Keras3 explicitly tell a predictor model to use CPU while use GPU for trainer model
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
ValueError: `inputs` argument cannot be empty. Received: inputs=[] [closed]
Closed 45 secs ago.
AttributeError: module ‘keras.src.activations’ has no attribute ‘get’
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’.
Issue with training Keras model using ModelCheckpoint in Kaggle notebook (Unexpected result of `train_function` (Empty logs))
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:
How to pass feature value to custom loss in Keras
I am using a custom loss to train my model and want to use feature value to have a differential loss, something like
Tensorflow training loop keeps appending to system memory over time
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.
PointNet DL model improvement, window segmentation
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:
Problems with Keras multi-input model using inputs with different shapes
I need help with the following scenario. Imagine that my original data have this format: