I have the following training setup from tensorflow v1’s estimators:

I’m trying to load a previously trained model but it gives me this error. Is there any way I can convert this file format to the new format or how do I fix this? I’d rather not have to train a new model if possible.

I am new to ML and tensorflow

I am training several Keras models inside a for loop. When the train starts, each epoch takes around 280ms. But as trains go on, each epoch lasts around 3s. I have tried to solve this problem using clear_session(), but nothing changed. I also have tried to delete the model when it finishes the .fit and also use gc.collect(), but none worked.

I’ve been trying to get a models performance to be able to optimise the inference and training time.

I’m trying to get tensorflow.keras.layers to work on VSCode but everything I try does not seem to work and I keep getting a warning saying that Import “tensorflow.keras.layers” could not be resolved Pylance. I’ve made a virtual environment and pip installed both tensorflow and keras and it tells me requirement already satisfied. I’m not really sure what to do. I have tensorflow version 2.17.0 and keras 3.4.1. I’ve seen questions similar to my problem on here but they haven’t really helped me. I’m also on a Mac though I’m not sure if that changes anything.

tensorflow 2.15 backend

I have ~100 tensorflow models to train, and on each training I run keras-tuner to find the best hyperparameters for each model. To save time, I would like to train one of these models per CPU core.

I have ~100 tensorflow models to train, and on each training I run keras-tuner to find the best hyperparameters for each model. To save time, I would like to train one of these models per CPU core.

In keras documentation: