tfa | Tired of having to pull out your phone all the time to grab

I am a bit new at Deep learning and image classification. I want to extract features from an image using VGG16 and give them as input to my vit-keras model. Following is my code:

Building a seq2seq based on tfa.seq2seq, basically works like in https://www.tensorflow.org/addons/tutorials/networks_seq2seq_nmt#train_the_model. I am looking at the nature of the outputs when calling a BasicDecoder. I create an instance of decoder

I was trying to implement Resnet56 in Tensorflow to classify the CIFAR10 images, but somehow I got a lower accuracy than the original creators.
I did everything exactly as described in the paper: same architecture, same data augmentation, same learning rate scheduling, same batch size...
But somehow my implementation produced an accuracy of only 91.84%, while in the original paper they reached 93.03% for the 56 layer Resnet.
Here is the link to the Resnet paper: https://arxiv.org/pdf/1512.03385.pdf
I found what my problem was (see answers if interested) and here you can find my (now correct) implementation, that can now reach the exact same accuracy:

I cannot find a way to pip install the following Python modules without compatibility issues (from a requirements.txt file, on Red Hat Enterprise Linux release 8.2):

I have to port a website which has a TFA authentication via sms/email to asp.net 6. The client doesn’t want to use an authenticator app. The application has some specialities such as the language of the user in the URL path as the first component.

In this post I searched a way to introduce the language part and was, although in a very ugly manner, successful.

However I'm also quite unhappy to use the default UI in general for this application, since I have to manually deactivate all the things that are not required but are included in the library. It feels extremely untidy to have such a huge framework of pages active for so little of required functionality. The only things I need are

• Login with a username and password
• Optionally checking a tfa email/sms code if the user has configured its account for this.

The configuration of the user's profile is done in another application and should not be included in this application (hence all the default identity ui stuff has to be deactivated).

I tried to create the authentication logic manually via controller actions and not installing the default ui at all. For the login with username and password, this was fairly easy.

However, setting up the TFA part seems quite tedious and dangerous (from a security perspective). I have not found any documentation what resources have to be registered and how to setup the authentication system.

Is searching out all the required dependencies and creating the code from the Microsoft source code of default identity UI the only way?
Or is there a template solution to accomplish the desired goal?

I am trying to use the tensorflow maxout implementation (https://www.tensorflow.org/addons/api_docs/python/tfa/layers/Maxout) but struggle with it;

I try to illustrate my problem: If I have the following

I have a generator in producing my TensorFlow data, as triplets (anchor, positive, negative), in batches. Each batch is a list of such triplets, making up the list labels. Using code from Moindrot's blog on triplet loss we get a mask for positives and negatives: With

I have been asked to check our db for the log4j vulnerability. The information on the web is a little confusing but it is my understanding if you have Log4j.jar or log4j.bin you need to remediate these issues and it is not just Apache. So I did a search on my db and found:

My image folder is set up as one main folder with 130 separate folders, each folder with its own images folder_with_130_folders-
folder1_class1-
img_in_class1_folder.jpg
img_in_class1_folder.jpg
...
folder130_class130-
img_in_class130_folder.jpg
img_in_class130_folder.jpg