Python absl.flags.adopt_module_key_flags() Examples
The following are 30
code examples of absl.flags.adopt_module_key_flags().
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Example #1
| Source File: core.py From ml-on-gcp with Apache License 2.0 | 6 votes |
|
def register_key_flags_in_core(f):
"""Defines a function in core.py, and registers its key flags.
absl uses the location of a flags.declare_key_flag() to determine the context
in which a flag is key. By making all declares in core, this allows model
main functions to call flags.adopt_module_key_flags() on core and correctly
chain key flags.
Args:
f: The function to be wrapped
Returns:
The "core-defined" version of the input function.
"""
def core_fn(*args, **kwargs):
key_flags = f(*args, **kwargs)
[flags.declare_key_flag(fl) for fl in key_flags] # pylint: disable=expression-not-assigned
return core_fn
Example #2
| Source File: movielens_main.py From g-tensorflow-models with Apache License 2.0 | 6 votes |
|
def define_movie_flags():
"""Define flags for movie dataset training."""
wide_deep_run_loop.define_wide_deep_flags()
flags.DEFINE_enum(
name="dataset", default=movielens.ML_1M,
enum_values=movielens.DATASETS, case_sensitive=False,
help=flags_core.help_wrap("Dataset to be trained and evaluated."))
flags.adopt_module_key_flags(wide_deep_run_loop)
flags_core.set_defaults(data_dir="/tmp/movielens-data/",
model_dir='/tmp/movie_model',
model_type="deep",
train_epochs=50,
epochs_between_evals=5,
inter_op_parallelism_threads=0,
intra_op_parallelism_threads=0,
batch_size=256)
@flags.validator("stop_threshold",
message="stop_threshold not supported for movielens model")
def _no_stop(stop_threshold):
return stop_threshold is None
Example #3
| Source File: wide_deep_run_loop.py From g-tensorflow-models with Apache License 2.0 | 6 votes |
|
def define_wide_deep_flags():
"""Add supervised learning flags, as well as wide-deep model type."""
flags_core.define_base()
flags_core.define_benchmark()
flags_core.define_performance(
num_parallel_calls=False, inter_op=True, intra_op=True,
synthetic_data=False, max_train_steps=False, dtype=False,
all_reduce_alg=False)
flags.adopt_module_key_flags(flags_core)
flags.DEFINE_enum(
name="model_type", short_name="mt", default="wide_deep",
enum_values=['wide', 'deep', 'wide_deep'],
help="Select model topology.")
flags.DEFINE_boolean(
name="download_if_missing", default=True, help=flags_core.help_wrap(
"Download data to data_dir if it is not already present."))
Example #4
| Source File: core.py From g-tensorflow-models with Apache License 2.0 | 6 votes |
|
def register_key_flags_in_core(f):
"""Defines a function in core.py, and registers its key flags.
absl uses the location of a flags.declare_key_flag() to determine the context
in which a flag is key. By making all declares in core, this allows model
main functions to call flags.adopt_module_key_flags() on core and correctly
chain key flags.
Args:
f: The function to be wrapped
Returns:
The "core-defined" version of the input function.
"""
def core_fn(*args, **kwargs):
key_flags = f(*args, **kwargs)
[flags.declare_key_flag(fl) for fl in key_flags] # pylint: disable=expression-not-assigned
return core_fn
Example #5
| Source File: core.py From models with Apache License 2.0 | 6 votes |
|
def register_key_flags_in_core(f):
"""Defines a function in core.py, and registers its key flags.
absl uses the location of a flags.declare_key_flag() to determine the context
in which a flag is key. By making all declares in core, this allows model
main functions to call flags.adopt_module_key_flags() on core and correctly
chain key flags.
Args:
f: The function to be wrapped
Returns:
The "core-defined" version of the input function.
"""
def core_fn(*args, **kwargs):
key_flags = f(*args, **kwargs)
[flags.declare_key_flag(fl) for fl in key_flags] # pylint: disable=expression-not-assigned
return core_fn
Example #6
| Source File: core.py From models with Apache License 2.0 | 6 votes |
|
def register_key_flags_in_core(f):
"""Defines a function in core.py, and registers its key flags.
absl uses the location of a flags.declare_key_flag() to determine the context
in which a flag is key. By making all declares in core, this allows model
main functions to call flags.adopt_module_key_flags() on core and correctly
chain key flags.
Args:
f: The function to be wrapped
Returns:
The "core-defined" version of the input function.
"""
def core_fn(*args, **kwargs):
key_flags = f(*args, **kwargs)
[flags.declare_key_flag(fl) for fl in key_flags] # pylint: disable=expression-not-assigned
return core_fn
Example #7
| Source File: core.py From models with Apache License 2.0 | 6 votes |
|
def register_key_flags_in_core(f):
"""Defines a function in core.py, and registers its key flags.
absl uses the location of a flags.declare_key_flag() to determine the context
in which a flag is key. By making all declares in core, this allows model
main functions to call flags.adopt_module_key_flags() on core and correctly
chain key flags.
Args:
f: The function to be wrapped
Returns:
The "core-defined" version of the input function.
"""
def core_fn(*args, **kwargs):
key_flags = f(*args, **kwargs)
[flags.declare_key_flag(fl) for fl in key_flags] # pylint: disable=expression-not-assigned
return core_fn
Example #8
| Source File: core.py From models with Apache License 2.0 | 6 votes |
|
def register_key_flags_in_core(f):
"""Defines a function in core.py, and registers its key flags.
absl uses the location of a flags.declare_key_flag() to determine the context
in which a flag is key. By making all declares in core, this allows model
main functions to call flags.adopt_module_key_flags() on core and correctly
chain key flags.
Args:
f: The function to be wrapped
Returns:
The "core-defined" version of the input function.
"""
def core_fn(*args, **kwargs):
key_flags = f(*args, **kwargs)
[flags.declare_key_flag(fl) for fl in key_flags] # pylint: disable=expression-not-assigned
return core_fn
Example #9
| Source File: movielens_main.py From multilabel-image-classification-tensorflow with MIT License | 6 votes |
|
def define_movie_flags():
"""Define flags for movie dataset training."""
wide_deep_run_loop.define_wide_deep_flags()
flags.DEFINE_enum(
name="dataset", default=movielens.ML_1M,
enum_values=movielens.DATASETS, case_sensitive=False,
help=flags_core.help_wrap("Dataset to be trained and evaluated."))
flags.adopt_module_key_flags(wide_deep_run_loop)
flags_core.set_defaults(data_dir="/tmp/movielens-data/",
model_dir='/tmp/movie_model',
model_type="deep",
train_epochs=50,
epochs_between_evals=5,
inter_op_parallelism_threads=0,
intra_op_parallelism_threads=0,
batch_size=256)
@flags.validator("stop_threshold",
message="stop_threshold not supported for movielens model")
def _no_stop(stop_threshold):
return stop_threshold is None
Example #10
| Source File: wide_deep_run_loop.py From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 | 6 votes |
|
def define_wide_deep_flags():
"""Add supervised learning flags, as well as wide-deep model type."""
flags_core.define_base(clean=True, train_epochs=True,
epochs_between_evals=True)
flags_core.define_benchmark()
flags_core.define_performance(
num_parallel_calls=False, inter_op=True, intra_op=True,
synthetic_data=False, max_train_steps=False, dtype=False,
all_reduce_alg=False)
flags.adopt_module_key_flags(flags_core)
flags.DEFINE_enum(
name="model_type", short_name="mt", default="wide_deep",
enum_values=['wide', 'deep', 'wide_deep'],
help="Select model topology.")
flags.DEFINE_boolean(
name="download_if_missing", default=True, help=flags_core.help_wrap(
"Download data to data_dir if it is not already present."))
Example #11
| Source File: core.py From ml-on-gcp with Apache License 2.0 | 6 votes |
|
def register_key_flags_in_core(f):
"""Defines a function in core.py, and registers its key flags.
absl uses the location of a flags.declare_key_flag() to determine the context
in which a flag is key. By making all declares in core, this allows model
main functions to call flags.adopt_module_key_flags() on core and correctly
chain key flags.
Args:
f: The function to be wrapped
Returns:
The "core-defined" version of the input function.
"""
def core_fn(*args, **kwargs):
key_flags = f(*args, **kwargs)
[flags.declare_key_flag(fl) for fl in key_flags] # pylint: disable=expression-not-assigned
return core_fn
Example #12
| Source File: wide_deep_run_loop.py From multilabel-image-classification-tensorflow with MIT License | 6 votes |
|
def define_wide_deep_flags():
"""Add supervised learning flags, as well as wide-deep model type."""
flags_core.define_base()
flags_core.define_benchmark()
flags_core.define_performance(
num_parallel_calls=False, inter_op=True, intra_op=True,
synthetic_data=False, max_train_steps=False, dtype=False,
all_reduce_alg=False)
flags.adopt_module_key_flags(flags_core)
flags.DEFINE_enum(
name="model_type", short_name="mt", default="wide_deep",
enum_values=['wide', 'deep', 'wide_deep'],
help="Select model topology.")
flags.DEFINE_boolean(
name="download_if_missing", default=True, help=flags_core.help_wrap(
"Download data to data_dir if it is not already present."))
Example #13
| Source File: core.py From ml-on-gcp with Apache License 2.0 | 6 votes |
|
def register_key_flags_in_core(f):
"""Defines a function in core.py, and registers its key flags.
absl uses the location of a flags.declare_key_flag() to determine the context
in which a flag is key. By making all declares in core, this allows model
main functions to call flags.adopt_module_key_flags() on core and correctly
chain key flags.
Args:
f: The function to be wrapped
Returns:
The "core-defined" version of the input function.
"""
def core_fn(*args, **kwargs):
key_flags = f(*args, **kwargs)
[flags.declare_key_flag(fl) for fl in key_flags] # pylint: disable=expression-not-assigned
return core_fn
Example #14
| Source File: core.py From multilabel-image-classification-tensorflow with MIT License | 6 votes |
|
def register_key_flags_in_core(f):
"""Defines a function in core.py, and registers its key flags.
absl uses the location of a flags.declare_key_flag() to determine the context
in which a flag is key. By making all declares in core, this allows model
main functions to call flags.adopt_module_key_flags() on core and correctly
chain key flags.
Args:
f: The function to be wrapped
Returns:
The "core-defined" version of the input function.
"""
def core_fn(*args, **kwargs):
key_flags = f(*args, **kwargs)
[flags.declare_key_flag(fl) for fl in key_flags] # pylint: disable=expression-not-assigned
return core_fn
Example #15
| Source File: movielens_main.py From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 | 6 votes |
|
def define_movie_flags():
"""Define flags for movie dataset training."""
wide_deep_run_loop.define_wide_deep_flags()
flags.DEFINE_enum(
name="dataset", default=movielens.ML_1M,
enum_values=movielens.DATASETS, case_sensitive=False,
help=flags_core.help_wrap("Dataset to be trained and evaluated."))
flags.adopt_module_key_flags(wide_deep_run_loop)
flags_core.set_defaults(data_dir="/tmp/movielens-data/",
model_dir='/tmp/movie_model',
model_type="deep",
train_epochs=50,
epochs_between_evals=5,
inter_op_parallelism_threads=0,
intra_op_parallelism_threads=0,
batch_size=256)
@flags.validator("stop_threshold",
message="stop_threshold not supported for movielens model")
def _no_stop(stop_threshold):
return stop_threshold is None
Example #16
| Source File: core.py From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 | 6 votes |
|
def register_key_flags_in_core(f):
"""Defines a function in core.py, and registers its key flags.
absl uses the location of a flags.declare_key_flag() to determine the context
in which a flag is key. By making all declares in core, this allows model
main functions to call flags.adopt_module_key_flags() on core and correctly
chain key flags.
Args:
f: The function to be wrapped
Returns:
The "core-defined" version of the input function.
"""
def core_fn(*args, **kwargs):
key_flags = f(*args, **kwargs)
[flags.declare_key_flag(fl) for fl in key_flags] # pylint: disable=expression-not-assigned
return core_fn
Example #17
| Source File: core.py From nsfw with Apache License 2.0 | 6 votes |
|
def register_key_flags_in_core(f):
"""Defines a function in core.py, and registers its key flags.
absl uses the location of a flags.declare_key_flag() to determine the context
in which a flag is key. By making all declares in core, this allows model
main functions to call flags.adopt_module_key_flags() on core and correctly
chain key flags.
Args:
f: The function to be wrapped
Returns:
The "core-defined" version of the input function.
"""
def core_fn(*args, **kwargs):
key_flags = f(*args, **kwargs)
[flags.declare_key_flag(fl) for fl in key_flags] # pylint: disable=expression-not-assigned
return core_fn
Example #18
| Source File: core.py From models with Apache License 2.0 | 6 votes |
|
def register_key_flags_in_core(f):
"""Defines a function in core.py, and registers its key flags.
absl uses the location of a flags.declare_key_flag() to determine the context
in which a flag is key. By making all declares in core, this allows model
main functions to call flags.adopt_module_key_flags() on core and correctly
chain key flags.
Args:
f: The function to be wrapped
Returns:
The "core-defined" version of the input function.
"""
def core_fn(*args, **kwargs):
key_flags = f(*args, **kwargs)
[flags.declare_key_flag(fl) for fl in key_flags] # pylint: disable=expression-not-assigned
return core_fn
Example #19
| Source File: imagenet_main.py From g-tensorflow-models with Apache License 2.0 | 5 votes |
|
def define_imagenet_flags():
resnet_run_loop.define_resnet_flags(
resnet_size_choices=['18', '34', '50', '101', '152', '200'])
flags.adopt_module_key_flags(resnet_run_loop)
flags_core.set_defaults(train_epochs=90)
Example #20
| Source File: census_main.py From multilabel-image-classification-tensorflow with MIT License | 5 votes |
|
def define_census_flags():
wide_deep_run_loop.define_wide_deep_flags()
flags.adopt_module_key_flags(wide_deep_run_loop)
flags_core.set_defaults(data_dir='/tmp/census_data',
model_dir='/tmp/census_model',
train_epochs=40,
epochs_between_evals=2,
inter_op_parallelism_threads=0,
intra_op_parallelism_threads=0,
batch_size=40)
Example #21
| Source File: mnist.py From multilabel-image-classification-tensorflow with MIT License | 5 votes |
|
def define_mnist_flags():
flags_core.define_base()
flags_core.define_performance(num_parallel_calls=False)
flags_core.define_image()
flags.adopt_module_key_flags(flags_core)
flags_core.set_defaults(data_dir='/tmp/mnist_data',
model_dir='/tmp/mnist_model',
batch_size=100,
train_epochs=40)
Example #22
| Source File: cifar10_main.py From multilabel-image-classification-tensorflow with MIT License | 5 votes |
|
def define_cifar_flags():
resnet_run_loop.define_resnet_flags()
flags.adopt_module_key_flags(resnet_run_loop)
flags_core.set_defaults(data_dir='/tmp/cifar10_data',
model_dir='/tmp/cifar10_model',
resnet_size='56',
train_epochs=182,
epochs_between_evals=10,
batch_size=128,
image_bytes_as_serving_input=False)
Example #23
| 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 #24
| Source File: imagenet_main.py From multilabel-image-classification-tensorflow with MIT License | 5 votes |
|
def define_imagenet_flags():
resnet_run_loop.define_resnet_flags(
resnet_size_choices=['18', '34', '50', '101', '152', '200'])
flags.adopt_module_key_flags(resnet_run_loop)
flags_core.set_defaults(train_epochs=90)
Example #25
| 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 #26
| 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 #27
| Source File: resnet_imagenet_main.py From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 | 5 votes |
|
def define_imagenet_keras_flags(): common.define_keras_flags() flags_core.set_defaults(train_epochs=90) flags.adopt_module_key_flags(common)
Example #28
| Source File: imagenet_main.py From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 | 5 votes |
|
def define_imagenet_flags():
resnet_run_loop.define_resnet_flags(
resnet_size_choices=['18', '34', '50', '101', '152', '200'],
dynamic_loss_scale=True,
fp16_implementation=True)
flags.adopt_module_key_flags(resnet_run_loop)
flags_core.set_defaults(train_epochs=90)
Example #29
| Source File: cifar10_main.py From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 | 5 votes |
|
def define_cifar_flags():
resnet_run_loop.define_resnet_flags()
flags.adopt_module_key_flags(resnet_run_loop)
flags_core.set_defaults(data_dir='/tmp/cifar10_data/cifar-10-batches-bin',
model_dir='/tmp/cifar10_model',
resnet_size='56',
train_epochs=182,
epochs_between_evals=10,
batch_size=128,
image_bytes_as_serving_input=False)
Example #30
| Source File: mnist.py From models with Apache License 2.0 | 5 votes |
|
def define_mnist_flags():
flags_core.define_base()
flags_core.define_image()
flags.adopt_module_key_flags(flags_core)
flags_core.set_defaults(data_dir='/tmp/mnist_data',
model_dir='/tmp/mnist_model',
batch_size=100,
train_epochs=40)
