid mode: {}'.format(FLAGS.mode))
def run(hparams, run_dir): """Run train/eval/test.""" train_dir = os.path.join(run_dir, 'train') if FLAGS.mode == 'eval': eval_dir = os.path.join(run_dir, 'eval') if FLAGS.eval_dir: eval_dir = os.path.join(eval_dir, FLAGS.eval_dir) train_util.evaluate(train_dir=train_dir, eval_dir=eval_dir, examples_path=FLAGS.examples_path, num_batches=FLAGS.eval_num_batches, hparams=hparams) elif FLAGS.mode == 'test': checkpoint_path = (os.path.expanduser(FLAGS.checkpoint_path) if FLAGS.checkpoint_path else tf.train.latest_checkpoint(train_dir)) tf.logging.info('Testing with checkpoint: %s', checkpoint_path) test_dir = os.path.join(run_dir, 'test') train_util.test(checkpoint_path=checkpoint_path, test_dir=test_dir, examples_path=FLAGS.examples_path, num_batches=FLAGS.eval_num_batches, hparams=hparams) elif FLAGS.mode == 'train': train_util.train(train_dir=train_dir, examples_path=FLAGS.examples_path, hparams=hparams, checkpoints_to_keep=FLAGS.checkpoints_to_keep, num_steps=FLAGS.num_steps) else: raise ValueError('Invalid mode: {}'.format(FLAGS.mode))
def run(config_map, data_fn, additional_trial_info): """Run training or evaluation.""" tf.logging.set_verbosity(FLAGS.log) config = config_map[FLAGS.config] model_dir = os.path.expanduser(FLAGS.model_dir) hparams = config.hparams # Command line flags override any of the preceding hyperparameter values. hparams.parse(FLAGS.hparams) if FLAGS.mode == 'train': train_util.train(model_fn=config.model_fn, data_fn=data_fn, additional_trial_info=additional_trial_info, master=FLAGS.master, model_dir=model_dir, use_tpu=FLAGS.use_tpu, preprocess_examples=FLAGS.preprocess_examples, hparams=hparams, keep_checkpoint_max=FLAGS.keep_checkpoint_max, num_steps=FLAGS.num_steps) elif FLAGS.mode == 'eval': train_util.evaluate(model_fn=config.model_fn, data_fn=data_fn, additional_trial_info=additional_trial_info, master=FLAGS.master, model_dir=model_dir, name=FLAGS.eval_name, preprocess_examples=FLAGS.preprocess_examples, hparams=hparams, num_steps=FLAGS.eval_num_steps) else: raise ValueError('Unknown/unsupported mode: %s' % FLAGS.mode)
def run(hparams, model_dir): """Run train/eval/test.""" train_util.train( model_dir=model_dir, examples_path=FLAGS.examples_path, hparams=hparams, keep_checkpoint_max=FLAGS.keep_checkpoint_max, num_steps=FLAGS.num_steps)
def run(hparams, model_dir): """Run train/eval/test.""" train_util.train( master=FLAGS.master, model_dir=model_dir, examples_path=FLAGS.examples_path, hparams=hparams, keep_checkpoint_max=FLAGS.keep_checkpoint_max, num_steps=FLAGS.num_steps)
def run(config_map, semisupervised_examples_map=None): """Run training or evaluation.""" tf.logging.set_verbosity(FLAGS.log) # Validate data path flags. if not FLAGS.examples_path and not FLAGS.semisupervised_examples_config: raise ValueError('You must set flags for either `examples_path` or ' '`semisupervised_examples_config`.') if FLAGS.examples_path and FLAGS.semisupervised_examples_config: raise ValueError('You must only set one of either `examples_path` or ' '`semisupervised_examples_config`.') if not FLAGS.examples_path and FLAGS.mode == 'eval': raise ValueError('You must set flags for `examples_path` if in eval mode.') semisupervised_configs = ( semisupervised_examples_map[FLAGS.semisupervised_examples_config] if FLAGS.semisupervised_examples_config and semisupervised_examples_map else None) config = config_map[FLAGS.config] model_dir = os.path.expanduser(FLAGS.model_dir) hparams = config.hparams # Command line flags override any of the preceding hyperparameter values. hparams.parse(FLAGS.hparams) if FLAGS.mode == 'train': train_util.train( model_fn=config.model_fn, master=FLAGS.master, model_dir=model_dir, use_tpu=FLAGS.use_tpu, examples_path=FLAGS.examples_path, preprocess_examples=FLAGS.preprocess_examples, hparams=hparams, keep_checkpoint_max=FLAGS.keep_checkpoint_max, num_steps=FLAGS.num_steps, semisupervised_configs=semisupervised_configs) elif FLAGS.mode == 'eval': train_util.evaluate( model_fn=config.model_fn, master=FLAGS.master, model_dir=model_dir, name=FLAGS.eval_name, examples_path=FLAGS.examples_path, preprocess_examples=FLAGS.preprocess_examples, hparams=hparams, num_steps=FLAGS.eval_num_steps) else: raise ValueError('Unknown/unsupported mode: %s' % FLAGS.mode)
def run(hparams, run_dir): """Run train/eval/test.""" train_dir = os.path.join(run_dir, 'train') if FLAGS.mode == 'eval': eval_dir = os.path.join(run_dir, 'eval') if FLAGS.eval_dir: eval_dir = os.path.join(eval_dir, FLAGS.eval_dir) train_util.evaluate( train_dir=train_dir, eval_dir=eval_dir, examples_path=FLAGS.examples_path, num_batches=FLAGS.eval_num_batches, hparams=hparams, master=FLAGS.master) elif FLAGS.mode == 'test': checkpoint_path = tf.train.latest_checkpoint(train_dir) if FLAGS.checkpoint_path: checkpoint_path = os.path.expanduser(FLAGS.checkpoint_path) tf.logging.info('Testing with checkpoint: %s', checkpoint_path) test_dir = os.path.join(run_dir, 'test') train_util.test( checkpoint_path=checkpoint_path, test_dir=test_dir, examples_path=FLAGS.examples_path, num_batches=FLAGS.eval_num_batches, hparams=hparams, master=FLAGS.master) elif FLAGS.mode == 'train': train_util.train( train_dir=train_dir, examples_path=FLAGS.examples_path, hparams=hparams, checkpoints_to_keep=FLAGS.checkpoints_to_keep, num_steps=FLAGS.num_steps, master=FLAGS.master, task=FLAGS.ps_task, num_ps_tasks=FLAGS.num_ps_tasks)
def run(hparams, run_dir): """Run train/eval/test.""" train_dir = os.path.join(run_dir, 'train') if FLAGS.mode == 'eval': eval_dir = os.path.join(run_dir, 'eval') if FLAGS.eval_dir: eval_dir = os.path.join(eval_dir, FLAGS.eval_dir) train_util.evaluate(train_dir=train_dir, eval_dir=eval_dir, examples_path=FLAGS.examples_path, num_batches=FLAGS.eval_num_batches, hparams=hparams, master=FLAGS.master) elif FLAGS.mode == 'test': checkpoint_path = tf.train.latest_checkpoint(train_dir) if FLAGS.checkpoint_path: checkpoint_path = os.path.expanduser(FLAGS.checkpoint_path) tf.logging.info('Testing with checkpoint: %s', checkpoint_path) test_dir = os.path.join(run_dir, 'test') train_util.test(checkpoint_path=checkpoint_path, test_dir=test_dir, examples_path=FLAGS.examples_path, num_batches=FLAGS.eval_num_batches, hparams=hparams, master=FLAGS.master) elif FLAGS.mode == 'train': train_util.train(train_dir=train_dir, examples_path=FLAGS.examples_path, hparams=hparams, checkpoints_to_keep=FLAGS.checkpoints_to_keep, num_steps=FLAGS.num_steps, master=FLAGS.master, task=FLAGS.ps_task, num_ps_tasks=FLAGS.num_ps_tasks)