From 361a53930f9162bb79af4f0d985350b44e84c762 Mon Sep 17 00:00:00 2001
From: Zhou Wei <52485244+zhouwei25@users.noreply.github.com>
Date: Mon, 16 Nov 2020 22:26:00 +0800
Subject: [PATCH] fix doc of save/load (#28645)

---
 python/paddle/framework/io.py | 2 --
 python/paddle/optimizer/lr.py | 6 ++++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/python/paddle/framework/io.py b/python/paddle/framework/io.py
index 945c8160b4..d794fce5e3 100644
--- a/python/paddle/framework/io.py
+++ b/python/paddle/framework/io.py
@@ -228,7 +228,6 @@ def save(obj, path):
             emb = paddle.nn.Embedding(10, 10)
             layer_state_dict = emb.state_dict()
             paddle.save(layer_state_dict, "emb.pdparams")
-
             scheduler = paddle.optimizer.lr.NoamDecay(	
                 d_model=0.01, warmup_steps=100, verbose=True)
             adam = paddle.optimizer.Adam(
@@ -320,7 +319,6 @@ def load(path, **configs):
             emb = paddle.nn.Embedding(10, 10)
             layer_state_dict = emb.state_dict()
             paddle.save(layer_state_dict, "emb.pdparams")
-
             scheduler = paddle.optimizer.lr.NoamDecay(	
                 d_model=0.01, warmup_steps=100, verbose=True)
             adam = paddle.optimizer.Adam(
diff --git a/python/paddle/optimizer/lr.py b/python/paddle/optimizer/lr.py
index e4fb54c229..2d5dc5d998 100644
--- a/python/paddle/optimizer/lr.py
+++ b/python/paddle/optimizer/lr.py
@@ -1434,7 +1434,8 @@ class CosineAnnealingDecay(LRScheduler):
                     loss.backward()
                     sgd.step()
                     sgd.clear_gradients()
-                scheduler.step()
+                    scheduler.step()    # If you update learning rate each step
+              # scheduler.step()        # If you update learning rate each epoch
 
             # train on static graph mode
             paddle.enable_static()
@@ -1460,7 +1461,8 @@ class CosineAnnealingDecay(LRScheduler):
                             'y': np.random.randn(3, 4, 5).astype('float32')
                         },
                         fetch_list=loss.name)
-                scheduler.step()
+                    scheduler.step()    # If you update learning rate each step
+              # scheduler.step()        # If you update learning rate each epoch
     """
 
     def __init__(self,
-- 
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