python - Tensorflow GPU crashing after first epoch - Stack Overflow

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I am training a neural network model and when running the code :

history = model.fit(
    X_train, y_train_encoded,
    epochs=20,
    batch_size=32,
    validation_data=(X_val, y_val_encoded),
)

The first epoch finishes successfully (in around 11 minutes), and then I get this error :

InternalError: Failed copying input tensor from /job:localhost/replica:0/task:0/device:CPU:0 to /job:localhost/replica:0/task:0/device:GPU:0 in order to run _EagerConst: Dst tensor is not initialized.

Contrary to the majority of people getting this error before the training starts. For me, The training works for the first epoch, finishes it successfully (it takes around 11 minutes) and then crashes before getting to the next epoch. It seems that tensorflow is not managing the GPU memory well because when inspected I see that 3.1GB/4GB of the GPU are being used (even after the code crashes), and won't start training again when I run it from the jupyter notebook, always gives the same error instantly. I need to restart the GPU to have it back to empty and only then it will run for the first epoch and then get the same error.

Did anyone encounter this problem ? And is it a bug related to some things that tensorflow does between epochs (like clearing memory) ?

I am using tensorflow 2.10.1 on windows with a GTX 1050ti GPU (ps : I can't afford better hardware, running on CPU takes around 5 times more time)

Thank you for any help.

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