tf.train.exponential_decay 用法

最新推荐文章于 2022-03-08 16:58:06 发布

壮如山的汉子

最新推荐文章于 2022-03-08 16:58:06 发布

阅读量5.8k

点赞数

CC 4.0 BY-SA版权

文章标签：人工智能深度学习 tensorflow 函数

本文链接：https://blog.youkuaiyun.com/yuezhilanyi/article/details/78077205

本文介绍如何在TensorFlow中使用tf.train.exponential_decay()实现学习率的指数衰减，并提供了一个具体的示例代码，展示了如何设置每批次更新的学习率以及如何正确地使用global_step参数。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

tf.train.exponential_decay() 为tensorflow提供的指数衰减学习率方法。

但是仅仅调用该函数并不能达到衰减学习率的效果，必须要特定的格式才能达到效果，以下为调用范例

  # Optimizer: set up a variable that's incremented once per batch and
  # controls the learning rate decay.
  batch = tf.Variable(0, dtype=data_type())
  # Decay once per epoch, using an exponential schedule starting at 0.01.
  learning_rate = tf.train.exponential_decay(
      0.01,                # Base learning rate.
      batch * BATCH_SIZE,  # Current index into the dataset.
      train_size,          # Decay step.
      0.95,                # Decay rate.
      staircase=True)
  # Use simple momentum for the optimization.
  optimizer = tf.train.MomentumOptimizer(learning_rate,
                                         0.9).minimize(loss,
                                                       global_step=batch)

需要特别注意的一点是，在优化器中必须定义“global_step"这个参数，否则batch参数不会随着优化器更新参数而迭代。

附tf.train.exponential_decay()的说明文档：

  def minimize(self, loss, global_step=None, var_list=None,
               gate_gradients=GATE_OP, aggregation_method=None,
               colocate_gradients_with_ops=False, name=None,
               grad_loss=None):
    """Add operations to minimize `loss` by updating `var_list`.

    This method simply combines calls `compute_gradients()` and
    `apply_gradients()`. If you want to process the gradient before applying
    them call `compute_gradients()` and `apply_gradients()` explicitly instead
    of using this function.

    Args:
      loss: A `Tensor` containing the value to minimize.
      global_step: Optional `Variable` to increment by one after the
        variables have been updated.
      var_list: Optional list or tuple of `Variable` objects to update to
        minimize `loss`.  Defaults to the list of variables collected in
        the graph under the key `GraphKeys.TRAINABLE_VARIABLES`.
      gate_gradients: How to gate the computation of gradients.  Can be
        `GATE_NONE`, `GATE_OP`, or  `GATE_GRAPH`.
      aggregation_method: Specifies the method used to combine gradient terms.
        Valid values are defined in the class `AggregationMethod`.
      colocate_gradients_with_ops: If True, try colocating gradients with
        the corresponding op.
      name: Optional name for the returned operation.
      grad_loss: Optional. A `Tensor` holding the gradient computed for `loss`.

    Returns:
      An Operation that updates the variables in `var_list`.  If `global_step`
      was not `None`, that operation also increments `global_step`.

    Raises:
      ValueError: If some of the variables are not `Variable` objects.
    """