Python tensorflow.python.ops.variables.moving_average_variables() Examples
The following are 8
code examples of tensorflow.python.ops.variables.moving_average_variables().
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Example #1
| Source File: stochastic_weight_averaging.py From swa-tf with BSD 2-Clause "Simplified" License | 5 votes |
|
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
Example #2
| Source File: moving_averages.py From lambda-packs with MIT License | 4 votes |
|
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
Example #3
| Source File: moving_averages.py From auto-alt-text-lambda-api with MIT License | 4 votes |
|
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
Example #4
| Source File: swa_moving_average.py From tf.fashionAI with Apache License 2.0 | 4 votes |
|
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
Example #5
| Source File: moving_averages.py From self-supervision with BSD 3-Clause "New" or "Revised" License | 4 votes |
|
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
Example #6
| Source File: moving_averages.py From deep_image_model with Apache License 2.0 | 4 votes |
|
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
Example #7
| Source File: moving_averages.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 4 votes |
|
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
Example #8
| Source File: moving_averages.py From keras-lambda with MIT License | 4 votes |
|
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
