[Python] Changing MobileNet Dropout After Loading

I am working a transfer learning problem. When I create a new model from just the Mobilenet, I set a dropout.

base_model = MobileNet(weights='imagenet', include_top=False, input_shape=(200,200,3), dropout=.15)
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(10, activation='softmax')(x)


I save models as I train using model_checkpoint_callback. As I train I find where overfitting is happening and adjust the amount of frozen layers and the learning rate. Can I also adjust dropout when I save a loaded model again?

I saw this answer but there are no actual dropout layers in Mobilenet, so this

for layer in model.layers:
if hasattr(layer, 'rate'):
print(layer.name)
layer.rate = 0.5


doesn't do anything.

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