(master,
                     model_dir,
                     use_tpu,
                     iterations_per_loop,
                     num_shards,
                     model_params,
                     include_features_in_predictions=True,
                     decode_keys=(),
                     train_init_checkpoint=None,
                     train_warmup_steps=10000,
                     save_checkpoints_steps=1000,
                     keep_checkpoint_max=5)
return tpu_estimator.TPUEstimator(
      model_fn=_estimator_model_fn(use_tpu, model_params, model_dir,
                                   include_features_in_predictions, decode_keys,
                                   train_init_checkpoint, train_warmup_steps),
      use_tpu=use_tpu,
      train_batch_size=model_params.batch_size * num_shards,
      eval_batch_size=model_params.batch_size * num_shards,
      predict_batch_size=model_params.batch_size * num_shards,
      config=run_config)
Returns an tensorflow estimator.
Returns an tensorflow estimator.
Returns an tensorflow estimator .
def create_estimator(master,
                     model_dir,
                     use_tpu,
                     iterations_per_loop,
                     num_shards,
                     model_params,
                     include_features_in_predictions=True,
                     decode_keys=(),
                     train_init_checkpoint=None,
                     train_warmup_steps=10000,
                     save_checkpoints_steps=1000,
                     keep_checkpoint_max=5):
  """Returns an tensorflow estimator."""

  run_config = tpu_config.RunConfig(
      master=master,
      model_dir=model_dir,
      session_config=tf.ConfigProto(
          allow_soft_placement=True, log_device_placement=False),
      tpu_config=tpu_config.TPUConfig(iterations_per_loop),
      save_checkpoints_steps=save_checkpoints_steps,
      keep_checkpoint_max=keep_checkpoint_max)

  return tpu_estimator.TPUEstimator(
      model_fn=_estimator_model_fn(use_tpu, model_params, model_dir,
                                   include_features_in_predictions, decode_keys,
                                   train_init_checkpoint, train_warmup_steps),
      use_tpu=use_tpu,
      train_batch_size=model_params.batch_size * num_shards,
      eval_batch_size=model_params.batch_size * num_shards,
      predict_batch_size=model_params.batch_size * num_shards,
      config=run_config)
def create_estimator ( master , model_dir , use_tpu , iterations_per_loop , num_shards , model_params , include_features_in_predictions = True , decode_keys = ( ) , train_init_checkpoint = None , train_warmup_steps = 10000 , save_checkpoints_steps = 1000 , keep_checkpoint_max = 5 ) : run_config = tpu_config . RunConfig ( master = master , model_dir = model_dir , session_config = tf . ConfigProto ( allow_soft_placement = True , log_device_placement = False ) , tpu_config = tpu_config . TPUConfig ( iterations_per_loop ) , save_checkpoints_steps = save_checkpoints_steps , keep_checkpoint_max = keep_checkpoint_max ) return tpu_estimator . TPUEstimator ( model_fn = _estimator_model_fn ( use_tpu , model_params , model_dir , include_features_in_predictions , decode_keys , train_init_checkpoint , train_warmup_steps ) , use_tpu = use_tpu , train_batch_size = model_params . batch_size * num_shards , eval_batch_size = model_params . batch_size * num_shards , predict_batch_size = model_params . batch_size * num_shards , config = run_config )