Add the missing keras activation parameter in the conv layers. (#217)

* Add the missing keras activation parameter in the conv layers.

* remove exp activation

* The sudden release of onnx 1.4 looks like a disaster.

Author: Wenbing Li

.azure-pipelines/linux-conda-CI.yml

@@ -15,10 +15,12 @@ jobs:
     matrix:
       Python27:
         python.version: '2.7'
+        ONNX_PATH: onnx==1.2.3
       # Python35:
       #   python.version: '3.5'
       Python36:
         python.version: '3.6'
+        ONNX_PATH: onnx==1.3.0
     maxParallel: 3
 
   steps:
@@ -36,6 +38,7 @@ jobs:
       conda install -c conda-forge cmake
       conda install -c conda-forge openmpi
       conda install -c conda-forge tensorflow
+      pip install $(ONNX_PATH)
       pip install -r requirements.txt
       pip install -r requirements-dev.txt
       test '$(python.version)' != '2.7' && pip install onnxruntime

.azure-pipelines/win32-conda-CI.yml

@@ -24,7 +24,7 @@ jobs:
 
       Python36:
         python.version: '3.6'
-        ONNX_PATH: onnx
+        ONNX_PATH: onnx==1.3.0
         KERAS: keras
         COREML_PATH: git+https://github.com/apple/coremltools
 

onnxmltools/convert/common/_apply_operation.py

@@ -378,7 +378,7 @@ def apply_prelu(scope, input_name, output_name, container, operator_name=None, s
         container.add_node('PRelu', [input_name, slope_tensor_name], output_name, op_version=7, name=name)
 
 
-def apply_elu(scope, input_name, output_name, container, operator_name=None, alpha=None):
+def apply_elu(scope, input_name, output_name, container, operator_name=None, alpha=1.0):
     _apply_unary_operation(scope, 'Elu', input_name, output_name, container, operator_name, alpha=alpha)
 
 

onnxmltools/convert/keras/operator_converters/Dense.py

@@ -7,13 +7,18 @@
 from keras.activations import get as _get_activation
 from keras.layers import Dense
 from ....proto import onnx_proto
-from ...common._apply_operation import apply_sigmoid, apply_softmax, apply_identity, apply_relu, apply_add
+from ...common._apply_operation import apply_sigmoid, apply_softmax, apply_identity, apply_add
+from ...common._apply_operation import apply_relu, apply_elu, apply_selu, apply_tanh, apply_hard_sigmoid
 from ...common._registration import register_converter
 
 _activation_map = {_get_activation('sigmoid'): apply_sigmoid,
                    _get_activation('softmax'): apply_softmax,
                    _get_activation('linear'): apply_identity,
-                   _get_activation('relu'): apply_relu}
+                   _get_activation('relu'): apply_relu,
+                   _get_activation('elu'): apply_elu,
+                   _get_activation('selu'): apply_selu,
+                   _get_activation('tanh'): apply_tanh,
+                   _get_activation('hard_sigmoid'): apply_hard_sigmoid}
 
 
 def convert_keras_dense(scope, operator, container):