本文整理汇总了Python中tensorflow.python.ops.variables.moving_average_variables方法的典型用法代码示例。如果您正苦于以下问题：Python variables.moving_average_variables方法的具体用法？Python variables.moving_average_variables怎么用？Python variables.moving_average_variables使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.python.ops.variables的用法示例。
在下文中一共展示了variables.moving_average_variables方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: variables_to_restore
# 需要导入模块: from tensorflow.python.ops import variables [as 别名]
# 或者: from tensorflow.python.ops.variables import moving_average_variables [as 别名]
def variables_to_restore(self, moving_avg_variables=None):
"""Returns a map of names to `Variables` to restore.
If a variable has a moving average, use the moving average variable name as
the restore name; otherwise, use the variable name.
For example,
```python
variables_to_restore = ema.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
```
Below is an example of such mapping:
```
conv/batchnorm/gamma/ExponentialMovingAverage: conv/batchnorm/gamma,
conv_4/conv2d_params/ExponentialMovingAverage: conv_4/conv2d_params,
global_step: global_step
```
Args:
moving_avg_variables: a list of variables that require to use of the
moving variable name to be restored. If None, it will default to
variables.moving_average_variables() + variables.trainable_variables()
Returns:
A map from restore_names to variables. The restore_name can be the
moving_average version of the variable name if it exist, or the original
variable name.
"""
name_map = {}
if moving_avg_variables is None:
# Include trainable variables and variables which have been explicitly
# added to the moving_average_variables collection.
moving_avg_variables = variables.trainable_variables()
moving_avg_variables += variables.moving_average_variables()
# Remove duplicates
moving_avg_variables = set(moving_avg_variables)
# Collect all the variables with moving average,
for v in moving_avg_variables:
name_map[self.average_name(v)] = v
# Make sure we restore variables without moving averages as well.
moving_avg_variable_names = set([v.name for v in moving_avg_variables])
for v in list(set(variables.global_variables())):
if v.name not in moving_avg_variable_names and v.op.name not in name_map:
name_map[v.op.name] = v
return name_map 示例2: variables_to_restore
# 需要导入模块: from tensorflow.python.ops import variables [as 别名]
# 或者: from tensorflow.python.ops.variables import moving_average_variables [as 别名]
def variables_to_restore(self, moving_avg_variables=None):
"""Returns a map of names to `Variables` to restore.
If a variable has a moving average, use the moving average variable name as
the restore name; otherwise, use the variable name.
For example,
```python
variables_to_restore = ema.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
```
Below is an example of such mapping:
```
conv/batchnorm/gamma/ExponentialMovingAverage: conv/batchnorm/gamma,
conv_4/conv2d_params/ExponentialMovingAverage: conv_4/conv2d_params,
global_step: global_step
```
Args:
moving_avg_variables: a list of variables that require to use of the
moving variable name to be restored. If None, it will default to
variables.moving_average_variables() + variables.trainable_variables()
Returns:
A map from restore_names to variables. The restore_name can be the
moving_average version of the variable name if it exist, or the original
variable name.
"""
name_map = {}
if moving_avg_variables is None:
# Include trainable variables and variables which have been explicitly
# added to the moving_average_variables collection.
moving_avg_variables = variables.trainable_variables()
moving_avg_variables += variables.moving_average_variables()
# Remove duplicates
moving_avg_variables = set(moving_avg_variables)
# Collect all the variables with moving average,
for v in moving_avg_variables:
name_map[self.average_name(v)] = v
# Make sure we restore variables without moving average as well.
for v in list(set(variables.global_variables()) - moving_avg_variables):
if v.op.name not in name_map:
name_map[v.op.name] = v
return name_map 示例3: variables_to_restore
# 需要导入模块: from tensorflow.python.ops import variables [as 别名]
# 或者: from tensorflow.python.ops.variables import moving_average_variables [as 别名]
def variables_to_restore(self, moving_avg_variables=None):
"""Returns a map of names to `Variables` to restore.
If a variable has a moving average, use the moving average variable name as
the restore name; otherwise, use the variable name.
For example,
```python
variables_to_restore = ema.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
```
Below is an example of such mapping:
```
conv/batchnorm/gamma/ExponentialMovingAverage: conv/batchnorm/gamma,
conv_4/conv2d_params/ExponentialMovingAverage: conv_4/conv2d_params,
global_step: global_step
```
Args:
moving_avg_variables: a list of variables that require to use of the
moving variable name to be restored. If None, it will default to
variables.moving_average_variables() + variables.trainable_variables()
Returns:
A map from restore_names to variables. The restore_name can be the
moving_average version of the variable name if it exist, or the original
variable name.
"""
name_map = {}
if moving_avg_variables is None:
# Include trainable variables and variables which have been explicitly
# added to the moving_average_variables collection.
moving_avg_variables = variables.trainable_variables()
moving_avg_variables += variables.moving_average_variables()
# Remove duplicates
moving_avg_variables = set(moving_avg_variables)
# Collect all the variables with moving average,
for v in moving_avg_variables:
name_map[self.average_name(v)] = v
# Make sure we restore variables without moving averages as well.
moving_avg_variable_names = set([v.name for v in moving_avg_variables])
for v in list(set(variables.global_variables())):
if v.name not in moving_avg_variable_names and v.op.name not in name_map:
name_map[v.op.name] = v
return name_map 示例4: variables_to_restore
# 需要导入模块: from tensorflow.python.ops import variables [as 别名]
# 或者: from tensorflow.python.ops.variables import moving_average_variables [as 别名]
def variables_to_restore(self, moving_avg_variables=None):
"""Returns a map of names to `Variables` to restore.
If a variable has a moving average, use the moving average variable name as
the restore name; otherwise, use the variable name.
For example,
```python
variables_to_restore = ema.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
```
Below is an example of such mapping:
```
conv/batchnorm/gamma/ExponentialMovingAverage: conv/batchnorm/gamma,
conv_4/conv2d_params/ExponentialMovingAverage: conv_4/conv2d_params,
global_step: global_step
```
Args:
moving_avg_variables: a list of variables that require to use of the
moving variable name to be restored. If None, it will default to
variables.moving_average_variables() + variables.trainable_variables()
Returns:
A map from restore_names to variables. The restore_name can be the
moving_average version of the variable name if it exist, or the original
variable name.
"""
name_map = {}
if moving_avg_variables is None:
# Include trainable variables and variables which have been explicitly
# added to the moving_average_variables collection.
moving_avg_variables = variables.trainable_variables()
moving_avg_variables += variables.moving_average_variables()
# Remove duplicates
moving_avg_variables = set(moving_avg_variables)
# Collect all the variables with moving average,
for v in moving_avg_variables:
name_map[self.average_name(v)] = v
# Make sure we restore variables without moving average as well.
for v in list(set(variables.all_variables()) - moving_avg_variables):
if v.op.name not in name_map:
name_map[v.op.name] = v
return name_map