From 75fb86d8b8cf0031a18d3e43ba70208bbc16e08d Mon Sep 17 00:00:00 2001
From: PaddlePaddle-Gardener <paddlepaddle_bot@163.com>
Date: Thu, 13 Jan 2022 14:20:59 +0800
Subject: [PATCH] mirgate_38782

---
 python/paddle/optimizer/lr.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/python/paddle/optimizer/lr.py b/python/paddle/optimizer/lr.py
index d4fafba922..90117f99ab 100644
--- a/python/paddle/optimizer/lr.py
+++ b/python/paddle/optimizer/lr.py
@@ -398,7 +398,7 @@ class NaturalExpDecay(LRScheduler):
 
     Args:
         learning_rate (float): The initial learning rate. It is a python float number.
-        gamma (float, optional): A Ratio to update the learning rate. Default: 0.1.
+        gamma (float, optional): A Ratio to update the learning rate, should greater than 0.0 to make learning rate decay. Default: 0.1.
         last_epoch (int, optional):  The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
         verbose (bool, optional): If ``True``, prints a message to stdout for each update. Default: ``False`` .
 
@@ -456,6 +456,7 @@ class NaturalExpDecay(LRScheduler):
     """
 
     def __init__(self, learning_rate, gamma, last_epoch=-1, verbose=False):
+        assert gamma > 0.0, " 'gamma' must be a positive number so that the learning rate will decay."
         self.gamma = gamma
         super(NaturalExpDecay, self).__init__(learning_rate, last_epoch,
                                               verbose)
@@ -573,7 +574,7 @@ class PolynomialDecay(LRScheduler):
         learning_rate (float): The initial learning rate. It is a python float number.
         decay_steps(int): The decay step size. It determines the decay cycle. It must be a positive integer.
         end_lr(float, optional): The minimum final learning rate. Default: 0.0001.
-        power(float, optional): Power of polynomial. Default: 1.0.
+        power(float, optional): Power of polynomial, should greater than 0.0 to get learning rate decay. Default: 1.0.
         cycle(bool, optional): Whether the learning rate rises again. If True, then the learning rate will rise when it decrease
             to ``end_lr`` .  If False, the learning rate is monotone decreasing. Default: False.
         last_epoch (int, optional):  The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
@@ -644,6 +645,7 @@ class PolynomialDecay(LRScheduler):
             decay_steps, int), " 'decay_steps' must be a positive integer."
         self.decay_steps = decay_steps
         self.end_lr = end_lr
+        assert power > 0.0, " 'power' must be greater than 0.0 so that the learning rate will decay."
         self.power = power
         self.cycle = cycle
         super(PolynomialDecay, self).__init__(learning_rate, last_epoch,
@@ -820,7 +822,7 @@ class ExponentialDecay(LRScheduler):
     Args:
         learning_rate (float): The initial learning rate. It is a python float number.
         gamma (float): The Ratio that the learning rate will be reduced. ``new_lr = origin_lr * gamma`` .
-            It should be less than 1.0.
+            It should be in interval (0.0, 1.0).
         last_epoch (int, optional):  The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
         verbose (bool, optional): If ``True``, prints a message to stdout for each update. Default: ``False`` .
 
@@ -878,6 +880,7 @@ class ExponentialDecay(LRScheduler):
     """
 
     def __init__(self, learning_rate, gamma, last_epoch=-1, verbose=False):
+        assert gamma > 0.0 and gamma < 1.0, " 'gamma' must be in interval (0.0, 1.0) so that the learning rate will decay."
         self.gamma = gamma
         super(ExponentialDecay, self).__init__(learning_rate, last_epoch,
                                                verbose)
-- 
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