[Keras] fix dropout bug (#399)

nnvm/python/nnvm/frontend/keras.py

@@ -355,7 +355,7 @@ def _convert_reshape(insym, keras_layer, _):
 
 def _default_skip(insym, keras_layer, _): # pylint: disable=unused-argument
     """Layers that can be skipped because they are train time only."""
-    return
+    return insym
 
 
 _convert_map = {

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

@@ -40,7 +40,6 @@ def verify_keras_frontend(keras_model):
 
     
 def test_forward_elemwise_add():
-    print("test_forward_elemwise_add")
     r = []
     data = keras.layers.Input(shape=(32,32,3))
     x = keras.layers.Conv2D(8, (3, 3), padding="same")(data)
@@ -48,28 +47,18 @@ def test_forward_elemwise_add():
     x = keras.layers.Conv2D(8, (3, 3), padding="same")(x)
     r.append(x)
     x = keras.layers.Conv2D(8, (3, 3), padding="same")(x)
-
+    # add two symbols
     y = keras.layers.add([keras.layers.add([x, r[0]]), r[1]])
     y = keras.layers.GlobalAveragePooling2D()(y)
     keras_model = keras.models.Model(data, y)
     verify_keras_frontend(keras_model)
-
+    # add three symbols
     y = keras.layers.add([x, r[0], r[1]])
     y = keras.layers.GlobalAveragePooling2D()(y)
     keras_model = keras.models.Model(data, y)
     verify_keras_frontend(keras_model)
 
 
-def test_forward_elementwise_add2():
-    data = keras.layers.Input(shape=(32,32,3))
-    r = keras.layers.Conv2D(10, (3, 3), padding="same")(data)
-    x = keras.layers.Conv2D(10, (3, 3), strides=(2, 2), padding="same")(data)
-    x = keras.layers.UpSampling2D()(x)
-    x = keras.layers.add([x, r])
-    x = keras.layers.GlobalAveragePooling2D()(x)
-    keras_model = keras.models.Model(data, x)
-
-
 def test_forward_softrelu():
     data = keras.layers.Input(shape=(32,32,3))
     x = keras.layers.Activation('softplus')(data)
@@ -92,6 +81,7 @@ def test_forward_dense():
     data = keras.layers.Input(shape=(32,32,3))
     x = keras.layers.MaxPooling2D(pool_size=(2,2))(data)
     x = keras.layers.Flatten()(x)
+    x = keras.layers.Dropout(0.5)(x)
     x = keras.layers.Dense(10, activation='relu', kernel_initializer='uniform')(x)
     keras_model = keras.models.Model(data, x)
     verify_keras_frontend(keras_model)
@@ -146,7 +136,6 @@ def test_forward_resnet50():
 
 if __name__ == '__main__':
     test_forward_elemwise_add()
-    test_forward_elementwise_add2()
     test_forward_softrelu()
     test_forward_leaky_relu()
     test_forward_dense()