I have two models, first one for trainer that should be use GPU, and the second one for the predictor for the evaluation that should be use CPU

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I am running the following error when I try to optimize a LSTM using keras tuner in Python: AttributeError: module ‘keras.src.activations’ has no attribute ‘get’.

I’m encountering an issue while trying to train a Keras model in a Kaggle notebook using TensorFlow’s ModelCheckpoint callback. Here’s my setup and the error I’m facing:

I am using a custom loss to train my model and want to use feature value to have a differential loss, something like

I am trying to use the encoder of a pre-trained UNet model to output features which are then used to train a densely connected MLP for a regression task. However, in the code shown below, there is some part that keeps appending to the system RAM over the course of 5-6 epochs, filling up all of the 54Gb of RAM of the machine.

I’m making a project where I give a pointnet architekture a bunch of pointclouds from rooms and the points have indexes/labels. They re simple .xyz files but I add a forth row 1 or 0. 1 means it part of a window, 0 means its not.
So, the model making is running well with low loss and high accuracy. The accuracy increasing as it should.
But when I want to use it for segmentating it thinks ever point is part of a window on that pointcloud, its sure in it not even 0.99 threshold helps, it still exports the whole cloud. And I just can’t figure it out why is not working.
Here is the code witch makes the model:

I need help with the following scenario. Imagine that my original data have this format: