Python optimization.create_optimizer() Examples

The following are 16 code examples of optimization.create_optimizer(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module optimization , or try the search function .
Example #1
Source File: run_classifier_with_tfhub.py    From Extending-Google-BERT-as-Question-and-Answering-model-and-Chatbot with Apache License 2.0 4 votes vote down vote up
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 = tf.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 = tf.contrib.tpu.TPUEstimatorSpec(
          mode=mode,
          loss=total_loss,
          eval_metrics=eval_metrics)
    elif mode == tf.estimator.ModeKeys.PREDICT:
      output_spec = tf.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 
Example #2
Source File: run_classifier_with_tfhub.py    From models with Apache License 2.0 4 votes vote down vote up
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.compat.v1.logging.info("*** Features ***")
    for name in sorted(features.keys()):
      tf.compat.v1.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 = tf.compat.v1.estimator.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(input=logits, axis=-1, output_type=tf.int32)
        accuracy = tf.compat.v1.metrics.accuracy(label_ids, predictions)
        loss = tf.compat.v1.metrics.mean(per_example_loss)
        return {
            "eval_accuracy": accuracy,
            "eval_loss": loss,
        }

      eval_metrics = (metric_fn, [per_example_loss, label_ids, logits])
      output_spec = tf.compat.v1.estimator.tpu.TPUEstimatorSpec(
          mode=mode,
          loss=total_loss,
          eval_metrics=eval_metrics)
    elif mode == tf.estimator.ModeKeys.PREDICT:
      output_spec = tf.compat.v1.estimator.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 
Example #3
Source File: run_classifier_with_tfhub.py    From models with Apache License 2.0 4 votes vote down vote up
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.compat.v1.logging.info("*** Features ***")
    for name in sorted(features.keys()):
      tf.compat.v1.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 = tf.compat.v1.estimator.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(input=logits, axis=-1, output_type=tf.int32)
        accuracy = tf.compat.v1.metrics.accuracy(label_ids, predictions)
        loss = tf.compat.v1.metrics.mean(per_example_loss)
        return {
            "eval_accuracy": accuracy,
            "eval_loss": loss,
        }

      eval_metrics = (metric_fn, [per_example_loss, label_ids, logits])
      output_spec = tf.compat.v1.estimator.tpu.TPUEstimatorSpec(
          mode=mode,
          loss=total_loss,
          eval_metrics=eval_metrics)
    elif mode == tf.estimator.ModeKeys.PREDICT:
      output_spec = tf.compat.v1.estimator.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 
Example #4
Source File: run_classifier_with_tfhub.py    From models with Apache License 2.0 4 votes vote down vote up
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.compat.v1.logging.info("*** Features ***")
    for name in sorted(features.keys()):
      tf.compat.v1.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 = tf.compat.v1.estimator.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(input=logits, axis=-1, output_type=tf.int32)
        accuracy = tf.compat.v1.metrics.accuracy(label_ids, predictions)
        loss = tf.compat.v1.metrics.mean(per_example_loss)
        return {
            "eval_accuracy": accuracy,
            "eval_loss": loss,
        }

      eval_metrics = (metric_fn, [per_example_loss, label_ids, logits])
      output_spec = tf.compat.v1.estimator.tpu.TPUEstimatorSpec(
          mode=mode,
          loss=total_loss,
          eval_metrics=eval_metrics)
    elif mode == tf.estimator.ModeKeys.PREDICT:
      output_spec = tf.compat.v1.estimator.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 
Example #5
Source File: run_classifier_with_tfhub.py    From text_bert_cnn with MIT License 4 votes vote down vote up
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) = 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 = tf.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 = tf.contrib.tpu.TPUEstimatorSpec(
          mode=mode,
          loss=total_loss,
          eval_metrics=eval_metrics)
    else:
      raise ValueError("Only TRAIN and EVAL modes are supported: %s" % (mode))

    return output_spec

  return model_fn 
Example #6
Source File: run_classifier_with_tfhub.py    From gobbli with Apache License 2.0 4 votes vote down vote up
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 = tf.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 = tf.contrib.tpu.TPUEstimatorSpec(
          mode=mode,
          loss=total_loss,
          eval_metrics=eval_metrics)
    elif mode == tf.estimator.ModeKeys.PREDICT:
      output_spec = tf.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 
Example #7
Source File: run_classifier_with_tfhub.py    From nlp_research with MIT License 4 votes vote down vote up
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 = tf.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 = tf.contrib.tpu.TPUEstimatorSpec(
          mode=mode,
          loss=total_loss,
          eval_metrics=eval_metrics)
    elif mode == tf.estimator.ModeKeys.PREDICT:
      output_spec = tf.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 
Example #8
Source File: run_classifier_with_tfhub.py    From bert with Apache License 2.0 4 votes vote down vote up
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 = tf.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 = tf.contrib.tpu.TPUEstimatorSpec(
          mode=mode,
          loss=total_loss,
          eval_metrics=eval_metrics)
    elif mode == tf.estimator.ModeKeys.PREDICT:
      output_spec = tf.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 
Example #9
Source File: run_classifier_with_tfhub.py    From uai-sdk with Apache License 2.0 4 votes vote down vote up
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 = tf.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 = tf.contrib.tpu.TPUEstimatorSpec(
          mode=mode,
          loss=total_loss,
          eval_metrics=eval_metrics)
    elif mode == tf.estimator.ModeKeys.PREDICT:
      output_spec = tf.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 
Example #10
Source File: run_classifier_with_tfhub.py    From delft with Apache License 2.0 4 votes vote down vote up
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 = tf.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 = tf.contrib.tpu.TPUEstimatorSpec(
          mode=mode,
          loss=total_loss,
          eval_metrics=eval_metrics)
    elif mode == tf.estimator.ModeKeys.PREDICT:
      output_spec = tf.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 
Example #11
Source File: run_classifier_with_tfhub.py    From QGforQA with MIT License 4 votes vote down vote up
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 = tf.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 = tf.contrib.tpu.TPUEstimatorSpec(
          mode=mode,
          loss=total_loss,
          eval_metrics=eval_metrics)
    elif mode == tf.estimator.ModeKeys.PREDICT:
      output_spec = tf.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 
Example #12
Source File: run_classifier_with_tfhub.py    From BERT-sentiment--classification with Apache License 2.0 4 votes vote down vote up
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 = tf.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 = tf.contrib.tpu.TPUEstimatorSpec(
          mode=mode,
          loss=total_loss,
          eval_metrics=eval_metrics)
    elif mode == tf.estimator.ModeKeys.PREDICT:
      output_spec = tf.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 
Example #13
Source File: run_classifier_with_tfhub.py    From pynlp with MIT License 4 votes vote down vote up
def model_fn_builder(num_labels, learning_rate, num_train_steps,
                     num_warmup_steps, use_tpu):
  """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) = create_model(
        is_training, input_ids, input_mask, segment_ids, label_ids, num_labels)

    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 = tf.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 = tf.contrib.tpu.TPUEstimatorSpec(
          mode=mode,
          loss=total_loss,
          eval_metrics=eval_metrics)
    else:
      raise ValueError("Only TRAIN and EVAL modes are supported: %s" % (mode))

    return output_spec

  return model_fn 
Example #14
Source File: run_classifier_with_tfhub.py    From BERT-for-Sequence-Labeling-and-Text-Classification with Apache License 2.0 4 votes vote down vote up
def model_fn_builder(num_labels, learning_rate, num_train_steps,
                     num_warmup_steps, use_tpu):
  """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) = create_model(
        is_training, input_ids, input_mask, segment_ids, label_ids, num_labels)

    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 = tf.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 = tf.contrib.tpu.TPUEstimatorSpec(
          mode=mode,
          loss=total_loss,
          eval_metrics=eval_metrics)
    else:
      raise ValueError("Only TRAIN and EVAL modes are supported: %s" % (mode))

    return output_spec

  return model_fn 
Example #15
Source File: run_classifier_with_tfhub.py    From MedicalRelationExtraction with MIT License 4 votes vote down vote up
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) = 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 = tf.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 = tf.contrib.tpu.TPUEstimatorSpec(
          mode=mode,
          loss=total_loss,
          eval_metrics=eval_metrics)
    else:
      raise ValueError("Only TRAIN and EVAL modes are supported: %s" % (mode))

    return output_spec

  return model_fn 
Example #16
Source File: run_classifier_with_tfhub.py    From coref with Apache License 2.0 4 votes vote down vote up
def model_fn_builder(num_labels, learning_rate, num_train_steps,
                     num_warmup_steps, use_tpu):
  """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) = create_model(
        is_training, input_ids, input_mask, segment_ids, label_ids, num_labels)

    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 = tf.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 = tf.contrib.tpu.TPUEstimatorSpec(
          mode=mode,
          loss=total_loss,
          eval_metrics=eval_metrics)
    else:
      raise ValueError("Only TRAIN and EVAL modes are supported: %s" % (mode))

    return output_spec

  return model_fn