I am trying to run a GradCAM that gives me the below error. I am confused as to why this is as Sequential is clearly being called. I have tried uninstalling tensorflow, restarting the kernel, and trying other environments. Any help is appreciated.

I am a newbie learning keras following this tutorial

I’ve been following along with a video series from 2020 that details how to build a variational autoencoder inside of Python, and I’ve had to reformat the code in some places to get it to work with the modern version of Tensorflow. I’ve run into an issue where I try to use K.function on a dense layer (called self.mu), but I keep getting an error saying that I can’t pass a Keras tensor into a Tensorflow function. For example, here is some of the problematic code:

I notice a huge difference in performance when running model.predict(x) for large datasets (~10mil)

In a machine learning exercice done with
(Tensorflow 2.13.1, Keras 2.13.1) and using binary cross entropy,
I obtain the following loss curve for the training and validation part.

i would like to ask question about one thing : for instance common form for transfer learning is this :

I’m using Keras 3/TensorFlow 2.16.1 and would like to implement this example (https://keras.io/examples/vision/semisupervised_simclr/) for semi-supervised image classification using contrastive pretraining for 3D-data instead of 2D-images.

I have trained InceptionResnetV2 using CBAM. Heres the cbam i am using

I have a keras model defined using Functional API. I also have its weights (.h5 file), and a single example input tensor (stored in a .npy file). I am trying to reimplement this exact model in pytorch. And i am getting a completely different result. I would like to see where did i go wrong by comparing the tensors between each layer. But i cant for the life of me extract those tensors.

I’m using these lines to load data from .npy files: