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Python variables.moving_average_variables方法代码示例

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, 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 
开发者ID:JGuillaumin,项目名称:swa-tf,代码行数:43,代码来源:stochastic_weight_averaging.py

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 
开发者ID:ryfeus,项目名称:lambda-packs,代码行数:48,代码来源:moving_averages.py

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 
开发者ID:HiKapok,项目名称:tf.fashionAI,代码行数:49,代码来源:swa_moving_average.py

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 
开发者ID:gustavla,项目名称:self-supervision,代码行数:48,代码来源:moving_averages.py


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