diff --git a/mace/python/tools/tf_converter_lib.py b/mace/python/tools/tf_converter_lib.py
index 5e9acbfd639384bf55e473c2f7c8f7099fbe2916..671230e51dfb01fa11d31e96e677817e2ac0ed88 100644
--- a/mace/python/tools/tf_converter_lib.py
+++ b/mace/python/tools/tf_converter_lib.py
@@ -12,6 +12,11 @@ pooling_type_mode = {
   'MaxPool': 2
 }
 
+def get_input_tensor(op, index):
+  input_tensor = op.inputs[index]
+  if input_tensor.op.type == 'Reshape':
+    input_tensor = get_input_tensor(input_tensor.op, 0)
+  return input_tensor
 
 def convert_ops(unresolved_ops, net_def):
   ops_count = len(unresolved_ops)
@@ -19,7 +24,7 @@ def convert_ops(unresolved_ops, net_def):
 
   first_op = unresolved_ops[0]
 
-  if first_op.type == 'Placeholder':
+  if first_op.type == 'Placeholder' or first_op.type == 'Reshape':
     pass
   elif first_op.type == 'Const':
     tf_tensor = first_op.outputs[0].eval()
@@ -76,12 +81,12 @@ def convert_ops(unresolved_ops, net_def):
     if mul_op.type != 'Mul' or mul_2_op.type != 'Mul' or mul_1_op.type != 'Mul' or sub_op.type != 'Sub' or add_1_op.type != 'Add':
       raise Exception('Invalid BatchNorm Op')
 
-    input_name = mul_1_op.inputs[0].name
-    gamma = mul_op.inputs[1].name
-    beta = sub_op.inputs[0].name
-    mean = mul_2_op.inputs[0].name
-    variance = add_op.inputs[0].name
-    epsilon = add_op.inputs[1].name
+    input_name = get_input_tensor(mul_1_op, 0).name
+    gamma = get_input_tensor(mul_op, 1).name
+    beta = get_input_tensor(sub_op, 0).name
+    mean = get_input_tensor(mul_2_op, 0).name
+    variance = get_input_tensor(add_op, 0).name
+    epsilon = get_input_tensor(add_op, 1).name
 
     op_def = net_def.op.add()
     op_def.name = first_op.name[:-4]  # remove /add