diff --git a/python/paddle/v2/fluid/tests/test_elementwise_max_op.py b/python/paddle/v2/fluid/tests/test_elementwise_max_op.py
new file mode 100644
index 0000000000000000000000000000000000000000..52bd123d8020d8d4572a1fbc0c03a4206604c5ef
--- /dev/null
+++ b/python/paddle/v2/fluid/tests/test_elementwise_max_op.py
@@ -0,0 +1,107 @@
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestElementwiseOp(OpTest):
+    def setUp(self):
+        self.op_type = "elementwise_max"
+        # If x and y have the same value, the max() is not differentiable.
+        # So we generate test data by the following method
+        # to avoid them being too close to each other.
+        x = np.random.uniform(0.1, 1, [13, 17]).astype("float32")
+        sgn = np.random.choice([-1, 1], [13, 17]).astype("float32")
+        y = x + sgn * np.random.uniform(0.1, 1, [13, 17]).astype("float32")
+        self.inputs = {'X': x, 'Y': y}
+        self.outputs = {'Out': np.maximum(self.inputs['X'], self.inputs['Y'])}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad_normal(self):
+        self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.005)
+
+    def test_check_grad_ingore_x(self):
+        self.check_grad(
+            ['Y'], 'Out', max_relative_error=0.005, no_grad_set=set("X"))
+
+    def test_check_grad_ingore_y(self):
+        self.check_grad(
+            ['X'], 'Out', max_relative_error=0.005, no_grad_set=set('Y'))
+
+
+class TestElementwiseMaxOp_Vector(TestElementwiseOp):
+    def setUp(self):
+        self.op_type = "elementwise_max"
+        x = np.random.random((32, )).astype("float32")
+        sgn = np.random.choice([-1, 1], (32, )).astype("float32")
+        y = x + sgn * np.random.uniform(0.1, 1, (32, )).astype("float32")
+        self.inputs = {'X': x, 'Y': y}
+        self.outputs = {'Out': np.maximum(self.inputs['X'], self.inputs['Y'])}
+
+
+class TestElementwiseMaxOp_broadcast_0(TestElementwiseOp):
+    def setUp(self):
+        self.op_type = "elementwise_max"
+        x = np.random.uniform(0.5, 1, (2, 3, 4)).astype(np.float32)
+        sgn = np.random.choice([-1, 1], (2, )).astype(np.float32)
+        y = x[:, 0, 0] + sgn * \
+            np.random.uniform(1, 2, (2, )).astype(np.float32)
+        self.inputs = {'X': x, 'Y': y}
+
+        self.attrs = {'axis': 0}
+        self.outputs = {
+            'Out':
+            np.maximum(self.inputs['X'], self.inputs['Y'].reshape(2, 1, 1))
+        }
+
+
+class TestElementwiseMaxOp_broadcast_1(TestElementwiseOp):
+    def setUp(self):
+        self.op_type = "elementwise_max"
+        x = np.random.uniform(0.5, 1, (2, 3, 4)).astype(np.float32)
+        sgn = np.random.choice([-1, 1], (3, )).astype(np.float32)
+        y = x[0, :, 0] + sgn * \
+            np.random.uniform(1, 2, (3, )).astype(np.float32)
+        self.inputs = {'X': x, 'Y': y}
+
+        self.attrs = {'axis': 1}
+        self.outputs = {
+            'Out':
+            np.maximum(self.inputs['X'], self.inputs['Y'].reshape(1, 3, 1))
+        }
+
+
+class TestElementwiseMaxOp_broadcast_2(TestElementwiseOp):
+    def setUp(self):
+        self.op_type = "elementwise_max"
+        x = np.random.uniform(0.5, 1, (2, 3, 4)).astype(np.float32)
+        sgn = np.random.choice([-1, 1], (4, )).astype(np.float32)
+        y = x[0, 0, :] + sgn * \
+            np.random.uniform(1, 2, (4, )).astype(np.float32)
+        self.inputs = {'X': x, 'Y': y}
+
+        self.outputs = {
+            'Out':
+            np.maximum(self.inputs['X'], self.inputs['Y'].reshape(1, 1, 4))
+        }
+
+
+class TestElementwiseMaxOp_broadcast_3(TestElementwiseOp):
+    def setUp(self):
+        self.op_type = "elementwise_max"
+        x = np.random.uniform(0.5, 1, (2, 3, 4, 5)).astype(np.float32)
+        sgn = np.random.choice([-1, 1], (3, 4)).astype(np.float32)
+        y = x[0, :, :, 0] + sgn * \
+            np.random.uniform(1, 2, (3, 4)).astype(np.float32)
+        self.inputs = {'X': x, 'Y': y}
+
+        self.attrs = {'axis': 1}
+        self.outputs = {
+            'Out':
+            np.maximum(self.inputs['X'], self.inputs['Y'].reshape(1, 3, 4, 1))
+        }
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/python/paddle/v2/fluid/tests/test_elementwise_min_op.py b/python/paddle/v2/fluid/tests/test_elementwise_min_op.py
new file mode 100644
index 0000000000000000000000000000000000000000..5ff5a4001397302e312a0ad3d326ab82cf45d849
--- /dev/null
+++ b/python/paddle/v2/fluid/tests/test_elementwise_min_op.py
@@ -0,0 +1,107 @@
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestElementwiseOp(OpTest):
+    def setUp(self):
+        self.op_type = "elementwise_min"
+        # If x and y have the same value, the max() is not differentiable.
+        # So we generate test data by the following method
+        # to avoid them being too close to each other.
+        x = np.random.uniform(0.1, 1, [13, 17]).astype("float32")
+        sgn = np.random.choice([-1, 1], [13, 17]).astype("float32")
+        y = x + sgn * np.random.uniform(0.1, 1, [13, 17]).astype("float32")
+        self.inputs = {'X': x, 'Y': y}
+        self.outputs = {'Out': np.minimum(self.inputs['X'], self.inputs['Y'])}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad_normal(self):
+        self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.005)
+
+    def test_check_grad_ingore_x(self):
+        self.check_grad(
+            ['Y'], 'Out', max_relative_error=0.005, no_grad_set=set("X"))
+
+    def test_check_grad_ingore_y(self):
+        self.check_grad(
+            ['X'], 'Out', max_relative_error=0.005, no_grad_set=set('Y'))
+
+
+class TestElementwiseMaxOp_Vector(TestElementwiseOp):
+    def setUp(self):
+        self.op_type = "elementwise_min"
+        x = np.random.random((32, )).astype("float32")
+        sgn = np.random.choice([-1, 1], (32, )).astype("float32")
+        y = x + sgn * np.random.uniform(0.1, 1, (32, )).astype("float32")
+        self.inputs = {'X': x, 'Y': y}
+        self.outputs = {'Out': np.minimum(self.inputs['X'], self.inputs['Y'])}
+
+
+class TestElementwiseMaxOp_broadcast_0(TestElementwiseOp):
+    def setUp(self):
+        self.op_type = "elementwise_min"
+        x = np.random.uniform(0.5, 1, (2, 3, 4)).astype(np.float32)
+        sgn = np.random.choice([-1, 1], (2, )).astype(np.float32)
+        y = x[:, 0, 0] + sgn * \
+            np.random.uniform(1, 2, (2, )).astype(np.float32)
+        self.inputs = {'X': x, 'Y': y}
+
+        self.attrs = {'axis': 0}
+        self.outputs = {
+            'Out':
+            np.minimum(self.inputs['X'], self.inputs['Y'].reshape(2, 1, 1))
+        }
+
+
+class TestElementwiseMaxOp_broadcast_1(TestElementwiseOp):
+    def setUp(self):
+        self.op_type = "elementwise_min"
+        x = np.random.uniform(0.5, 1, (2, 3, 4)).astype(np.float32)
+        sgn = np.random.choice([-1, 1], (3, )).astype(np.float32)
+        y = x[0, :, 0] + sgn * \
+            np.random.uniform(1, 2, (3, )).astype(np.float32)
+        self.inputs = {'X': x, 'Y': y}
+
+        self.attrs = {'axis': 1}
+        self.outputs = {
+            'Out':
+            np.minimum(self.inputs['X'], self.inputs['Y'].reshape(1, 3, 1))
+        }
+
+
+class TestElementwiseMaxOp_broadcast_2(TestElementwiseOp):
+    def setUp(self):
+        self.op_type = "elementwise_min"
+        x = np.random.uniform(0.5, 1, (2, 3, 4)).astype(np.float32)
+        sgn = np.random.choice([-1, 1], (4, )).astype(np.float32)
+        y = x[0, 0, :] + sgn * \
+            np.random.uniform(1, 2, (4, )).astype(np.float32)
+        self.inputs = {'X': x, 'Y': y}
+
+        self.outputs = {
+            'Out':
+            np.minimum(self.inputs['X'], self.inputs['Y'].reshape(1, 1, 4))
+        }
+
+
+class TestElementwiseMaxOp_broadcast_3(TestElementwiseOp):
+    def setUp(self):
+        self.op_type = "elementwise_min"
+        x = np.random.uniform(0.5, 1, (2, 3, 4, 5)).astype(np.float32)
+        sgn = np.random.choice([-1, 1], (3, 4)).astype(np.float32)
+        y = x[0, :, :, 0] + sgn * \
+            np.random.uniform(1, 2, (3, 4)).astype(np.float32)
+        self.inputs = {'X': x, 'Y': y}
+
+        self.attrs = {'axis': 1}
+        self.outputs = {
+            'Out':
+            np.minimum(self.inputs['X'], self.inputs['Y'].reshape(1, 3, 4, 1))
+        }
+
+
+if __name__ == '__main__':
+    unittest.main()