diff --git a/docs/sample_code/golden/onnx_demo.py b/docs/sample_code/golden/onnx_demo.py
index 959ecb81265f85e72e98ef8c241fd3ab9faf390f..0c2980c9a000c96f0b90653a9d306d2d8c9abba2 100644
--- a/docs/sample_code/golden/onnx_demo.py
+++ b/docs/sample_code/golden/onnx_demo.py
@@ -34,7 +34,8 @@ def generate_random_input_data_and_run_model(args):
 
     if args.inDataFile:  # User specifies input
         input_data_files = args.inDataFile.split(':')
-        assert len(input_data_files) == len(input_tensors), "Shape of input data is not compatible with the model!"
+        if not len(input_data_files) == len(input_tensors):
+            raise ValueError("Shape of input data is not compatible with the model!")
         for i, input_tensor in enumerate(input_tensors):
             tensor_type = input_tensor.type[7:-1]
             if tensor_type == "float":
@@ -48,12 +49,12 @@ def generate_random_input_data_and_run_model(args):
             if args.inputShape:
                 stable_shape = ast.literal_eval(args.inputShape)
                 shape_info = stable_shape[i]
-                assert len(input_data) == np.prod(
-                    shape_info), "Shape of input data is not compatible with the input shape!"
+                if not len(input_data) == np.prod(shape_info):
+                    raise ValueError("Shape of input data is not compatible with the input shape!")
                 input_data = input_data.reshape(shape_info)
             else:
-                assert len(input_data) == np.prod(
-                    input_tensor.shape), "Shape of input data is not compatible with the model!"
+                if not len(input_data) == np.prod(input_tensor.shape):
+                    raise ValueError("Shape of input data is not compatible with the model!")
                 input_data = input_data.reshape(input_tensor.shape)
             input_dict[input_tensor.name] = input_data
     else:  # Generate random input and save