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)
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)
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)
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)
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)
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)