diff --git a/caffe2/python/hypothesis_test.py b/caffe2/python/hypothesis_test.py
index cac38bf80be..497e269a627 100644
--- a/caffe2/python/hypothesis_test.py
+++ b/caffe2/python/hypothesis_test.py
@@ -1422,8 +1422,9 @@ class TestOperators(hu.HypothesisTestCase):
                (["async_dag"] if workspace.has_gpu_support else [])),
            do=st.sampled_from(hu.device_options))
     def test_dag_net_forking(self, net_type, num_workers, do):
-        from caffe2.python.cnn import CNNModelHelper
-        m = CNNModelHelper()
+        from caffe2.python.model_helper import ModelHelper
+        from caffe2.python import brew
+        m = ModelHelper(name="test_model")
         n = 10
         d = 2
         depth = 2
@@ -1437,16 +1438,18 @@ class TestOperators(hu.HypothesisTestCase):
                 mid_1 = "{}_{}_m".format(i + 1, 2 * j)
                 mid_2 = "{}_{}_m".format(i + 1, 2 * j + 1)
                 top = "{}_{}".format(i, j)
-                m.FC(
+                brew.fc(
+                    m,
                     bottom_1, mid_1,
                     dim_in=d, dim_out=d,
-                    weight_init=m.ConstantInit(np.random.randn()),
-                    bias_init=m.ConstantInit(np.random.randn()))
-                m.FC(
+                    weight_init=('ConstantFill', dict(value=np.random.randn())),
+                    bias_init=('ConstantFill', dict(value=np.random.randn())))
+                brew.fc(
+                    m,
                     bottom_2, mid_2,
                     dim_in=d, dim_out=d,
-                    weight_init=m.ConstantInit(np.random.randn()),
-                    bias_init=m.ConstantInit(np.random.randn()))
+                    weight_init=('ConstantFill', dict(value=np.random.randn())),
+                    bias_init=('ConstantFill', dict(value=np.random.randn())))
                 m.net.Sum([mid_1, mid_2], top)
         m.net.SquaredL2Distance(["0_0", "label"], "xent")
         m.net.AveragedLoss("xent", "loss")
@@ -1769,16 +1772,17 @@ class TestOperators(hu.HypothesisTestCase):
            n=st.integers(1, 5),
            d=st.integers(1, 5))
     def test_elman_recurrent_network(self, t, n, d):
-        from caffe2.python import cnn
+        from caffe2.python import model_helper, brew
         np.random.seed(1701)
-        step_net = cnn.CNNModelHelper(name="Elman")
+        step_net = model_helper.ModelHelper(name="Elman")
         # TODO: name scope external inputs and outputs
         step_net.Proto().external_input.extend(
             ["input_t", "seq_lengths", "timestep",
              "hidden_t_prev", "gates_t_w", "gates_t_b"])
         step_net.Proto().type = "simple"
         step_net.Proto().external_output.extend(["hidden_t", "gates_t"])
-        step_net.FC("hidden_t_prev", "gates_t", dim_in=d, dim_out=d, axis=2)
+        brew.fc(step_net,
+                "hidden_t_prev", "gates_t", dim_in=d, dim_out=d, axis=2)
         step_net.net.Sum(["gates_t", "input_t"], ["gates_t"])
         step_net.net.Sigmoid(["gates_t"], ["hidden_t"])