Python FlowersData Examples

Example #1

File: flowers_eval.py Project: xiaohong404/transferability-advdnn-pub

def main(unused_argv=None):
    dataset = FlowersData(subset=FLAGS.subset)
    assert dataset.data_files()
    if tf.gfile.Exists(FLAGS.eval_dir):
        tf.gfile.DeleteRecursively(FLAGS.eval_dir)
    tf.gfile.MakeDirs(FLAGS.eval_dir)
    inception_eval.evaluate(dataset)

Example #2

File: flowers_train.py Project: notlixiang/tensorflow

def main(_):
    dataset = FlowersData(subset=FLAGS.subset)
    assert dataset.data_files()
    if tf.gfile.Exists(FLAGS.train_dir):
        tf.gfile.DeleteRecursively(FLAGS.train_dir)
    tf.gfile.MakeDirs(FLAGS.train_dir)
    inception_train.train(dataset)

Example #3

File: flowers_distributed_train.py Project: Joranson/modifiedInception

def main(unused_args):
    assert FLAGS.job_name in ['ps', 'worker'], 'job_name must be ps or worker'

    # Extract all the hostnames for the ps and worker jobs to construct the
    # cluster spec.
    ps_hosts = FLAGS.ps_hosts.split(',')
    worker_hosts = FLAGS.worker_hosts.split(',')
    tf.logging.info('PS hosts are: %s' % ps_hosts)
    tf.logging.info('Worker hosts are: %s' % worker_hosts)

    cluster_spec = tf.train.ClusterSpec({
        'ps': ps_hosts,
        'worker': worker_hosts
    })
    server = tf.train.Server({
        'ps': ps_hosts,
        'worker': worker_hosts
    },
                             job_name=FLAGS.job_name,
                             task_index=FLAGS.task_id)

    if FLAGS.job_name == 'ps':
        # `ps` jobs wait for incoming connections from the workers.
        server.join()
    else:
        # `worker` jobs will actually do the work.
        dataset = FlowersData(subset=FLAGS.subset)
        assert dataset.data_files()
        # Only the chief checks for or creates train_dir.
        if FLAGS.task_id == 0:
            if not tf.gfile.Exists(FLAGS.train_dir):
                tf.gfile.MakeDirs(FLAGS.train_dir)
        inception_distributed_train.train(server.target, dataset, cluster_spec)

Example #4

File: flowers_train.py Project: 1206lyp/models

def main(_):
  dataset = FlowersData(subset=FLAGS.subset)
  assert dataset.data_files()
  if tf.gfile.Exists(FLAGS.train_dir):
    tf.gfile.DeleteRecursively(FLAGS.train_dir)
  tf.gfile.MakeDirs(FLAGS.train_dir)
  inception_train.train(dataset)

Example #5

File: flowers_eval.py Project: jwhpryor/models

def main_orig(unused_argv=None):
  dataset = FlowersData(subset=FLAGS.subset)
  assert dataset.data_files()
  if tf.gfile.Exists(FLAGS.eval_dir):
    tf.gfile.DeleteRecursively(FLAGS.eval_dir)
  tf.gfile.MakeDirs(FLAGS.eval_dir)
  inception_eval.evaluate(dataset)

Example #6

File: flowers_train.py Project: icodingc/FinalProject

def main(_):
    dataset = FlowersData(subset=FLAGS.subset)
    assert dataset.data_files()
    if tf.gfile.Exists(FLAGS.train_dir):
        print('Dir already exits...')
        sys.exit()
        tf.gfile.DeleteRecursively(FLAGS.train_dir)
    tf.gfile.MakeDirs(FLAGS.train_dir)
    inception_train.train(dataset)

Example #7

File: flowers_train.py Project: jwhpryor/models

def main(_):
  dataset = FlowersData(subset='train')
  validation_dataset = FlowersData(subset='validation')
  assert dataset.data_files()
  if tf.gfile.Exists(FLAGS.train_dir):
    print('WARNING:  About to delete active train directory...')
    time.sleep(3)
    tf.gfile.DeleteRecursively(FLAGS.train_dir)
  tf.gfile.MakeDirs(FLAGS.train_dir)
  inception_train.train(dataset, validation_dataset)