[TEST][Keras] use pretrained model to avoid small error caused by random weights (#1701)

nnvm/python/nnvm/frontend/keras.py

@@ -75,6 +75,8 @@ def _convert_activation(insym, keras_layer, _):
 def _convert_advanced_activation(insym, keras_layer, symtab):
     act_type = type(keras_layer).__name__
     if act_type == 'ReLU':
+        if keras_layer.max_value:
+            return _sym.clip(insym, a_min=0, a_max=keras_layer.max_value)
         return _sym.relu(insym)
     elif act_type == 'LeakyReLU':
         return _sym.leaky_relu(insym, alpha=keras_layer.alpha)

nnvm/tests/python/frontend/keras/test_forward.py

@@ -141,25 +141,25 @@ def test_forward_crop():
 
 
 def test_forward_vgg16():
-    keras_model = keras.applications.vgg16.VGG16(include_top=True, weights=None,
+    keras_model = keras.applications.vgg16.VGG16(include_top=True, weights='imagenet',
         input_shape=(224,224,3), classes=1000)
     verify_keras_frontend(keras_model)
 
 
 def test_forward_xception():
-    keras_model = keras.applications.xception.Xception(include_top=True, weights=None,
+    keras_model = keras.applications.xception.Xception(include_top=True, weights='imagenet',
         input_shape=(299,299,3), classes=1000)
     verify_keras_frontend(keras_model)
 
 
 def test_forward_resnet50():
-    keras_model = keras.applications.resnet50.ResNet50(include_top=True, weights=None,
+    keras_model = keras.applications.resnet50.ResNet50(include_top=True, weights='imagenet',
         input_shape=(224,224,3), classes=1000)
     verify_keras_frontend(keras_model)
 
 
 def test_forward_mobilenet():
-    keras_model = keras.applications.mobilenet.MobileNet(include_top=True, weights=None,
+    keras_model = keras.applications.mobilenet.MobileNet(include_top=True, weights='imagenet',
         input_shape=(224,224,3), classes=1000)
     verify_keras_frontend(keras_model)
 
@@ -169,6 +169,7 @@ def test_forward_activations():
     act_funcs = [keras.layers.Activation('softmax'),
                  keras.layers.Activation('softplus'),
                  keras.layers.ReLU(),
+                 keras.layers.ReLU(max_value=6.),
                  keras.layers.LeakyReLU(alpha=0.3),
                  keras.layers.PReLU(weights=weights, alpha_initializer="zero"),
                  keras.layers.ELU(alpha=0.5),