Adding the default attribute test case

paddle/operators/rmsprop_op.cc

@@ -89,13 +89,13 @@ class RmspropOpMaker : public framework::OpProtoAndCheckerMaker {
     AddAttr<float>("epsilon",
                    "(float, default 1e-10) Constant "
                    "for numerical stability.")
-        .SetDefault(1e-10);
+        .SetDefault(1.0e-10f);
     AddAttr<float>("decay",
                    "(float, default 0.9) "
                    "Discounting factor for coming gradient.")
-        .SetDefault(0.9);
+        .SetDefault(0.9f);
     AddAttr<float>("momentum", "(float, default 0.0) Constant value")
-        .SetDefault(0.0);
+        .SetDefault(0.0f);
     AddComment(R"DOC(
 
 RMSprop

paddle/operators/rmsprop_op.h

@@ -28,9 +28,9 @@ template <typename Place, typename T>
 class RmspropOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
-    auto param_out = ctx.Output<Tensor>("ParamOut");
-    auto moment_out = ctx.Output<Tensor>("MomentOut");
-    auto mean_square_out = ctx.Output<Tensor>("MeanSquareOut");
+    auto* param_out = ctx.Output<Tensor>("ParamOut");
+    auto* moment_out = ctx.Output<Tensor>("MomentOut");
+    auto* mean_square_out = ctx.Output<Tensor>("MeanSquareOut");
 
     param_out->mutable_data<T>(ctx.GetPlace());
     moment_out->mutable_data<T>(ctx.GetPlace());

python/paddle/v2/framework/tests/test_rmsprop_op.py

@@ -3,7 +3,10 @@ import numpy as np
 from op_test import OpTest
 
 
-class TestRmspropOp(OpTest):
+class TestRmspropOp1(OpTest):
+    ''' Test RMSProp with explicit inputs
+    '''
+
     def setUp(self):
         self.op_type = "rmsprop"
 
@@ -42,5 +45,45 @@ class TestRmspropOp(OpTest):
         self.check_output()
 
 
+class TestRmspropOp2(OpTest):
+    '''Test RMSProp with defaukt values for attributes
+    '''
+
+    def setUp(self):
+        self.op_type = "rmsprop"
+
+        param = np.random.random((123, 321)).astype("float32")
+        mean_square = np.random.random((123, 321)).astype("float32")
+        learning_rate = np.array([0.01]).astype("float32")
+        grad = np.random.random((123, 321)).astype("float32")
+        moment = np.zeros((123, 321)).astype("float32")
+
+        epsilon = 1.0e-10
+        decay = 0.9
+        momentum = 0.0
+
+        self.inputs = {
+            'Param': param,
+            'MeanSquare': mean_square,
+            'LearningRate': learning_rate,
+            'Grad': grad,
+            'Moment': moment,
+        }
+
+        ms_out = decay * mean_square + (1 - decay) * grad * grad
+        moment_out = momentum * moment + \
+            learning_rate * grad / np.sqrt(ms_out + epsilon)
+        param_out = param - moment_out
+
+        self.outputs = {
+            'ParamOut': param_out,
+            'MomentOut': moment_out,
+            'MeanSquareOut': ms_out
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+
 if __name__ == "__main__":
     unittest.main()