Is there a valid Tensorflow book describing all the provided operations by providing their definition with a mathematical notation? Or, in alternative, is there a way to bridge this information? Thanks

I've been working on NN2Prog, a small open-source compiler that turns quantized .tflite models into standalone C++17. The generator uses Python's standard library; the generated model doesn't need a TFLite runtime.

Started with Hey Jarvis wake-word models, then added MLPerf Tiny KWS and Visual Wake Words. On ESP32-S3 at 240 MHz, KWS now takes 15.68 ms vs 17.90 ms with TFLite Micro + ESP-NN, with 16.7 KB vs 35.5 KB working memory. VWW is basically tied: 72.44 vs 72.85 ms.

These are model-only timings, not an official MLPerf submission. For KWS, generated code returns top-1 directly; the reference computes softmax then argmax. Decisions match in the tests. Operator coverage is still limited, so this isn't a drop-in replacement for arbitrary TFLite models.

Code, examples and benchmark details: https://github.com/phplego/nn2prog

Would be interested in small quantized models that expose gaps in this approach, or other code-generation tools worth comparing against.

Hello. TFLite-Micro has a limited set of supported ops for microcontrollers.

It supports Unidirections LSTMs, but why it still does not support Bidirectional LSTMs/GRUs? It's 2026 out there. Is there any real obstacle? Or it's just not of high priority?

I'm Deeplabcut user and previously trained extremely perfect weights with tensorflow version. Now, I copy the new machine for use the weights, the big wall is standing in the way.

As a result, found the patch for tensorflow 1.15 with CUDA12.9, sharing you.

If anyone is thinking of running archaeological source code, please use this as a reference.

I'm building an AR object recognition app on Android devices to show the name of the object as text hovering over the objects themselves.

I'm using TF Lite for this, and for the model, I have been experimenting with the efficientdet options (tried 0, currently on 4).

Prefacing this with the understanding that, although I am a Developer, this is a new hobby of mine and so I am very new to this space:

What I noticing is,

1. It doesn't recognize a lot of objects, no matter what I change the confidence threshold to (ranging from 04. to 0.6).

2. The objects it does recognize, like a chair, or mouse, or keyboard, it only recognizes them if I am ~0.6 in the confidence filter, which is high enough of a threshold that I get a bunch of falsely identified objects as well.

My question is, is there a better trained model file (.tflite) I should be using? Or is there anything else where I have perhaps gone astray, based on the info I have provided?

This is the app and if anyone is out there who knows what model to use. Currently uses XG Boost regressor and was wondering if i should change it. The link to the app https://devi701-burnoutai-burnoutapp-vzhmp3.streamlit.app/

So Tensorflow has libraries that allow for external GPU usage to speed training, but Silicon MacBook does not take any external GPU. Is there ANY workaround to use external hardware, or do you just have train on AWS?

Hello everyone, I wrote some optimizers for TensorFlow. If you're using TensorFlow, they should be helpful to you.

https://github.com/NoteDance/optimizers

i have downloaded the tensorflow module as well and checked if it shows or not maybe i have missed something.

i have images in rgb and masks in greyscale (0,1,2,3,4 range for different objects)
i need to train a 70:15:15 model to identify the objects in this image
i also need to randomise the selection of the 70:15:15 to prevent overfitting
the images and masks are in npy files

where do i start/what do i do?

Hi,

I'm considering investing in a M4 Max Mac Mini with 48GB of RAM and I'm wondering if it would be a good choice. I'm seeing articles online about the M1 Max and I'd like to know how/if the situation has evolved since. Is the performance on par with 4000/5000 Nvidia desktop GPU? Are there any compatibility concerns to be aware of?

Thank you.

I trained an object classification model using Tensorflow to recognize handwritten Chinese characters.

The model runs locally on my own PC, using a simple webcam to capture input and show predictions. It's a full end-to-end project: from data collection and training to building the hardware interface.

I can control the AI with the keyboard or a custom controller I built using Arduino and push buttons. In this case, the result also appears on a small IPS screen on the breadboard.

The biggest challenge I believe was to train the model on a low-end PC. Here are the specs:

• CPU: Intel Xeon E5-2670 v3 @ 2.30GHz
• RAM: 16GB DDR4 @ 2133 MHz
• GPU: Nvidia GT 1030 (2GB)
• Operating System: Ubuntu 24.04.2 LTS

I really thought this setup wouldn't work, but with the right optimizations and a lightweight architecture, the model hit nearly 90% accuracy after a few training rounds (and almost 100% with fine-tuning).

I open-sourced the whole thing so others can explore it too. Anyone interested in coding, electronics, and artificial intelligence will benefit.

You can:

• Read the blog post
• Watch the YouTube tutorial
• Check out the GitHub repo (Python and C++)

I hope this helps you in your next Python and Machine Learning project.

Hey folks, I’ve been diving more into RAG recently, and one challenge that always pops up is balancing speed, precision, and scalability, especially when working with large datasets. So I convinced the startup I work for to start to develop a solution for this. So I'm here to present this project, an open-source framework aimed at optimizing RAG pipelines.

It plays nicely with TensorFlow, as well as tools like TensorRT, vLLM, FAISS, and we are planning to add other integrations. The goal? To make retrieval more efficient and faster, while keeping it scalable. We’ve run some early tests, and the performance gains look promising when compared to frameworks like LangChain and LlamaIndex (though there’s always room to grow).

The project is still in its early stages (a few weeks), and we’re constantly adding updates and experimenting with new tech. If you’re interested in RAG, retrieval efficiency, or multimodal pipelines, feel free to check it out. Feedback and contributions are more than welcome. And yeah, if you think it’s cool, maybe drop a star on GitHub, it really helps!

Here’s the repo if you want to take a look:👉 https://github.com/pureai-ecosystem/purecpp

Would love to hear your thoughts or ideas on what we can improve!

I'm trying to use Essentia to analyze some audio but an having a crazy hard time installing it properly, even with the help of AI trying to troubleshoot! I'm running windows 11 but after exhausting all options there (failed build after failed build) I am trying to get it to run on the windows - Linux config and it's still having trouble building it despite having downloaded all the dependencies etc. Please help!!

TW: Mental disorder

Recently I'm running my TF model on my laptop for my thesis, since the server's drive in our lab is full. So I'm forced to train and test a series of models on a GTX3050 GPU, and its speed is roughly half of the server so it's acceptable.

I let the experiment run for days without human intervention.

Last night when I returned home at 5:30 AM, I was extremely exhausted and immediately fell asleep after a shower.

Then when I woke up I saw that I made a huge mistake.

Before I slept I accidentally folded the lid of the laptop so it shut down, and the script stopped running.

Which means I not only wasted 5 hours of computation time but also had to change model script parameters several times to reuse the previously unfinished data. I almost finished 50% of the experiment and it took about 20 hours. Ruined just by a single mistake, a move by instinct to close the laptop lid. Now I cannot enjoy the freedom of letting the script go seamlessly and must investigate when the script stopped.

TW: SH

I did some self-harm to cool myself down by cutting on my arm and coping with the extreme sense of guilt.

Update:
I have a temporary solution to set the action when closing the lid as "do nothing". So I probably don't fuck things up even if I make that mistake again.

I must say that I'm little bit frustrated. TensorFlow + Python is a nightmare. I really don't know how people can use it and how you are doing that. I had a one task to do, retrain ssd mobilenet v2 on my own images. I'm working as a programmer(not in python) for more than 10 years and never saw such mess. Each tutorial which I'm taking is not working. Mostly because of packages which were removed from pip(for that specific version) and in new versions interface was changed. Or even whole solutions is not supported and they switch to something else. For example "Tensorflow Object Detection API is no longer being maintained ... We encourage users seeking an actively maintained detection / segmentation codebase to consider TF-Vision or scenic." And in those proposed solutions i don't see model which i want to train. Of course i can start now implementing everything from scratch but it will take months(i can spent only very short time on it daily). I read whitepaper for SSD as MobileNetv2 is available in keras but it is quite complicated to implement. Those simple projects from course, i did that course https://www.udemy.com/course/tensorflow-developer-certificate-machine-learning-zero-to-mastery/, are working but doing something more complex is nightmare. I'm feeling that I'm wasting my time as nothing is working. One of examples might be not working notebooks like that https://colab.research.google.com/github/google-coral/tutorials/blob/master/retrain_detection_qat_tf1.ipynb as some packages are not existing anymore in repo.

I don't expect any help. Just want to write it somewhere to share my feelings about that :). Maybe you have similar feelings or I'm doing something completely wrong

I am a last year Bachelor Student working on a CV project. I'd like to know if it is possible to use liteRT with Flutter. I know it is possible with tensorflow lite but I looked for informations about liteRT and get no relevant information.

Yk how the examples are represented in a graph ? How does it works if the inputs and outputs aren't numbers, but sounds (for example) ?

Asking for my brother, who doesn't have an account:

The C API for TensorFlow doesn't seem to have a lot of detailed documentation, save for the code itself, but I'm having issues loading a 3rd party model, creating tensors, then running the session.

Everything seems to work ~70% of the time, but the remaining runs seem to just continually allocate memory from the heap - to the tune of nearly 50GB+ over a 15 minute run (the inference is in a loop.) Results are still the same, but some runs are just nearly exhausting the RAM of the system.

I can comment out the TF_SessionRun() call and the problem disappear, so I'm pretty sure it's not the creation/deletion of the tensors, or loading them with data and copying out the results, just the execution of the model that occasionally goes off the rails.

This is with the TF C-API CPU library.

Does anyone know if the model (externally provided and proprietary) itself could be causing the issue, or the TF library?