Cielciel/aift-model-review-multiple-label-classification

bbc5ecf almost 3 years ago

2.9 kB

# Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the \"License\");
# you may not use this file except in compliance with the License.\n",
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an \"AS IS\" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from trainer import experiment


def get_args():
    """Define the task arguments with the default values.
    Returns:
        experiment parameters
    """
    args_parser = argparse.ArgumentParser()


    # Experiment arguments
    args_parser.add_argument(
        '--batch-size',
        help='Batch size for each training and evaluation step.',
        type=int,
        default=16)
    args_parser.add_argument(
        '--num-epochs',
        help="""\
        Maximum number of training data epochs on which to train.
        If both --train-size and --num-epochs are specified,
        --train-steps will be: (train-size/train-batch-size) * num-epochs.\
        """,
        default=1,
        type=int,
    )
    args_parser.add_argument(
        '--seed',
        help='Random seed (default: 42)',
        type=int,
        default=42,
    )

    # Estimator arguments
    args_parser.add_argument(
        '--learning-rate',
        help='Learning rate value for the optimizers.',
        default=2e-5,
        type=float)
    args_parser.add_argument(
        '--weight-decay',
        help="""
      The factor by which the learning rate should decay by the end of the
      training.
      decayed_learning_rate =
        learning_rate * decay_rate ^ (global_step / decay_steps)
      If set to 0 (default), then no decay will occur.
      If set to 0.5, then the learning rate should reach 0.5 of its original
          value at the end of the training.
      Note that decay_steps is set to train_steps.
      """,
        default=0.01,
        type=float)

    # Enable hyperparameter
    args_parser.add_argument(
        '--hp-tune',
        default="n",
        help='Enable hyperparameter tuning. Valida values are: "y" - enable, "n" - disable')
    
    # Saved model arguments
    args_parser.add_argument(
        '--job-dir',
        default=os.getenv('AIP_MODEL_DIR'),
        help='GCS location to export models')
    args_parser.add_argument(
        '--model-name',
        default="finetuned-bert-classifier",
        help='The name of your saved model')

    return args_parser.parse_args()


def main():
    """Setup / Start the experiment
    """
    args = get_args()
    print(args)
    experiment.run(args)


if __name__ == '__main__':
    main()

	# Copyright 2019 Google LLC
	#
	# Licensed under the Apache License, Version 2.0 (the \"License\");
	# you may not use this file except in compliance with the License.\n",
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an \"AS IS\" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.

	import argparse
	import os

	from trainer import experiment


	def get_args():
	"""Define the task arguments with the default values.

	Returns:
	experiment parameters
	"""
	args_parser = argparse.ArgumentParser()


	# Experiment arguments
	args_parser.add_argument(
	'--batch-size',
	help='Batch size for each training and evaluation step.',
	type=int,
	default=16)
	args_parser.add_argument(
	'--num-epochs',
	help="""\
	Maximum number of training data epochs on which to train.
	If both --train-size and --num-epochs are specified,
	--train-steps will be: (train-size/train-batch-size) * num-epochs.\
	""",
	default=1,
	type=int,
	)
	args_parser.add_argument(
	'--seed',
	help='Random seed (default: 42)',
	type=int,
	default=42,
	)

	# Estimator arguments
	args_parser.add_argument(
	'--learning-rate',
	help='Learning rate value for the optimizers.',
	default=2e-5,
	type=float)
	args_parser.add_argument(
	'--weight-decay',
	help="""
	The factor by which the learning rate should decay by the end of the
	training.

	decayed_learning_rate =
	learning_rate * decay_rate ^ (global_step / decay_steps)

	If set to 0 (default), then no decay will occur.
	If set to 0.5, then the learning rate should reach 0.5 of its original
	value at the end of the training.
	Note that decay_steps is set to train_steps.
	""",
	default=0.01,
	type=float)

	# Enable hyperparameter
	args_parser.add_argument(
	'--hp-tune',
	default="n",
	help='Enable hyperparameter tuning. Valida values are: "y" - enable, "n" - disable')

	# Saved model arguments
	args_parser.add_argument(
	'--job-dir',
	default=os.getenv('AIP_MODEL_DIR'),
	help='GCS location to export models')
	args_parser.add_argument(
	'--model-name',
	default="finetuned-bert-classifier",
	help='The name of your saved model')

	return args_parser.parse_args()


	def main():
	"""Setup / Start the experiment
	"""
	args = get_args()
	print(args)
	experiment.run(args)


	if __name__ == '__main__':
	main()