diff --git a/python/paddle/v2/framework/tests/test_gradient_checker.py b/python/paddle/v2/framework/tests/test_gradient_checker.py
new file mode 100644
index 0000000000000000000000000000000000000000..e0b315120862bea284e067070492dcdfbb661081
--- /dev/null
+++ b/python/paddle/v2/framework/tests/test_gradient_checker.py
@@ -0,0 +1,43 @@
+import unittest
+import numpy
+from paddle.v2.framework.op import Operator
+from gradient_checker import GradientChecker
+from gradient_checker import get_numeric_gradient
+
+
+class GetNumericGradientTest(unittest.TestCase):
+    def test_add_op(self):
+        add_op = Operator('add_two', X="X", Y="Y", Out="Z")
+        x = numpy.random.random((10, 1)).astype("float32")
+        y = numpy.random.random((10, 1)).astype("float32")
+
+        arr = get_numeric_gradient(add_op, {'X': x, "Y": y}, 'Z', 'X')
+        self.assertAlmostEqual(arr.mean(), 1.0, delta=1e-4)
+
+    def test_softmax_op(self):
+        def stable_softmax(x):
+            """Compute the softmax of vector x in a numerically stable way."""
+            shiftx = x - numpy.max(x)
+            exps = numpy.exp(shiftx)
+            return exps / numpy.sum(exps)
+
+        def label_softmax_grad(Y, dY):
+            dX = Y * 0.0
+            for i in range(Y.shape[0]):
+                d = numpy.dot(Y[i, :], dY[i, :])
+                dX[i, :] = Y[i, :] * (dY[i, :] - d)
+            return dX
+
+        softmax_op = Operator("softmax", X="X", Y="Y")
+
+        X = numpy.random.random((2, 2)).astype("float32")
+        Y = numpy.apply_along_axis(stable_softmax, 1, X)
+        dY = numpy.ones(Y.shape)
+        dX = label_softmax_grad(Y, dY)
+
+        arr = get_numeric_gradient(softmax_op, {"X": X}, 'Y', 'X')
+        numpy.testing.assert_almost_equal(arr, dX, decimal=1e-2)
+
+
+if __name__ == '__main__':
+    unittest.main()