Keras VS tensorflow

The report output for scalene does look much nicer, but the slowness for me dropped me from continuing to use it. Maybe there's some bad interaction with tensorflow/pytest. I can try to make an example, but I'd guess if you try running it on tensorflows actual unit tests (something like this) you'd get similar behavior.

One way to improve your projects and coding skills is to try new models and libraries. For example, if you did classification with logistic regression, try also with random forest; if you used Tensorflow, now try Keras; if you scraped a website with BeautifulSoup, now do it with Scrapy. You get the point.

The source for the keras.Model class has grown to be several thousand lines of code. This makes it incredibly challenging to sift through, especially for beginners.

Keras - Keras is an open-source software library that provides a Python interface for artificial neural networks. Keras acts as an interface for the TensorFlow library. Link - https://keras.io/

I think I found my answer here Thank you for your help

Website: https://keras.io/ Github Repository: https://github.com/keras-team/keras Developed By: various Developers, initially by Francois Chollet Primary purpose: Focused on Neural Networks

I also recommend trying to understand the software that's being built by the machine learning class. If you want to build your own machine learning software, check out Keras (http://keras.io/) and the machine learning API's that Keras provides.

It appears this doesn't exist out of the box in Keras. Here's a note about it: https://github.com/tensorflow/tensorflow/issues/42296

No surprises here: deep learning is the most popular subcategory, with hugging face transformers repo, YOLOv5, Tensorflow and Deepmind’s Alphafold all in the mix. Surprisingly, the only proper infrastructure-ey repos on the list are Meilisearch and Clickhouse, a tad bit surprising given all the hype data infrastructure receives in VC-world, but again, probably just a question of size of end-user populations + whether data scientists spend tons of time on Github vs. Web Developers…

Also all but 1 of the issues they found relates to test code, it seems people are a little less careful compared to functional code.

Also in terms of mistakes codereviewdoctor twice linked to the same issue in their blog https://github.com/tensorflow/tensorflow/issues/53636 and raised the PR to the wrong project https://github.com/tensorflow/tensorflow/pull/53637 (I guess Tensorflow vendors Keras, easy mistake)

Tensorflow

Source in github

Edit (link): https://github.com/tensorflow/tensorflow/issues/53549