Python tpu.TPUEstimatorSpec方法代码示例

# 需要导入模块: from tensorflow.contrib import tpu [as 别名]
# 或者: from tensorflow.contrib.tpu import TPUEstimatorSpec [as 别名]
def model_fn_builder(bert_config, init_checkpoint, layer_indexes, use_tpu,
                     use_one_hot_embeddings):
  """Returns `model_fn` closure for TPUEstimator."""

  def model_fn(features, labels, mode, params):  # pylint: disable=unused-argument
    """The `model_fn` for TPUEstimator."""

    unique_ids = features["unique_ids"]
    input_ids = features["input_ids"]
    input_mask = features["input_mask"]
    input_type_ids = features["input_type_ids"]

    model = modeling.BertModel(
        config=bert_config,
        is_training=False,
        input_ids=input_ids,
        input_mask=input_mask,
        token_type_ids=input_type_ids,
        use_one_hot_embeddings=use_one_hot_embeddings)

    if mode != tf.estimator.ModeKeys.PREDICT:
      raise ValueError("Only PREDICT modes are supported: %s" % (mode))

    tvars = tf.trainable_variables()
    scaffold_fn = None
    (assignment_map,
     initialized_variable_names) = modeling.get_assignment_map_from_checkpoint(
         tvars, init_checkpoint)
    if use_tpu:

      def tpu_scaffold():
        tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
        return tf.train.Scaffold()

      scaffold_fn = tpu_scaffold
    else:
      tf.train.init_from_checkpoint(init_checkpoint, assignment_map)

    tf.logging.info("**** Trainable Variables ****")
    for var in tvars:
      init_string = ""
      if var.name in initialized_variable_names:
        init_string = ", *INIT_FROM_CKPT*"
      tf.logging.info("  name = %s, shape = %s%s", var.name, var.shape,
                      init_string)

    all_layers = model.get_all_encoder_layers()

    predictions = {
        "unique_id": unique_ids,
    }

    for (i, layer_index) in enumerate(layer_indexes):
      predictions["layer_output_%d" % i] = all_layers[layer_index]

    output_spec = contrib_tpu.TPUEstimatorSpec(
        mode=mode, predictions=predictions, scaffold_fn=scaffold_fn)
    return output_spec

  return model_fn 

# 需要导入模块: from tensorflow.contrib import tpu [as 别名]
# 或者: from tensorflow.contrib.tpu import TPUEstimatorSpec [as 别名]
def model_fn_builder(num_labels, learning_rate, num_train_steps,
                     num_warmup_steps, use_tpu, bert_hub_module_handle):
  """Returns `model_fn` closure for TPUEstimator."""

  def model_fn(features, labels, mode, params):  # pylint: disable=unused-argument
    """The `model_fn` for TPUEstimator."""

    tf.logging.info("*** Features ***")
    for name in sorted(features.keys()):
      tf.logging.info("  name = %s, shape = %s" % (name, features[name].shape))

    input_ids = features["input_ids"]
    input_mask = features["input_mask"]
    segment_ids = features["segment_ids"]
    label_ids = features["label_ids"]

    is_training = (mode == tf.estimator.ModeKeys.TRAIN)

    (total_loss, per_example_loss, logits, probabilities) = create_model(
        is_training, input_ids, input_mask, segment_ids, label_ids, num_labels,
        bert_hub_module_handle)

    output_spec = None
    if mode == tf.estimator.ModeKeys.TRAIN:
      train_op = optimization.create_optimizer(
          total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)

      output_spec = contrib_tpu.TPUEstimatorSpec(
          mode=mode, loss=total_loss, train_op=train_op)
    elif mode == tf.estimator.ModeKeys.EVAL:

      def metric_fn(per_example_loss, label_ids, logits):
        predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
        accuracy = tf.metrics.accuracy(label_ids, predictions)
        loss = tf.metrics.mean(per_example_loss)
        return {
            "eval_accuracy": accuracy,
            "eval_loss": loss,
        }

      eval_metrics = (metric_fn, [per_example_loss, label_ids, logits])
      output_spec = contrib_tpu.TPUEstimatorSpec(
          mode=mode, loss=total_loss, eval_metrics=eval_metrics)
    elif mode == tf.estimator.ModeKeys.PREDICT:
      output_spec = contrib_tpu.TPUEstimatorSpec(
          mode=mode, predictions={"probabilities": probabilities})
    else:
      raise ValueError(
          "Only TRAIN, EVAL and PREDICT modes are supported: %s" % (mode))

    return output_spec

  return model_fn