bhack

Can you check also this case: https://discuss.tensorflow.org/t/text-based-tensorflow-unexpected-result-of-train-function-empty-logs/11075

What was the logic to expose this in model compile instead?

I suppose that if we already let the user to `jit_compile` or not in the model compile API we don't want to automatically compile layers without any user control. https://github.com/keras-team/keras/blob/39ad2c1cb22b231baf05a0218322328c13654bda/keras/engine/training.py#L532

/cc @qlzh727 @LukeWood I suppose that we will have a small "explosion" of XLA `jit_compile` failures when we will enable the XLA compilation. And they will be more fatal then...

@fchollet @qlzh727 Can you migrate this to keras-cv now? It seems @LukeWood has not enough rights in this repo for the migration.

So do you want something like this? https://www.tensorflow.org/datasets/splits

> No, that's not what we're looking for. Thanks for the pointer, though. Yes I've used like.. as that one is working with TFDS and not with tf.data as you...

Have you tried with: ```python import tensorflow as tf class MyModel(tf.keras.Model): def __init__(self, initializer): self.initializer = initializer super().__init__(inputs=[], outputs=[]) def get_config(self): return {"initializer": self.initializer} mymodel = MyModel(initializer=tf.keras.initializers.TruncatedNormal()) tf.keras.models.save_model(mymodel, "mymodel.sm", overwrite=True)...

Do you want to do something like: ```python import tensorflow as tf from tensorflow import keras # Define a subclassed model with the same architecture class MyModel(keras.Model): def __init__(self, output_dim,name=None,...

I don't see a subsclassed model there and the initializer is used in a custom layer there. So how do you have derived your "minimal" subclassed model gist? Also mine...