Add both tf.nn.X and tf.compat.v1.nn.X to activation_map (#513)

Author: jiafatom

keras2onnx/ke2onnx/activation.py

@@ -34,8 +34,18 @@
                   tf.nn.relu: apply_relu,
                   tf.nn.relu6: apply_relu_6,
                   tf.nn.elu: apply_elu,
+                  tf.nn.selu: apply_selu,
                   tf.nn.tanh: apply_tanh}
 
+if hasattr(tf.compat, 'v1'):
+    activation_map.update({tf.compat.v1.nn.sigmoid: apply_sigmoid})
+    activation_map.update({tf.compat.v1.nn.softmax: apply_softmax})
+    activation_map.update({tf.compat.v1.nn.relu: apply_relu})
+    activation_map.update({tf.compat.v1.nn.relu6: apply_relu_6})
+    activation_map.update({tf.compat.v1.nn.elu: apply_elu})
+    activation_map.update({tf.compat.v1.nn.selu: apply_selu})
+    activation_map.update({tf.compat.v1.nn.tanh: apply_tanh})
+
 
 def convert_keras_activation(scope, operator, container):
     input_name = operator.input_full_names[0]

tests/test_layers.py

@@ -1374,7 +1374,7 @@ def test_Softmax(advanced_activation_runner):
 
 
 def test_tf_nn_activation(runner):
-    for activation in ['relu', tf.nn.relu, tf.nn.relu6]:
+    for activation in [tf.nn.relu, 'relu', tf.nn.relu6, tf.nn.softmax]:
         model = keras.Sequential([
             Dense(64, activation=activation, input_shape=[10]),
             Dense(64, activation=activation),