Fix learning rate scheduler being unexpectedly overwritten by optimizer's default value

python/mxnet/optimizer/optimizer.py

@@ -63,8 +63,10 @@ class Optimizer(object):
     clip_gradient : float, optional, default None
         Clip the gradient by projecting onto the box ``[-clip_gradient, clip_gradient]``.
 
-    learning_rate : float, optional, default 0.01
-        The initial learning rate.
+    learning_rate : float, optional, default None
+        The initial learning rate. If None, the optimization will use the
+        learning rate from ``lr_scheduler``. If not None, it will overwrite
+        the learning rate in ``lr_scheduler``.
 
     lr_scheduler : LRScheduler, optional, default None
         The learning rate scheduler.
@@ -97,14 +99,19 @@ class Optimizer(object):
         optimizer, its learning rate can be accessed as optimizer.learning_rate.
     """
     def __init__(self, rescale_grad=1., param_idx2name=None, wd=0.,
-                 clip_gradient=None, learning_rate=0.01,
+                 clip_gradient=None, learning_rate=None,
                  lr_scheduler=None, sym=None, begin_num_update=0,
                  multi_precision=False, param_dict=None):
         self.rescale_grad = rescale_grad
-        self.lr = learning_rate
         self.lr_scheduler = lr_scheduler
-        if lr_scheduler is not None:
-            self.lr_scheduler.base_lr = learning_rate
+        if self.lr_scheduler is None and learning_rate is None:
+            learning_rate = 0.01
+        self.lr = learning_rate
+        if self.lr_scheduler is not None and learning_rate is not None:
+            if self.lr_scheduler.base_lr != learning_rate:
+                raise UserWarning("learning rate from ``lr_scheduler`` has been "
+                                   "overwritten by ``learning_rate`` in optimizer.")
+                self.lr_scheduler.base_lr = learning_rate
 
         self.wd = wd
         self.lr_mult = {}

tests/python/unittest/test_optimizer.py

@@ -39,6 +39,10 @@ def test_learning_rate():
     o2.lr_scheduler.base_lr = 0.4
     assert o2.learning_rate == 0.4
 
+    lr_s = lr_scheduler.FactorScheduler(base_lr=1024)
+    o3 = mx.optimizer.Optimizer(lr_scheduler=lr_s)
+    assert o3.learning_rate == 1024
+
 
 @raises(UserWarning)
 @with_seed()