change end_lr to end_learning_rate

Author: Asthestarsfalll

python/paddle/fluid/tests/unittests/test_lr_scheduler.py

@@ -325,7 +325,7 @@ def one_cycle_lr(epoch_num,
                  max_learning_rate,
                  total_steps,
                  divide_factor=25,
-                 end_lr=0.0001,
+                 end_learning_rate=0.0001,
                  phase_pct=0.3,
                  anneal_strategy='cos',
                  three_phase=False,
@@ -347,7 +347,7 @@ def one_cycle_lr(epoch_num,
             },
             {
                 'start_lr': initial_lr,
-                'end_lr': end_lr,
+                'end_lr': end_learning_rate,
             },
         ]
     else:
@@ -359,7 +359,7 @@ def one_cycle_lr(epoch_num,
             },
             {
                 'start_lr': max_learning_rate,
-                'end_lr': end_lr,
+                'end_lr': end_learning_rate,
             },
         ]
 
@@ -539,10 +539,10 @@ def test_scheduler(self):
                 max_learning_rate=-1.5, total_steps=20)
         with self.assertRaises(TypeError):
             paddle.optimizer.lr.OneCycleLR(
-                max_learning_rate=0.1, total_steps=20, end_lr='test')
+                max_learning_rate=0.1, total_steps=20, end_learning_rate='test')
         with self.assertRaises(ValueError):
             paddle.optimizer.lr.OneCycleLR(
-                max_learning_rate=0.1, total_steps=20, end_lr=-1)
+                max_learning_rate=0.1, total_steps=20, end_learning_rate=-1)
         with self.assertRaises(TypeError):
             paddle.optimizer.lr.OneCycleLR(
                 max_learning_rate=0.1, total_steps='test')
@@ -622,31 +622,31 @@ def test_scheduler(self):
             "max_learning_rate": 0.1,
             "total_steps": 20,
             "divide_factor": 5,
-            "end_lr": 0.0001,
+            "end_learning_rate": 0.0001,
             "anneal_strategy": 'cos',
             "phase_pct": 0.3,
             "three_phase": False,
         }), (one_cycle_lr, paddle.optimizer.lr.OneCycleLR, {
             "max_learning_rate": 0.5,
             "total_steps": 20,
             "divide_factor": 10,
-            "end_lr": 0.001,
+            "end_learning_rate": 0.001,
             "anneal_strategy": 'linear',
             "phase_pct": 0.4,
             "three_phase": False,
         }), (one_cycle_lr, paddle.optimizer.lr.OneCycleLR, {
             "max_learning_rate": 1.0,
             "total_steps": 20,
             "divide_factor": 9,
-            "end_lr": 0.0001,
+            "end_learning_rate": 0.0001,
             "anneal_strategy": 'cos',
             "phase_pct": 0.3,
             "three_phase": True,
         }), (one_cycle_lr, paddle.optimizer.lr.OneCycleLR, {
             "max_learning_rate": 0.3,
             "total_steps": 20,
             "divide_factor": 25,
-            "end_lr": 0.0005,
+            "end_learning_rate": 0.0005,
             "anneal_strategy": 'linear',
             "phase_pct": 0.2,
             "three_phase": True,

python/paddle/optimizer/lr.py

@@ -1613,7 +1613,7 @@ class OneCycleLR(LRScheduler):
              Functionally, it defines the initial learning rate by ``divide_factor`` .
         total_steps (int): Number of total training steps.
         divide_factor (float): Initial learning rate will be determined by initial_learning_rate = max_learning_rate / divide_factor. Default: 25.
-        end_lr (float, optional): The minimum learning rate during training, it should be much less than initial learning rate.
+        end_learning_rate (float, optional): The minimum learning rate during training, it should be much less than initial learning rate.
         phase_pct (float): The percentage of total steps which used to increasing learning rate. Default: 0.3.
         anneal_strategy (str, optional): Strategy of adjusting learning rate.'cos' for cosine annealing,
             'linear' for linear annealing. Default: 'cos'.
@@ -1682,7 +1682,7 @@ def __init__(self,
                  max_learning_rate,
                  total_steps,
                  divide_factor=25.,
-                 end_lr=0.0001,
+                 end_learning_rate=0.0001,
                  phase_pct=0.3,
                  anneal_strategy='cos',
                  three_phase=False,
@@ -1696,13 +1696,13 @@ def __init__(self,
         if max_learning_rate < 0:
             raise ValueError("'max_learning_rate' must be a positive integer.")
 
-        # Check type and value of end_lr
-        if not isinstance(end_lr, (float, int)):
+        # Check type and value of end_learning_rate
+        if not isinstance(end_learning_rate, (float, int)):
             raise TypeError(
-                "'end_lr' must be 'float' or 'int', but received {}".format(
-                    type(total_steps)))
-        if end_lr < 0:
-            raise ValueError("'end_lr' must be a positive integer.")
+                "'end_learning_rate' must be 'float' or 'int', but received {}".
+                format(type(total_steps)))
+        if end_learning_rate < 0:
+            raise ValueError("'end_learning_rate' must be a positive integer.")
 
         # Check type and value of total_steps
         if not isinstance(total_steps, int):
@@ -1728,7 +1728,7 @@ def __init__(self,
                 format(type(divide_factor)))
 
         initial_lr = max_learning_rate / float(divide_factor)
-        min_lr = float(end_lr)
+        min_lr = float(end_learning_rate)
 
         if three_phase:
             if phase_pct >= 0.5: