diff --git a/python/paddle/fluid/tests/unittests/test_imperative_deepcf.py b/python/paddle/fluid/tests/unittests/test_imperative_deepcf.py
index 04a0e5e4cd10f7ece370e879986056d508c894ff..3e222e3c658ecd105811f3694a25d20f1826bcda 100644
--- a/python/paddle/fluid/tests/unittests/test_imperative_deepcf.py
+++ b/python/paddle/fluid/tests/unittests/test_imperative_deepcf.py
@@ -24,6 +24,7 @@ import paddle.fluid.core as core
 from test_imperative_base import new_program_scope
 from paddle.fluid.dygraph.base import to_variable
 from paddle.fluid.dygraph import Linear
+from paddle.fluid.framework import _test_eager_guard
 
 # Can use Amusic dataset as the DeepCF describes.
 DATA_PATH = os.environ.get('DATA_PATH', '')
@@ -294,9 +295,42 @@ class TestDygraphDeepCF(unittest.TestCase):
                     sys.stderr.write('dynamic loss: %s %s\n' %
                                      (slice, dy_loss2))
 
+        with fluid.dygraph.guard():
+            with _test_eager_guard():
+                paddle.seed(seed)
+                paddle.framework.random._manual_program_seed(seed)
+                fluid.default_startup_program().random_seed = seed
+                fluid.default_main_program().random_seed = seed
+
+                deepcf = DeepCF(num_users, num_items, matrix)
+                adam = fluid.optimizer.AdamOptimizer(
+                    0.01, parameter_list=deepcf.parameters())
+
+                for e in range(NUM_EPOCHES):
+                    sys.stderr.write('epoch %d\n' % e)
+                    for slice in range(0, BATCH_SIZE * NUM_BATCHES, BATCH_SIZE):
+                        if slice + BATCH_SIZE >= users_np.shape[0]:
+                            break
+                        prediction = deepcf(
+                            to_variable(users_np[slice:slice + BATCH_SIZE]),
+                            to_variable(items_np[slice:slice + BATCH_SIZE]))
+                        loss = fluid.layers.reduce_sum(
+                            fluid.layers.log_loss(prediction,
+                                                  to_variable(
+                                                      labels_np[slice:slice +
+                                                                BATCH_SIZE])))
+                        loss.backward()
+                        adam.minimize(loss)
+                        deepcf.clear_gradients()
+                        eager_loss = loss.numpy()
+                        sys.stderr.write('eager loss: %s %s\n' %
+                                         (slice, eager_loss))
+
         self.assertEqual(static_loss, dy_loss)
         self.assertEqual(static_loss, dy_loss2)
+        self.assertEqual(static_loss, eager_loss)
 
 
 if __name__ == '__main__':
+    paddle.enable_static()
     unittest.main()
diff --git a/python/paddle/fluid/tests/unittests/test_imperative_recurrent_usage.py b/python/paddle/fluid/tests/unittests/test_imperative_recurrent_usage.py
index d0b3adc490945377a4dd05f9c414cd5c35c7fae5..f12ca0a93ffd9441761c2da866c2c811a30c6e68 100644
--- a/python/paddle/fluid/tests/unittests/test_imperative_recurrent_usage.py
+++ b/python/paddle/fluid/tests/unittests/test_imperative_recurrent_usage.py
@@ -16,9 +16,11 @@ from __future__ import print_function
 
 import unittest
 import paddle.fluid as fluid
+import paddle
 import paddle.fluid.core as core
 from paddle.fluid.dygraph.nn import Embedding
 import paddle.fluid.framework as framework
+from paddle.fluid.framework import _test_eager_guard
 from paddle.fluid.optimizer import SGDOptimizer
 from paddle.fluid.dygraph.base import to_variable
 from test_imperative_base import new_program_scope
@@ -60,6 +62,25 @@ class TestRecurrentFeed(unittest.TestCase):
                 original_in1.stop_gradient = True
                 rt.clear_gradients()
 
+        with fluid.dygraph.guard():
+            with _test_eager_guard():
+                fluid.default_startup_program().random_seed = seed
+                fluid.default_main_program().random_seed = seed
+                original_in1 = to_variable(original_np1)
+                original_in2 = to_variable(original_np2)
+                original_in1.stop_gradient = False
+                original_in2.stop_gradient = False
+                rt = RecurrentTest("RecurrentTest")
+
+                for i in range(3):
+                    sum_out, out = rt(original_in1, original_in2)
+                    original_in1 = out
+                    eager_sum_out_value = sum_out.numpy()
+                    sum_out.backward()
+                    eager_dyout = out.gradient()
+                    original_in1.stop_gradient = True
+                    rt.clear_gradients()
+
         with new_program_scope():
             fluid.default_startup_program().random_seed = seed
             fluid.default_main_program().random_seed = seed
@@ -88,8 +109,11 @@ class TestRecurrentFeed(unittest.TestCase):
                 original_np1 = static_out_value
 
         self.assertTrue(np.array_equal(static_sum_out, sum_out_value))
+        self.assertTrue(np.array_equal(static_sum_out, eager_sum_out_value))
         self.assertTrue(np.array_equal(static_dout, dyout))
+        self.assertTrue(np.array_equal(static_dout, eager_dyout))
 
 
 if __name__ == '__main__':
+    paddle.enable_static()
     unittest.main()