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add tf.compat.v1 for tf2 support #56

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add tf.compat.v1 for tf2 support #56

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12 changes: 4 additions & 8 deletions deepexplain/tensorflow/methods.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down Expand Up @@ -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:
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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:
Expand All @@ -644,7 +644,3 @@ def _check_ops(self):
elif 'keras_learning_phase' in op.name:
self.keras_phase_placeholder = op.outputs[0]