def MyXception(imageShape, nClasses, trainable="onlyTop", dropout=None):
# Load pretrained model
modelBase = xception.Xception(include_top = False, input_shape = imageShape, \
weights = "imagenet")
# Add top
x = modelBase.outputs[0]
x = GlobalAveragePooling2D(name="avg_pool")(x)
if dropout is not None:
print("Using dropout", dropout)
x = Dropout(dropout, name="dropout")(x)
x = Dense(nClasses, activation="softmax", name='predictions')(x)
model = Model(modelBase.inputs, x, name="xception")
# Add method to instance (not very good idea)
model.SetTrainable = SetTrainableXception.__get__(model, type(model))
# Set trainable mode
model.SetTrainable(trainable)
return model
