Ramesh Sampath

@nicolasnn - Thanks for reporting this issue with detailed examples. I am working on an update to `BackupAndRestore` callback to address this specific scenario. I will submit a PR in...

@nicolaspi - Keras recently introduced `get_metrics_result` API for `Model` that addresses part of this issue / PR. Can you please rebase and only address the `training` parameter added to `compute_metrics`

@bvbellomo - Yes, batch norm should work in the inputs as well. But there's a typo in your network definition, you are overriding `inputs`, but its required later for defining...

Thanks for reporting the issue. `keras-cv` was updated to support `keras-core` along with the existing `tf.keras` backend and might have had this regression. I will take a look and get...

On the above error, you could wrap it in `tf.data.DataSet.from_generator` for your generator function and provide it the proper shape of the image - ``` from functools import partial def...

@chenmoneygithub - Thanks for raising this issue. 1. Starting in TF 2.16, Keras 3 will be installed with TensorFlow. But TF will also remain compatible with `tf-keras` and users can...

@JuanVargas - Can you try in a new virtual env - ``` pip install torch # also install Cuda 12 pip install tensorflow # will download TF 2.15 that should...

@james77777778 - Just wanted to confirm I captured the issue correctly. ``` import keras from keras import layers inputs = layers.Input(shape=(2,)) out1 = layers.Dense(1, activation=None, name="out1")(inputs) out2 = layers.Dense(1, activation='sigmoid',...

This is documented as one of the differences in Keras 2 vs Keras 3 compatibility issues doc #18467 - * When having multiple named outputs (for example named output_a and...

So, the way forward is the explicitly add them to metrics unless @fchollet suggests otherwise - ``` model = keras.Model(inputs=inputs, outputs=[out1, out2]) model.compile("sgd", ["mse", "binary_crossentropy"], metrics=["mse", "binary_crossentropy"]) Epoch 1/2 1/1...