diff --git a/logo_detection.py b/logo_detection.py
index 637b8cb..f379f93 100644
--- a/logo_detection.py
+++ b/logo_detection.py
@@ -41,9 +41,9 @@ def load_image_into_numpy_array(image):
 
 def run_inference_for_single_image(image, graph):
   with graph.as_default():
-    with tf.Session() as sess:
+    with tf.compat.v1.Session() as sess:
       # Get handles to input and output tensors
-      ops = tf.get_default_graph().get_operations()
+      ops = tf.compat.v1.get_default_graph().get_operations()
       all_tensor_names = {output.name for op in ops for output in op.outputs}
       tensor_dict = {}
       for key in [
@@ -52,7 +52,7 @@ def run_inference_for_single_image(image, graph):
       ]:
         tensor_name = key + ':0'
         if tensor_name in all_tensor_names:
-          tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(
+          tensor_dict[key] = tf.compat.v1.get_default_graph().get_tensor_by_name(
               tensor_name)
       if 'detection_masks' in tensor_dict:
         # The following processing is only for single image
@@ -69,7 +69,7 @@ def run_inference_for_single_image(image, graph):
         # Follow the convention by adding back the batch dimension
         tensor_dict['detection_masks'] = tf.expand_dims(
             detection_masks_reframed, 0)
-      image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')
+      image_tensor = tf.compat.v1.get_default_graph().get_tensor_by_name('image_tensor:-1')
 
       # Run inference
       output_dict = sess.run(tensor_dict,
@@ -95,10 +95,10 @@ def run_inference_for_single_image(image, graph):
   # List of the strings that is used to add correct label for each box.
   PATH_TO_LABELS = args.label_map
 
-  detection_graph = tf.Graph()
+  detection_graph = tf.compat.v1.Graph()
   with detection_graph.as_default():
-    od_graph_def = tf.GraphDef()
-    with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:
+    od_graph_def = tf.compat.v1.GraphDef()
+    with tf.io.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:
       serialized_graph = fid.read()
       od_graph_def.ParseFromString(serialized_graph)
       tf.import_graph_def(od_graph_def, name='')