diff --git a/deepexplain/tensorflow/methods.py b/deepexplain/tensorflow/methods.py
index 8301b25..a2996e7 100644
--- a/deepexplain/tensorflow/methods.py
+++ b/deepexplain/tensorflow/methods.py
@@ -73,7 +73,7 @@ def __init__(self, T, X, session, keras_learning_phase=None):
         self.Y_shape = [None,] + T.get_shape().as_list()[1:]
         # Most often T contains multiple output units. In this case, it is often necessary to select
         # a single unit to compute contributions for. This can be achieved passing 'ys' as weight for the output Tensor.
-        self.Y = tf.placeholder(tf.float32, self.Y_shape)
+        self.Y = tf.compat.v1.placeholder(tf.float32, self.Y_shape)
         # placeholder_from_data(ys) if ys is not None else 1.0  # Tensor that represents weights for T
         self.T = self.T * self.Y
         self.symbolic_attribution = None
@@ -373,7 +373,7 @@ def _init_references(self):
         sys.stdout.flush()
         self._deeplift_ref.clear()
         ops = []
-        g = tf.get_default_graph()
+        g = tf.compat.v1.get_default_graph()
         for op in g.get_operations():
             if len(op.inputs) > 0 and not op.name.startswith('gradients'):
                 if op.type in SUPPORTED_ACTIVATIONS:
@@ -556,7 +556,7 @@ def deepexplain_grad(op, grad):
 
 class DeepExplain(object):
 
-    def __init__(self, graph=None, session=tf.get_default_session()):
+    def __init__(self, graph=None, session=tf.compat.v1.get_default_session()):
         self.method = None
         self.batch_size = None
         self.session = session
@@ -635,7 +635,7 @@ def _check_ops(self):
          and needs to be passed in feed_dict.
         :return:
         """
-        g = tf.get_default_graph()
+        g = tf.compat.v1.get_default_graph()
         for op in g.get_operations():
             if len(op.inputs) > 0 and not op.name.startswith('gradients'):
                 if op.type in UNSUPPORTED_ACTIVATIONS:
@@ -644,7 +644,3 @@ def _check_ops(self):
             elif 'keras_learning_phase' in op.name:
                 self.keras_phase_placeholder = op.outputs[0]
 
-
-
-
-