Python absl.flags.DEFINE_float() Examples
The following are 26
code examples of absl.flags.DEFINE_float().
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Example #1
| Source File: flags.py From benchmarks with Apache License 2.0 | 7 votes |
|
def define_flags(specs=None):
"""Define a command line flag for each ParamSpec in flags.param_specs."""
specs = specs or param_specs
define_flag = {
'boolean': absl_flags.DEFINE_boolean,
'float': absl_flags.DEFINE_float,
'integer': absl_flags.DEFINE_integer,
'string': absl_flags.DEFINE_string,
'enum': absl_flags.DEFINE_enum,
'list': absl_flags.DEFINE_list
}
for name, param_spec in six.iteritems(specs):
if param_spec.flag_type not in define_flag:
raise ValueError('Unknown flag_type %s' % param_spec.flag_type)
else:
define_flag[param_spec.flag_type](name, param_spec.default_value,
help=param_spec.description,
**param_spec.kwargs)
Example #2
| Source File: lamb_flags.py From lamb with Apache License 2.0 | 6 votes |
|
def _define_flags(options):
for option in _filter_options(options):
name, type_, default_ = option[:3]
if type_ == 'boolean':
flags.DEFINE_boolean(name, default_, '')
elif type_ == 'integer':
flags.DEFINE_integer(name, default_, '')
elif type_ == 'float':
flags.DEFINE_float(name, default_, '')
elif type_ == 'string':
flags.DEFINE_string(name, default_, '')
elif type_ == 'list_of_ints':
flags.DEFINE_string(name, default_, '')
else:
assert 'Unexpected option type %s' % type_
# Define command-line flags for all options (unless `internal`).
Example #3
| Source File: app.py From clgen with GNU General Public License v3.0 | 6 votes |
|
def DEFINE_float(
name: str,
default: Optional[float],
help: str,
required: bool = False,
lower_bound: Optional[float] = None,
upper_bound: Optional[float] = None,
validator: Callable[[float], bool] = None,
):
"""Registers a flag whose value must be a float."""
absl_flags.DEFINE_float(
name,
default,
help,
module_name=get_calling_module_name(),
lower_bound=lower_bound,
upper_bound=upper_bound,
)
if required:
absl_flags.mark_flag_as_required(name)
if validator:
RegisterFlagValidator(name, validator)
Example #4
| Source File: argparse_flags_test.py From abseil-py with Apache License 2.0 | 6 votes |
|
def setUp(self):
self._absl_flags = flags.FlagValues()
flags.DEFINE_bool(
'absl_bool', None, 'help for --absl_bool.',
short_name='b', flag_values=self._absl_flags)
# Add a boolean flag that starts with "no", to verify it can correctly
# handle the "no" prefixes in boolean flags.
flags.DEFINE_bool(
'notice', None, 'help for --notice.',
flag_values=self._absl_flags)
flags.DEFINE_string(
'absl_string', 'default', 'help for --absl_string=%.',
short_name='s', flag_values=self._absl_flags)
flags.DEFINE_integer(
'absl_integer', 1, 'help for --absl_integer.',
flag_values=self._absl_flags)
flags.DEFINE_float(
'absl_float', 1, 'help for --absl_integer.',
flag_values=self._absl_flags)
flags.DEFINE_enum(
'absl_enum', 'apple', ['apple', 'orange'], 'help for --absl_enum.',
flag_values=self._absl_flags)
Example #5
| Source File: flags.py From dlcookbook-dlbs with Apache License 2.0 | 6 votes |
|
def define_flags():
"""Define a command line flag for each ParamSpec in flags.param_specs."""
define_flag = {
'boolean': absl_flags.DEFINE_boolean,
'float': absl_flags.DEFINE_float,
'integer': absl_flags.DEFINE_integer,
'string': absl_flags.DEFINE_string,
'enum': absl_flags.DEFINE_enum,
'list': absl_flags.DEFINE_list
}
for name, param_spec in six.iteritems(param_specs):
if param_spec.flag_type not in define_flag:
raise ValueError('Unknown flag_type %s' % param_spec.flag_type)
else:
define_flag[param_spec.flag_type](name, param_spec.default_value,
help=param_spec.description,
**param_spec.kwargs)
Example #6
| Source File: mnist_eager.py From multilabel-image-classification-tensorflow with MIT License | 5 votes |
|
def define_mnist_eager_flags():
"""Defined flags and defaults for MNIST in eager mode."""
flags_core.define_base_eager()
flags_core.define_image()
flags.adopt_module_key_flags(flags_core)
flags.DEFINE_integer(
name='log_interval', short_name='li', default=10,
help=flags_core.help_wrap('batches between logging training status'))
flags.DEFINE_string(
name='output_dir', short_name='od', default=None,
help=flags_core.help_wrap('Directory to write TensorBoard summaries'))
flags.DEFINE_float(name='learning_rate', short_name='lr', default=0.01,
help=flags_core.help_wrap('Learning rate.'))
flags.DEFINE_float(name='momentum', short_name='m', default=0.5,
help=flags_core.help_wrap('SGD momentum.'))
flags.DEFINE_bool(name='no_gpu', short_name='nogpu', default=False,
help=flags_core.help_wrap(
'disables GPU usage even if a GPU is available'))
flags_core.set_defaults(
data_dir='/tmp/tensorflow/mnist/input_data',
model_dir='/tmp/tensorflow/mnist/checkpoints/',
batch_size=100,
train_epochs=10,
)
Example #7
| Source File: flags.py From benchmarks with Apache License 2.0 | 5 votes |
|
def DEFINE_float(name, default, help, lower_bound=None, upper_bound=None): # pylint: disable=invalid-name,redefined-builtin
kwargs = {'lower_bound': lower_bound, 'upper_bound': upper_bound}
param_specs[name] = ParamSpec('float', default, help, kwargs)
Example #8
| Source File: train_higgs.py From multilabel-image-classification-tensorflow with MIT License | 5 votes |
|
def define_train_higgs_flags():
"""Add tree related flags as well as training/eval configuration."""
flags_core.define_base(clean=False, stop_threshold=False, batch_size=False,
num_gpu=False)
flags_core.define_benchmark()
flags.adopt_module_key_flags(flags_core)
flags.DEFINE_integer(
name="train_start", default=0,
help=help_wrap("Start index of train examples within the data."))
flags.DEFINE_integer(
name="train_count", default=1000000,
help=help_wrap("Number of train examples within the data."))
flags.DEFINE_integer(
name="eval_start", default=10000000,
help=help_wrap("Start index of eval examples within the data."))
flags.DEFINE_integer(
name="eval_count", default=1000000,
help=help_wrap("Number of eval examples within the data."))
flags.DEFINE_integer(
"n_trees", default=100, help=help_wrap("Number of trees to build."))
flags.DEFINE_integer(
"max_depth", default=6, help=help_wrap("Maximum depths of each tree."))
flags.DEFINE_float(
"learning_rate", default=0.1,
help=help_wrap("The learning rate."))
flags_core.set_defaults(data_dir="/tmp/higgs_data",
model_dir="/tmp/higgs_model")
Example #9
| Source File: common.py From models with Apache License 2.0 | 5 votes |
|
def define_pruning_flags():
"""Define flags for pruning methods."""
flags.DEFINE_string('pruning_method', None,
'Pruning method.'
'None (no pruning) or polynomial_decay.')
flags.DEFINE_float('pruning_initial_sparsity', 0.0,
'Initial sparsity for pruning.')
flags.DEFINE_float('pruning_final_sparsity', 0.5,
'Final sparsity for pruning.')
flags.DEFINE_integer('pruning_begin_step', 0,
'Begin step for pruning.')
flags.DEFINE_integer('pruning_end_step', 100000,
'End step for pruning.')
flags.DEFINE_integer('pruning_frequency', 100,
'Frequency for pruning.')
Example #10
| Source File: mnist_eager.py From g-tensorflow-models with Apache License 2.0 | 5 votes |
|
def define_mnist_eager_flags():
"""Defined flags and defaults for MNIST in eager mode."""
flags_core.define_base_eager()
flags_core.define_image()
flags.adopt_module_key_flags(flags_core)
flags.DEFINE_integer(
name='log_interval', short_name='li', default=10,
help=flags_core.help_wrap('batches between logging training status'))
flags.DEFINE_string(
name='output_dir', short_name='od', default=None,
help=flags_core.help_wrap('Directory to write TensorBoard summaries'))
flags.DEFINE_float(name='learning_rate', short_name='lr', default=0.01,
help=flags_core.help_wrap('Learning rate.'))
flags.DEFINE_float(name='momentum', short_name='m', default=0.5,
help=flags_core.help_wrap('SGD momentum.'))
flags.DEFINE_bool(name='no_gpu', short_name='nogpu', default=False,
help=flags_core.help_wrap(
'disables GPU usage even if a GPU is available'))
flags_core.set_defaults(
data_dir='/tmp/tensorflow/mnist/input_data',
model_dir='/tmp/tensorflow/mnist/checkpoints/',
batch_size=100,
train_epochs=10,
)
Example #11
| Source File: train_higgs.py From g-tensorflow-models with Apache License 2.0 | 5 votes |
|
def define_train_higgs_flags():
"""Add tree related flags as well as training/eval configuration."""
flags_core.define_base(clean=False, stop_threshold=False, batch_size=False,
num_gpu=False)
flags_core.define_benchmark()
flags.adopt_module_key_flags(flags_core)
flags.DEFINE_integer(
name="train_start", default=0,
help=help_wrap("Start index of train examples within the data."))
flags.DEFINE_integer(
name="train_count", default=1000000,
help=help_wrap("Number of train examples within the data."))
flags.DEFINE_integer(
name="eval_start", default=10000000,
help=help_wrap("Start index of eval examples within the data."))
flags.DEFINE_integer(
name="eval_count", default=1000000,
help=help_wrap("Number of eval examples within the data."))
flags.DEFINE_integer(
"n_trees", default=100, help=help_wrap("Number of trees to build."))
flags.DEFINE_integer(
"max_depth", default=6, help=help_wrap("Maximum depths of each tree."))
flags.DEFINE_float(
"learning_rate", default=0.1,
help=help_wrap("The learning rate."))
flags_core.set_defaults(data_dir="/tmp/higgs_data",
model_dir="/tmp/higgs_model")
Example #12
| Source File: flags.py From dlcookbook-dlbs with Apache License 2.0 | 5 votes |
|
def DEFINE_float(name, default, help, lower_bound=None, upper_bound=None): # pylint: disable=invalid-name,redefined-builtin
kwargs = {'lower_bound': lower_bound, 'upper_bound': upper_bound}
param_specs[name] = ParamSpec('float', default, help, kwargs)
Example #13
| Source File: train_higgs.py From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 | 5 votes |
|
def define_train_higgs_flags():
"""Add tree related flags as well as training/eval configuration."""
flags_core.define_base(clean=False, stop_threshold=False, batch_size=False,
num_gpu=False)
flags_core.define_benchmark()
flags.adopt_module_key_flags(flags_core)
flags.DEFINE_integer(
name="train_start", default=0,
help=help_wrap("Start index of train examples within the data."))
flags.DEFINE_integer(
name="train_count", default=1000000,
help=help_wrap("Number of train examples within the data."))
flags.DEFINE_integer(
name="eval_start", default=10000000,
help=help_wrap("Start index of eval examples within the data."))
flags.DEFINE_integer(
name="eval_count", default=1000000,
help=help_wrap("Number of eval examples within the data."))
flags.DEFINE_integer(
"n_trees", default=100, help=help_wrap("Number of trees to build."))
flags.DEFINE_integer(
"max_depth", default=6, help=help_wrap("Maximum depths of each tree."))
flags.DEFINE_float(
"learning_rate", default=0.1,
help=help_wrap("The learning rate."))
flags_core.set_defaults(data_dir="/tmp/higgs_data",
model_dir="/tmp/higgs_model")
Example #14
| Source File: mnist_eager.py From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 | 5 votes |
|
def define_mnist_eager_flags():
"""Defined flags and defaults for MNIST in eager mode."""
flags_core.define_base_eager(clean=True, train_epochs=True)
flags_core.define_image()
flags.adopt_module_key_flags(flags_core)
flags.DEFINE_integer(
name='log_interval', short_name='li', default=10,
help=flags_core.help_wrap('batches between logging training status'))
flags.DEFINE_string(
name='output_dir', short_name='od', default=None,
help=flags_core.help_wrap('Directory to write TensorBoard summaries'))
flags.DEFINE_float(name='learning_rate', short_name='lr', default=0.01,
help=flags_core.help_wrap('Learning rate.'))
flags.DEFINE_float(name='momentum', short_name='m', default=0.5,
help=flags_core.help_wrap('SGD momentum.'))
flags.DEFINE_bool(name='no_gpu', short_name='nogpu', default=False,
help=flags_core.help_wrap(
'disables GPU usage even if a GPU is available'))
flags_core.set_defaults(
data_dir='/tmp/tensorflow/mnist/input_data',
model_dir='/tmp/tensorflow/mnist/checkpoints/',
batch_size=100,
train_epochs=10,
)
Example #15
| Source File: mnist_eager.py From models with Apache License 2.0 | 5 votes |
|
def define_mnist_eager_flags():
"""Defined flags and defaults for MNIST in eager mode."""
flags_core.define_base_eager()
flags_core.define_image()
flags.adopt_module_key_flags(flags_core)
flags.DEFINE_integer(
name='log_interval', short_name='li', default=10,
help=flags_core.help_wrap('batches between logging training status'))
flags.DEFINE_string(
name='output_dir', short_name='od', default=None,
help=flags_core.help_wrap('Directory to write TensorBoard summaries'))
flags.DEFINE_float(name='learning_rate', short_name='lr', default=0.01,
help=flags_core.help_wrap('Learning rate.'))
flags.DEFINE_float(name='momentum', short_name='m', default=0.5,
help=flags_core.help_wrap('SGD momentum.'))
flags.DEFINE_bool(name='no_gpu', short_name='nogpu', default=False,
help=flags_core.help_wrap(
'disables GPU usage even if a GPU is available'))
flags_core.set_defaults(
data_dir='/tmp/tensorflow/mnist/input_data',
model_dir='/tmp/tensorflow/mnist/checkpoints/',
batch_size=100,
train_epochs=10,
)
Example #16
| Source File: mnist_eager.py From models with Apache License 2.0 | 5 votes |
|
def define_mnist_eager_flags():
"""Defined flags and defaults for MNIST in eager mode."""
flags_core.define_base_eager()
flags_core.define_image()
flags.adopt_module_key_flags(flags_core)
flags.DEFINE_integer(
name='log_interval', short_name='li', default=10,
help=flags_core.help_wrap('batches between logging training status'))
flags.DEFINE_string(
name='output_dir', short_name='od', default=None,
help=flags_core.help_wrap('Directory to write TensorBoard summaries'))
flags.DEFINE_float(name='learning_rate', short_name='lr', default=0.01,
help=flags_core.help_wrap('Learning rate.'))
flags.DEFINE_float(name='momentum', short_name='m', default=0.5,
help=flags_core.help_wrap('SGD momentum.'))
flags.DEFINE_bool(name='no_gpu', short_name='nogpu', default=False,
help=flags_core.help_wrap(
'disables GPU usage even if a GPU is available'))
flags_core.set_defaults(
data_dir='/tmp/tensorflow/mnist/input_data',
model_dir='/tmp/tensorflow/mnist/checkpoints/',
batch_size=100,
train_epochs=10,
)
Example #17
| Source File: mnist_eager.py From models with Apache License 2.0 | 5 votes |
|
def define_mnist_eager_flags():
"""Defined flags and defaults for MNIST in eager mode."""
flags_core.define_base_eager()
flags_core.define_image()
flags.adopt_module_key_flags(flags_core)
flags.DEFINE_integer(
name='log_interval', short_name='li', default=10,
help=flags_core.help_wrap('batches between logging training status'))
flags.DEFINE_string(
name='output_dir', short_name='od', default=None,
help=flags_core.help_wrap('Directory to write TensorBoard summaries'))
flags.DEFINE_float(name='learning_rate', short_name='lr', default=0.01,
help=flags_core.help_wrap('Learning rate.'))
flags.DEFINE_float(name='momentum', short_name='m', default=0.5,
help=flags_core.help_wrap('SGD momentum.'))
flags.DEFINE_bool(name='no_gpu', short_name='nogpu', default=False,
help=flags_core.help_wrap(
'disables GPU usage even if a GPU is available'))
flags_core.set_defaults(
data_dir='/tmp/tensorflow/mnist/input_data',
model_dir='/tmp/tensorflow/mnist/checkpoints/',
batch_size=100,
train_epochs=10,
)
Example #18
| Source File: mnist_eager.py From models with Apache License 2.0 | 5 votes |
|
def define_mnist_eager_flags():
"""Defined flags and defaults for MNIST in eager mode."""
flags_core.define_base_eager()
flags_core.define_image()
flags.adopt_module_key_flags(flags_core)
flags.DEFINE_integer(
name='log_interval', short_name='li', default=10,
help=flags_core.help_wrap('batches between logging training status'))
flags.DEFINE_string(
name='output_dir', short_name='od', default=None,
help=flags_core.help_wrap('Directory to write TensorBoard summaries'))
flags.DEFINE_float(name='learning_rate', short_name='lr', default=0.01,
help=flags_core.help_wrap('Learning rate.'))
flags.DEFINE_float(name='momentum', short_name='m', default=0.5,
help=flags_core.help_wrap('SGD momentum.'))
flags.DEFINE_bool(name='no_gpu', short_name='nogpu', default=False,
help=flags_core.help_wrap(
'disables GPU usage even if a GPU is available'))
flags_core.set_defaults(
data_dir='/tmp/tensorflow/mnist/input_data',
model_dir='/tmp/tensorflow/mnist/checkpoints/',
batch_size=100,
train_epochs=10,
)
Example #19
| Source File: flags_test.py From abseil-py with Apache License 2.0 | 5 votes |
|
def test_float_error_text(self):
flags.DEFINE_float('positive', 4, 'non-negative flag', lower_bound=1,
flag_values=self.flag_values)
flags.DEFINE_float('non_negative', 4, 'positive flag', lower_bound=0,
flag_values=self.flag_values)
flags.DEFINE_float('negative', -4, 'negative flag', upper_bound=-1,
flag_values=self.flag_values)
flags.DEFINE_float('non_positive', -4, 'non-positive flag', upper_bound=0,
flag_values=self.flag_values)
flags.DEFINE_float('greater', 19, 'greater-than flag', lower_bound=4,
flag_values=self.flag_values)
flags.DEFINE_float('smaller', -19, 'smaller-than flag', upper_bound=4,
flag_values=self.flag_values)
flags.DEFINE_float('usual', 4, 'usual flag', lower_bound=0,
upper_bound=10000, flag_values=self.flag_values)
flags.DEFINE_float('another_usual', 0, 'usual flag', lower_bound=-1,
upper_bound=1, flag_values=self.flag_values)
self._check_error_message('positive', 0.5, 'number >= 1')
self._check_error_message('non_negative', -4.0, 'a non-negative number')
self._check_error_message('negative', 0.5, 'number <= -1')
self._check_error_message('non_positive', 4.0, 'a non-positive number')
self._check_error_message('usual', -4.0, 'a number in the range [0, 10000]')
self._check_error_message('another_usual', 4.0,
'a number in the range [-1, 1]')
self._check_error_message('smaller', 5.0, 'number <= 4')
Example #20
| Source File: flags_test.py From abseil-py with Apache License 2.0 | 5 votes |
|
def test_float_flag_with_wrong_type(self):
fv = flags.FlagValues()
with self.assertRaises(flags.IllegalFlagValueError):
flags.DEFINE_float('name', False, 'help', flag_values=fv)
Example #21
| Source File: flags_numeric_bounds_test.py From abseil-py with Apache License 2.0 | 5 votes |
|
def test_success_if_exactly_equals(self):
flags.DEFINE_float('float_flag', None, '',
lower_bound=1, upper_bound=1,
flag_values=self.flag_values)
argv = ('./program', '--float_flag=1')
self.flag_values(argv)
self.assertEqual(1, self.flag_values.float_flag)
Example #22
| Source File: benchmark_cnn.py From parallax with Apache License 2.0 | 5 votes |
|
def define_flags():
"""Define a command line flag for each ParamSpec in _DEFAULT_PARAMS."""
define_flag = {
'boolean': flags.DEFINE_boolean,
'float': flags.DEFINE_float,
'integer': flags.DEFINE_integer,
'string': flags.DEFINE_string,
}
for name, param_spec in six.iteritems(_DEFAULT_PARAMS):
if param_spec.flag_type not in define_flag:
raise ValueError('Unknown flag_type %s' % param_spec.flag_type)
else:
define_flag[param_spec.flag_type](name, param_spec.default_value,
param_spec.description)
flags.declare_key_flag(name)
Example #23
| Source File: utils_impl_test.py From federated with Apache License 2.0 | 5 votes |
|
def test_record_new_flags(self):
with utils_impl.record_new_flags() as hparam_flags:
flags.DEFINE_string('exp_name', 'name', 'Unique name for the experiment.')
flags.DEFINE_float('learning_rate', 0.1, 'Optimizer learning rate.')
self.assertCountEqual(hparam_flags, ['exp_name', 'learning_rate'])
Example #24
| Source File: common_flags.py From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 | 4 votes |
|
def define_common_bert_flags():
"""Define the flags related to TPU's."""
flags.DEFINE_string('bert_config_file', None,
'Bert configuration file to define core bert layers.')
flags.DEFINE_string('model_dir', None, (
'The directory where the model weights and training/evaluation summaries '
'are stored. If not specified, save to /tmp/bert20/.'))
flags.DEFINE_string(
'model_export_path', None,
'Path to the directory, where trainined model will be '
'exported.')
flags.DEFINE_string('tpu', '', 'TPU address to connect to.')
flags.DEFINE_string(
'init_checkpoint', None,
'Initial checkpoint (usually from a pre-trained BERT model).')
flags.DEFINE_enum(
'strategy_type', 'mirror', ['tpu', 'mirror', 'multi_worker_mirror'],
'Distribution Strategy type to use for training. `tpu` uses '
'TPUStrategy for running on TPUs, `mirror` uses GPUs with single host, '
'`multi_worker_mirror` uses CPUs or GPUs with multiple hosts.')
flags.DEFINE_integer('num_train_epochs', 3,
'Total number of training epochs to perform.')
flags.DEFINE_integer(
'steps_per_loop', 200,
'Number of steps per graph-mode loop. Only training step '
'happens inside the loop. Callbacks will not be called '
'inside.')
flags.DEFINE_float('learning_rate', 5e-5,
'The initial learning rate for Adam.')
flags.DEFINE_boolean(
'run_eagerly', False,
'Run the model op by op without building a model function.')
flags.DEFINE_boolean(
'scale_loss', False,
'Whether to divide the loss by number of replica inside the per-replica '
'loss function.')
# Adds flags for mixed precision training.
flags_core.define_performance(
num_parallel_calls=False,
inter_op=False,
intra_op=False,
synthetic_data=False,
max_train_steps=False,
dtype=True,
dynamic_loss_scale=True,
loss_scale=True,
all_reduce_alg=False,
num_packs=False,
enable_xla=True
)
Example #25
| Source File: run_squad_helper.py From models with Apache License 2.0 | 4 votes |
|
def define_common_squad_flags():
"""Defines common flags used by SQuAD tasks."""
flags.DEFINE_enum(
'mode', 'train_and_eval',
['train_and_eval', 'train_and_predict',
'train', 'eval', 'predict', 'export_only'],
'One of {"train_and_eval", "train_and_predict", '
'"train", "eval", "predict", "export_only"}. '
'`train_and_eval`: train & predict to json files & compute eval metrics. '
'`train_and_predict`: train & predict to json files. '
'`train`: only trains the model. '
'`eval`: predict answers from squad json file & compute eval metrics. '
'`predict`: predict answers from the squad json file. '
'`export_only`: will take the latest checkpoint inside '
'model_dir and export a `SavedModel`.')
flags.DEFINE_string('train_data_path', '',
'Training data path with train tfrecords.')
flags.DEFINE_string(
'input_meta_data_path', None,
'Path to file that contains meta data about input '
'to be used for training and evaluation.')
# Model training specific flags.
flags.DEFINE_integer('train_batch_size', 32, 'Total batch size for training.')
# Predict processing related.
flags.DEFINE_string('predict_file', None,
'SQuAD prediction json file path. '
'`predict` mode supports multiple files: one can use '
'wildcard to specify multiple files and it can also be '
'multiple file patterns separated by comma. Note that '
'`eval` mode only supports a single predict file.')
flags.DEFINE_bool(
'do_lower_case', True,
'Whether to lower case the input text. Should be True for uncased '
'models and False for cased models.')
flags.DEFINE_float(
'null_score_diff_threshold', 0.0,
'If null_score - best_non_null is greater than the threshold, '
'predict null. This is only used for SQuAD v2.')
flags.DEFINE_bool(
'verbose_logging', False,
'If true, all of the warnings related to data processing will be '
'printed. A number of warnings are expected for a normal SQuAD '
'evaluation.')
flags.DEFINE_integer('predict_batch_size', 8,
'Total batch size for prediction.')
flags.DEFINE_integer(
'n_best_size', 20,
'The total number of n-best predictions to generate in the '
'nbest_predictions.json output file.')
flags.DEFINE_integer(
'max_answer_length', 30,
'The maximum length of an answer that can be generated. This is needed '
'because the start and end predictions are not conditioned on one '
'another.')
common_flags.define_common_bert_flags()
Example #26
| Source File: optimizer_utils.py From federated with Apache License 2.0 | 4 votes |
|
def define_lr_schedule_flags(prefix: str) -> None:
"""Defines flags with `prefix` to configure a learning rate schedule.
This method is intended to be paired with `create_optimizer_from_flags` with
the same `prefix`, to allow Python binaries to construct `tf.keras.optimizer`
objects from flags, along with an associated learning rate schedule.
This creates four new flags:
* `--<prefix>_lr_schedule`
* `--<prefix>_lr_decay_step`
* `--<prefix>_lr_decay_rate`
* `--<prefix>_lr_staircase`
Note that this should generally be preceded by `define_optimizer_flags`, and
followed by `create_lr_schedule_from_flags`. This will then create a learning
rate scheduling function governed by the flags defined herein.
Args:
prefix: A string (possibly empty) indicating which optimizer is being
configured.
"""
def prefixed(basename):
return '{}_{}'.format(prefix, basename) if prefix else basename
initial_lr_flag_name = prefixed('learning_rate')
if flags.FLAGS[initial_lr_flag_name] is None:
logging.warning(
'The flag %s is not set. This must be set before calling '
'`create_lr_schedule_from_flags`.', initial_lr_flag_name)
flags.DEFINE_enum(
'{!s}_lr_schedule'.format(prefix),
default='constant',
enum_values=['constant', 'exp_decay', 'inv_lin_decay', 'inv_sqrt_decay'],
help='Type of learning rate decay schedule to use for `{!s}`.'.format(
prefix))
flags.DEFINE_integer(
'{!s}_lr_decay_steps'.format(prefix),
default=None,
help='An int used to compute the learning rate schedule.'
'If staircase is set to True, then the learning rate changes every '
'`{!s}_lr_decay_steps` rounds.'.format(prefix))
flags.DEFINE_float(
'{!s}_lr_decay_rate'.format(prefix),
default=None,
help='The decay rate of the {!s} learning rate schedule.'.format(prefix))
flags.DEFINE_bool(
'{!s}_lr_staircase'.format(prefix),
default=False,
help='Whether to decay the `{!s}` learning rate at discrete intervals.'
.format(prefix))
