Python tensorflow.python.training.training.get_global_step() Examples
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code examples of tensorflow.python.training.training.get_global_step().
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Example #1
| Source File: tpu_estimator.py From Chinese-XLNet with Apache License 2.0 | 5 votes |
|
def _create_global_step(graph):
graph = graph or ops.get_default_graph()
if training.get_global_step(graph) is not None:
raise ValueError('"global_step" already exists.')
# Create in proper graph and base name_scope.
with graph.as_default() as g, g.name_scope(None):
return variable_scope.get_variable(
ops.GraphKeys.GLOBAL_STEP,
shape=[],
dtype=dtypes.int64,
initializer=init_ops.zeros_initializer(),
trainable=False,
use_resource=True,
collections=[ops.GraphKeys.GLOBAL_VARIABLES, ops.GraphKeys.GLOBAL_STEP])
Example #2
| Source File: tpu_estimator.py From Chinese-XLNet with Apache License 2.0 | 5 votes |
|
def begin(self):
self._global_step_tensor = training_util.get_global_step()
if self._global_step_tensor is None:
raise RuntimeError('Global step should be created.')
self._iterations_per_loop_var = _create_or_get_iterations_per_loop()
Example #3
| Source File: tpu_estimator.py From embedding-as-service with MIT License | 5 votes |
|
def _create_global_step(graph):
graph = graph or ops.get_default_graph()
if training.get_global_step(graph) is not None:
raise ValueError('"global_step" already exists.')
# Create in proper graph and base name_scope.
with graph.as_default() as g, g.name_scope(None):
return variable_scope.get_variable(
ops.GraphKeys.GLOBAL_STEP,
shape=[],
dtype=dtypes.int64,
initializer=init_ops.zeros_initializer(),
trainable=False,
use_resource=True,
collections=[ops.GraphKeys.GLOBAL_VARIABLES, ops.GraphKeys.GLOBAL_STEP])
Example #4
| Source File: tpu_estimator.py From embedding-as-service with MIT License | 5 votes |
|
def begin(self):
self._global_step_tensor = training_util.get_global_step()
if self._global_step_tensor is None:
raise RuntimeError('Global step should be created.')
self._iterations_per_loop_var = _create_or_get_iterations_per_loop()
Example #5
| Source File: tpu_estimator.py From transformer-xl with Apache License 2.0 | 5 votes |
|
def _create_global_step(graph):
graph = graph or ops.get_default_graph()
if training.get_global_step(graph) is not None:
raise ValueError('"global_step" already exists.')
# Create in proper graph and base name_scope.
with graph.as_default() as g, g.name_scope(None):
return variable_scope.get_variable(
ops.GraphKeys.GLOBAL_STEP,
shape=[],
dtype=dtypes.int64,
initializer=init_ops.zeros_initializer(),
trainable=False,
use_resource=True,
collections=[ops.GraphKeys.GLOBAL_VARIABLES, ops.GraphKeys.GLOBAL_STEP])
Example #6
| Source File: tpu_estimator.py From transformer-xl with Apache License 2.0 | 5 votes |
|
def begin(self):
self._global_step_tensor = training_util.get_global_step()
if self._global_step_tensor is None:
raise RuntimeError('Global step should be created.')
self._iterations_per_loop_var = _create_or_get_iterations_per_loop()
Example #7
| Source File: estimator_v2.py From boxnet with GNU General Public License v3.0 | 5 votes |
|
def _create_and_assert_global_step(self, graph):
"""Creates and asserts properties of the global step.
Args:
graph: The graph in which to create the global step tensor.
Returns:
The global step `Tensor`.
"""
step = self._create_global_step(graph)
assert step == training.get_global_step()
assert step.dtype.is_integer
return step
Example #8
| Source File: tpu_estimator.py From xlnet with Apache License 2.0 | 5 votes |
|
def _create_global_step(graph):
graph = graph or ops.get_default_graph()
if training.get_global_step(graph) is not None:
raise ValueError('"global_step" already exists.')
# Create in proper graph and base name_scope.
with graph.as_default() as g, g.name_scope(None):
return variable_scope.get_variable(
ops.GraphKeys.GLOBAL_STEP,
shape=[],
dtype=dtypes.int64,
initializer=init_ops.zeros_initializer(),
trainable=False,
use_resource=True,
collections=[ops.GraphKeys.GLOBAL_VARIABLES, ops.GraphKeys.GLOBAL_STEP])
Example #9
| Source File: tpu_estimator.py From xlnet with Apache License 2.0 | 5 votes |
|
def begin(self):
self._global_step_tensor = training_util.get_global_step()
if self._global_step_tensor is None:
raise RuntimeError('Global step should be created.')
self._iterations_per_loop_var = _create_or_get_iterations_per_loop()
Example #10
| Source File: estimator.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
|
def _create_and_assert_global_step(self, graph):
"""Creates and asserts properties of the global step.
Args:
graph: The graph in which to create the global step tensor.
Returns:
The global step `Tensor`.
"""
step = self._create_global_step(graph)
assert step == training.get_global_step()
assert step.dtype.is_integer
return step
Example #11
| Source File: tpu_estimator.py From Chinese-XLNet with Apache License 2.0 | 4 votes |
|
def _create_or_get_iterations_per_loop():
"""Creates or gets the iterations_per_loop variable.
In TPUEstimator, the user provided computation, the model_fn, is wrapped
inside a tf.while_loop for peak performance. The iterations of the loop are
specified by this variable, which adjusts its value on the CPU after each TPU
program execution and before the next TPU execution.
The purpose of using a variable, rather then a constant, is to allow
TPUEstimator adapt the TPU training iterations according to the final steps
specified by users. For example, if the user sets the iterations_per_loop as 4
in TPUConfig and steps as 10 in TPUEstimator.train(), the iterations_per_loop
variable will have the following value before each TPU training.
- 1-th TPU execution: iterations_per_loop = 4
- 2-th TPU execution: iterations_per_loop = 4
- 3-th TPU execution: iterations_per_loop = 2
As model_fn increases the global step once per train_op invocation, the global
step is 10 after all TPU executions, matching the steps=10 inputs passed in by
users.
Returns:
A TF non-trainable resource variable.
Raises:
RuntimeError: If multi iterations_per_loop variables were found.
"""
graph = ops.get_default_graph()
collection_name = '{}_{}'.format(_TPU_ESTIMATOR, _ITERATIONS_PER_LOOP_VAR)
iter_vars = graph.get_collection(collection_name)
if len(iter_vars) == 1:
return iter_vars[0]
elif len(iter_vars) > 1:
raise RuntimeError('Multiple iterations_per_loop_var in collection.')
with ops.colocate_with(training_util.get_global_step()):
with variable_scope.variable_scope(
_TPU_ESTIMATOR, reuse=variable_scope.AUTO_REUSE):
return variable_scope.get_variable(
_ITERATIONS_PER_LOOP_VAR,
initializer=init_ops.zeros_initializer(),
shape=[],
dtype=dtypes.int32,
trainable=False,
collections=[collection_name, ops.GraphKeys.LOCAL_VARIABLES],
use_resource=True)
Example #12
| Source File: tpu_estimator.py From embedding-as-service with MIT License | 4 votes |
|
def _create_or_get_iterations_per_loop():
"""Creates or gets the iterations_per_loop variable.
In TPUEstimator, the user provided computation, the model_fn, is wrapped
inside a tf.while_loop for peak performance. The iterations of the loop are
specified by this variable, which adjusts its value on the CPU after each TPU
program execution and before the next TPU execution.
The purpose of using a variable, rather then a constant, is to allow
TPUEstimator adapt the TPU training iterations according to the final steps
specified by users. For example, if the user sets the iterations_per_loop as 4
in TPUConfig and steps as 10 in TPUEstimator.train(), the iterations_per_loop
variable will have the following value before each TPU training.
- 1-th TPU execution: iterations_per_loop = 4
- 2-th TPU execution: iterations_per_loop = 4
- 3-th TPU execution: iterations_per_loop = 2
As model_fn increases the global step once per train_op invocation, the global
step is 10 after all TPU executions, matching the steps=10 inputs passed in by
users.
Returns:
A TF non-trainable resource variable.
Raises:
RuntimeError: If multi iterations_per_loop variables were found.
"""
graph = ops.get_default_graph()
collection_name = '{}_{}'.format(_TPU_ESTIMATOR, _ITERATIONS_PER_LOOP_VAR)
iter_vars = graph.get_collection(collection_name)
if len(iter_vars) == 1:
return iter_vars[0]
elif len(iter_vars) > 1:
raise RuntimeError('Multiple iterations_per_loop_var in collection.')
with ops.colocate_with(training_util.get_global_step()):
with variable_scope.variable_scope(
_TPU_ESTIMATOR, reuse=variable_scope.AUTO_REUSE):
return variable_scope.get_variable(
_ITERATIONS_PER_LOOP_VAR,
initializer=init_ops.zeros_initializer(),
shape=[],
dtype=dtypes.int32,
trainable=False,
collections=[collection_name, ops.GraphKeys.LOCAL_VARIABLES],
use_resource=True)
Example #13
| Source File: tpu_estimator.py From transformer-xl with Apache License 2.0 | 4 votes |
|
def _create_or_get_iterations_per_loop():
"""Creates or gets the iterations_per_loop variable.
In TPUEstimator, the user provided computation, the model_fn, is wrapped
inside a tf.while_loop for peak performance. The iterations of the loop are
specified by this variable, which adjusts its value on the CPU after each TPU
program execution and before the next TPU execution.
The purpose of using a variable, rather then a constant, is to allow
TPUEstimator adapt the TPU training iterations according to the final steps
specified by users. For example, if the user sets the iterations_per_loop as 4
in TPUConfig and steps as 10 in TPUEstimator.train(), the iterations_per_loop
variable will have the following value before each TPU training.
- 1-th TPU execution: iterations_per_loop = 4
- 2-th TPU execution: iterations_per_loop = 4
- 3-th TPU execution: iterations_per_loop = 2
As model_fn increases the global step once per train_op invocation, the global
step is 10 after all TPU executions, matching the steps=10 inputs passed in by
users.
Returns:
A TF non-trainable resource variable.
Raises:
RuntimeError: If multi iterations_per_loop variables were found.
"""
graph = ops.get_default_graph()
collection_name = '{}_{}'.format(_TPU_ESTIMATOR, _ITERATIONS_PER_LOOP_VAR)
iter_vars = graph.get_collection(collection_name)
if len(iter_vars) == 1:
return iter_vars[0]
elif len(iter_vars) > 1:
raise RuntimeError('Multiple iterations_per_loop_var in collection.')
with ops.colocate_with(training_util.get_global_step()):
with variable_scope.variable_scope(
_TPU_ESTIMATOR, reuse=variable_scope.AUTO_REUSE):
return variable_scope.get_variable(
_ITERATIONS_PER_LOOP_VAR,
initializer=init_ops.zeros_initializer(),
shape=[],
dtype=dtypes.int32,
trainable=False,
collections=[collection_name, ops.GraphKeys.LOCAL_VARIABLES],
use_resource=True)
Example #14
| Source File: tpu_estimator.py From xlnet with Apache License 2.0 | 4 votes |
|
def _create_or_get_iterations_per_loop():
"""Creates or gets the iterations_per_loop variable.
In TPUEstimator, the user provided computation, the model_fn, is wrapped
inside a tf.while_loop for peak performance. The iterations of the loop are
specified by this variable, which adjusts its value on the CPU after each TPU
program execution and before the next TPU execution.
The purpose of using a variable, rather then a constant, is to allow
TPUEstimator adapt the TPU training iterations according to the final steps
specified by users. For example, if the user sets the iterations_per_loop as 4
in TPUConfig and steps as 10 in TPUEstimator.train(), the iterations_per_loop
variable will have the following value before each TPU training.
- 1-th TPU execution: iterations_per_loop = 4
- 2-th TPU execution: iterations_per_loop = 4
- 3-th TPU execution: iterations_per_loop = 2
As model_fn increases the global step once per train_op invocation, the global
step is 10 after all TPU executions, matching the steps=10 inputs passed in by
users.
Returns:
A TF non-trainable resource variable.
Raises:
RuntimeError: If multi iterations_per_loop variables were found.
"""
graph = ops.get_default_graph()
collection_name = '{}_{}'.format(_TPU_ESTIMATOR, _ITERATIONS_PER_LOOP_VAR)
iter_vars = graph.get_collection(collection_name)
if len(iter_vars) == 1:
return iter_vars[0]
elif len(iter_vars) > 1:
raise RuntimeError('Multiple iterations_per_loop_var in collection.')
with ops.colocate_with(training_util.get_global_step()):
with variable_scope.variable_scope(
_TPU_ESTIMATOR, reuse=variable_scope.AUTO_REUSE):
return variable_scope.get_variable(
_ITERATIONS_PER_LOOP_VAR,
initializer=init_ops.zeros_initializer(),
shape=[],
dtype=dtypes.int32,
trainable=False,
collections=[collection_name, ops.GraphKeys.LOCAL_VARIABLES],
use_resource=True)
