NEGU93
Model subclassing compatibility
I've been trying to get this to work with the model subclassing API, but for some reason, the first layer of the model always expects the data to be in float32. Any idea how to get this to work?
Did you add the ComplexInput layer at the start? If not TF will automatically use the tensorflow Input and cast it to float.
How would you do that in a subclassed model? I have a custom train and test step so I can't just do:
inputs = ComplexInput((1,2,3))
outputs = SubClassedModel(inputs)
model = tf.keras.Model(inputs, outputs)
It is possible... did it work? I have never worked with SubCalssedModels, I don't know how they worked. What is the difference from a normal model?
I couldn't get it to work. In order to use the approach above, I would have needed too big a refactor. Subclassed models are just a pytorch like interface. You just inherit from the normal model and then build the layers in the constructor, and the implementation in call. It's the same as a custom layer, but with a model instead.