parameters: (learning_rate, global_step, decay_steps, decay_rate,
                        staircase=False, name=None)
docstring: Applies exponential decay to the learning rate.
  
    When training a model, it is often recommended to lower the learning rate as
    the training progresses.  This function applies an exponential decay function
    to a provided initial learning rate.  It requires a `global_step` value to
    compute the decayed learning rate.  You can just pass a TensorFlow variable
    that you increment at each training step.
  
    The function returns the decayed learning rate.  It is computed as:
  
    ```python
    decayed_learning_rate = learning_rate *
                            decay_rate ^ (global_step / decay_steps)
    ```
  
    If the argument `staircase` is `True`, then `global_step / decay_steps` is an
    integer division and the decayed learning rate follows a staircase function.
  
    Example: decay every 100000 steps with a base of 0.96:
  
    ```python
    ...
    global_step = tf.Variable(0, trainable=False)
    starter_learning_rate = 0.1
    learning_rate = tf.train.exponential_decay(starter_learning_rate, global_step,
                                               100000, 0.96, staircase=True)
    # Passing global_step to minimize() will increment it at each step.
    learning_step = (
        tf.train.GradientDescentOptimizer(learning_rate)
        .minimize(...my loss..., global_step=global_step)
    )
    ```
  
    Args:
      learning_rate: A scalar `float32` or `float64` `Tensor` or a
        Python number.  The initial learning rate.
      global_step: A scalar `int32` or `int64` `Tensor` or a Python number.
        Global step to use for the decay computation.  Must not be negative.
      decay_steps: A scalar `int32` or `int64` `Tensor` or a Python number.
        Must be positive.  See the decay computation above.
      decay_rate: A scalar `float32` or `float64` `Tensor` or a
        Python number.  The decay rate.
      staircase: Boolean.  It `True` decay the learning rate at discrete intervals
      name: String.  Optional name of the operation.  Defaults to 
        'ExponentialDecay'
  
    Returns:
      A scalar `Tensor` of the same type as `learning_rate`.  The decayed
      learning rate.
docstring_summary: Applies exponential decay to the learning rate.
docstring_tokens: Applies exponential decay to the learning rate .
function: def exponential_decay(learning_rate, global_step, decay_steps, decay_rate,
                        staircase=False, name=None):
    """Applies exponential decay to the learning rate.
  
    When training a model, it is often recommended to lower the learning rate as
    the training progresses.  This function applies an exponential decay function
    to a provided initial learning rate.  It requires a `global_step` value to
    compute the decayed learning rate.  You can just pass a TensorFlow variable
    that you increment at each training step.
  
    The function returns the decayed learning rate.  It is computed as:
  
    ```python
    decayed_learning_rate = learning_rate *
                            decay_rate ^ (global_step / decay_steps)
    ```
  
    If the argument `staircase` is `True`, then `global_step / decay_steps` is an
    integer division and the decayed learning rate follows a staircase function.
  
    Example: decay every 100000 steps with a base of 0.96:
  
    ```python
    ...
    global_step = tf.Variable(0, trainable=False)
    starter_learning_rate = 0.1
    learning_rate = tf.train.exponential_decay(starter_learning_rate, global_step,
                                               100000, 0.96, staircase=True)
    # Passing global_step to minimize() will increment it at each step.
    learning_step = (
        tf.train.GradientDescentOptimizer(learning_rate)
        .minimize(...my loss..., global_step=global_step)
    )
    ```
  
    Args:
      learning_rate: A scalar `float32` or `float64` `Tensor` or a
        Python number.  The initial learning rate.
      global_step: A scalar `int32` or `int64` `Tensor` or a Python number.
        Global step to use for the decay computation.  Must not be negative.
      decay_steps: A scalar `int32` or `int64` `Tensor` or a Python number.
        Must be positive.  See the decay computation above.
      decay_rate: A scalar `float32` or `float64` `Tensor` or a
        Python number.  The decay rate.
      staircase: Boolean.  It `True` decay the learning rate at discrete intervals
      name: String.  Optional name of the operation.  Defaults to 
        'ExponentialDecay'
  
    Returns:
      A scalar `Tensor` of the same type as `learning_rate`.  The decayed
      learning rate.
    """
    with ops.op_scope([learning_rate, global_step, decay_steps, decay_rate],
                      name, "ExponentialDecay") as name:
      learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate")
      dtype = learning_rate.dtype
      global_step = math_ops.cast(global_step, dtype)
      decay_steps = math_ops.cast(decay_steps, dtype)
      decay_rate = math_ops.cast(decay_rate, dtype)
      p = global_step / decay_steps
      if staircase:
        p = math_ops.floor(p)
      return math_ops.mul(learning_rate, math_ops.pow(decay_rate, p), name=name)
function_tokens: def exponential_decay ( learning_rate , global_step , decay_steps , decay_rate , staircase = False , name = None ) : with ops . op_scope ( [ learning_rate , global_step , decay_steps , decay_rate ] , name , "ExponentialDecay" ) as name : learning_rate = ops . convert_to_tensor ( learning_rate , name = "learning_rate" ) dtype = learning_rate . dtype global_step = math_ops . cast ( global_step , dtype ) decay_steps = math_ops . cast ( decay_steps , dtype ) decay_rate = math_ops . cast ( decay_rate , dtype ) p = global_step / decay_steps if staircase : p = math_ops . floor ( p ) return math_ops . mul ( learning_rate , math_ops . pow ( decay_rate , p ) , name = name )