diff --git a/example/mnist_demo/lenet5_mnist_fuzzing.py b/example/mnist_demo/lenet5_mnist_fuzzing.py
index 4adcbbd64f627b6667d7fddf17e8f771d0418498..77f7da51efb47fc871323062be3f5297ebfadad3 100644
--- a/example/mnist_demo/lenet5_mnist_fuzzing.py
+++ b/example/mnist_demo/lenet5_mnist_fuzzing.py
@@ -41,7 +41,7 @@ def test_lenet_mnist_fuzzing():
     model = Model(net)
 
     # get training data
-    data_list = "./MNIST_datasets/train"
+    data_list = "./MNIST_unzip/train"
     batch_size = 32
     ds = generate_mnist_dataset(data_list, batch_size, sparse=True)
     train_images = []
@@ -55,7 +55,7 @@ def test_lenet_mnist_fuzzing():
 
     # fuzz test with original test data
     # get test data
-    data_list = "./MNIST_datasets/test"
+    data_list = "./MNIST_unzip/test"
     batch_size = 32
     ds = generate_mnist_dataset(data_list, batch_size, sparse=True)
     test_images = []
diff --git a/tests/ut/python/defenses/test_ad.py b/tests/ut/python/defenses/test_ad.py
index 78b581ad4c0f9722c5ca2fbf1a6087952df5d78c..0a3015f6419334232fd6852e2b64e349a40475ef 100644
--- a/tests/ut/python/defenses/test_ad.py
+++ b/tests/ut/python/defenses/test_ad.py
@@ -39,7 +39,7 @@ TAG = 'Ad_Test'
 def test_ad():
     """UT for adversarial defense."""
     num_classes = 10
-    batch_size = 16
+    batch_size = 32
 
     sparse = False
     context.set_context(mode=context.GRAPH_MODE)
diff --git a/tests/ut/python/defenses/test_ead.py b/tests/ut/python/defenses/test_ead.py
index 9eeac213f1a9ff3c151486b120739a3aa4c371a2..f44cc412bd35bff87f511b7d95d2f99c79fd5c5d 100644
--- a/tests/ut/python/defenses/test_ead.py
+++ b/tests/ut/python/defenses/test_ead.py
@@ -41,7 +41,7 @@ TAG = 'Ead_Test'
 def test_ead():
     """UT for ensemble adversarial defense."""
     num_classes = 10
-    batch_size = 16
+    batch_size = 64
 
     sparse = False
     context.set_context(mode=context.GRAPH_MODE)
@@ -53,7 +53,7 @@ def test_ead():
     if not sparse:
         labels = np.eye(num_classes)[labels].astype(np.float32)
 
-    net = SimpleNet()
+    net = Net()
     loss_fn = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=sparse)
     optimizer = Momentum(net.trainable_params(), 0.001, 0.9)
 
diff --git a/tests/ut/python/defenses/test_nad.py b/tests/ut/python/defenses/test_nad.py
index 938a4e53ef76460da57ed18357ab0f296ca5465d..f0d89812c8b03cf54e61a772c0c121fb2ca567e8 100644
--- a/tests/ut/python/defenses/test_nad.py
+++ b/tests/ut/python/defenses/test_nad.py
@@ -39,7 +39,7 @@ TAG = 'Nad_Test'
 def test_nad():
     """UT for natural adversarial defense."""
     num_classes = 10
-    batch_size = 16
+    batch_size = 32
 
     sparse = False
     context.set_context(mode=context.GRAPH_MODE)
diff --git a/tests/ut/python/defenses/test_pad.py b/tests/ut/python/defenses/test_pad.py
index 79080954e2004582d3abbc4de05a414bd6d6ab32..e92d7cef6a03edb828ba77fb2e770207961a7bea 100644
--- a/tests/ut/python/defenses/test_pad.py
+++ b/tests/ut/python/defenses/test_pad.py
@@ -39,7 +39,7 @@ TAG = 'Pad_Test'
 def test_pad():
     """UT for projected adversarial defense."""
     num_classes = 10
-    batch_size = 16
+    batch_size = 32
 
     sparse = False
     context.set_context(mode=context.GRAPH_MODE)