I was trying to convert an example that I have from tf.keras to Keras_core. It's a CV example where I use VGG19 as a base model and then some Dense layers.
I am using Keras CV RandAugment Layer along with other Keras layers for Resizing and Rescaling.
In Keras Core (TF backend), for RandAugment, I am getting the following error:
A KerasTensor cannot be used as input to a TensorFlow function. A KerasTensor is a symbolic placeholder for a shape and dtype, used when constructing Keras Functional models or Keras Functions. You can only use it as input to a Keras layer or a Keras operation (from the namespaces `keras_core.layers` and `keras_core.operations`). You are likely doing something like:
x = Input(...)
tf_fn(x) # Invalid.
What you should do instead is wrap `tf_fn` in a layer:
def call(self, x):
x = MyLayer()(x)
Call arguments received by layer 'rand_augment_4' (type RandAugment):
• inputs=<KerasTensor shape=(None, None, None, 3), dtype=float32, name=keras_tensor_196>
Do we always have to wrap Keras CV layers with a Keras layer?