diff --git a/python/paddle/v2/fluid/layers/math_op_patch.py b/python/paddle/v2/fluid/layers/math_op_patch.py
index 9b5f22759cf5ccba841919ec158ff58e6505dcf7..4cf995ec8552b3725986ba8e83168430b3154325 100644
--- a/python/paddle/v2/fluid/layers/math_op_patch.py
+++ b/python/paddle/v2/fluid/layers/math_op_patch.py
@@ -151,7 +151,11 @@ def monkey_patch_variable():
         ("__div__", "elementwise_div", False),
         ("__rdiv__", "elementwise_div", True),
         ("__pow__", "elementwise_pow", False),
-        ("__rpow__", "elementwise_pow", True)):
+        ("__rpow__", "elementwise_pow", True),
+            # for logical compare
+        ("__eq__", "equal", False),
+        ("__lt__", "less_then", False),
+        ("__le__", "less_equal", False), ):
         setattr(Variable, method_name,
                 _elemwise_method_creator_(method_name, op_type, reverse))
 
diff --git a/python/paddle/v2/fluid/learning_rate_decay.py b/python/paddle/v2/fluid/learning_rate_decay.py
index 2a2a29fd9cbedc138dc82ca75ccd78208fd33195..0826d3da79a96590f00159a2d2e6f069792909c4 100644
--- a/python/paddle/v2/fluid/learning_rate_decay.py
+++ b/python/paddle/v2/fluid/learning_rate_decay.py
@@ -179,7 +179,7 @@ def polynomial_decay(learning_rate,
                 shape=[1], dtype='float32', value=1.0)
 
             with layers.Switch() as switch:
-                with switch.case(layers.equal(x=global_step, y=zero_var)):
+                with switch.case(global_step == zero_var):
                     layers.assign(input=one_var, output=div_res)
             decay_steps = decay_steps * div_res
         else:
@@ -229,7 +229,7 @@ def piecewise_decay(global_step, boundaries, values):
                     shape=[1], dtype='float32', value=float(boundaries[i]))
                 value_var = layers.fill_constant(
                     shape=[1], dtype='float32', value=float(values[i]))
-                with switch.case(layers.less_than(global_step, boundary_val)):
+                with switch.case(global_step < boundary_val):
                     layers.assign(value_var, lr)
             last_value_var = layers.fill_constant(
                 shape=[1],