It trains the LeNet-5 neural network on the MNIST dataset from scratch, and afterwards you can give it newly hand-written digits 0 to 9 and it will hopefully recognize the digit for you.
Ciro Santilli created a small fork of this repo at lenet adding better automation for:
- extracting MNIST images as PNG
- ONNX CLI inference taking any image files as input
- a Python
tkinterGUI that lets you draw and see inference live - running on GPU
Install on Ubuntu 24.10 with:We use our own
sudo apt install protobuf-compiler
git clone https://github.com/activatedgeek/LeNet-5
cd LeNet-5
git checkout 95b55a838f9d90536fd3b303cede12cf8b5da47f
virtualenv -p python3 .venv
. .venv/bin/activate
pip install \
Pillow==6.2.0 \
numpy==1.24.2 \
onnx==1.13.1 \
torch==2.0.0 \
torchvision==0.15.1 \
visdom==0.2.4 \
;pip install because their requirements.txt uses >= instead of == making it random if things will work or not.On Ubuntu 22.10 it was instead:
pip install
Pillow==6.2.0 \
numpy==1.26.4 \
onnx==1.17.0 torch==2.6.0 \
torchvision==0.21.0 \
visdom==0.2.4 \
;Then run with:This script:
python run.pyIt throws a billion exceptions because we didn't start the Visdom server, but everything works nevertheless, we just don't get a visualization of the training.
The terminal outputs lines such as:
Train - Epoch 1, Batch: 0, Loss: 2.311587
Train - Epoch 1, Batch: 10, Loss: 2.067062
Train - Epoch 1, Batch: 20, Loss: 0.959845
...
Train - Epoch 1, Batch: 230, Loss: 0.071796
Test Avg. Loss: 0.000112, Accuracy: 0.967500
...
Train - Epoch 15, Batch: 230, Loss: 0.010040
Test Avg. Loss: 0.000038, Accuracy: 0.989300One of the benefits of the ONNX output is that we can nicely visualize the neural network on Netron:
To run the demos locally, tested on Ubuntu 22.10:and this opens up the demos on the browser.
git clone https://github.com/liabru/matter-js
cd matter-js
git checkout 0.19.0
npm install
npm run devLikely the best JavaScript 2D game engine as of 2023.Uses Matter.js as a physics engine if enabled. There's also an alternative (in-house?) "arcade" engine: photonstorm.github.io/phaser3-docs/Phaser.Physics.Arcade.ArcadePhysics.html but it appears to be simpler/less robust (but also possibly faster).
- github.com/photonstorm/phaser
- phaser.io/
- phaser.io/examples/v3.85.0/games contains the demo games
The bad:
- Clunky UI
- circuit diagram doesn't show any state??
External 3D view of the Brodmann areas
. Source. CLI hello world:
gnuplot -p -e 'p sin(x)' Git tips The key to solve conflicts: see the two conflicting diffs by
Ciro Santilli 40 Updated 2025-07-16
The key to solve conflicts is:
You have to understand what are the two commits that touched a given line (one from master, one from features), and then combine them somehow.
In order to solve conflicts, you just have to understand what commit you are trying to move where.
E.g. if from:we do:what happens step by step is first 6 is moved on top of 5:and then 7 is moved on top of the new 6:
5 master
|
4 7 my-feature HEAD
| |
3 6
|/
2
|
1git rebase master6on5 HEAD
|
5 master
|
4 7 my-feature
| |
3 6
| |
2-----------------+
|
17on5 HEAD
|
6on5
|
5 master
|
4 7 my-feature
| |
3 6
| |
2-----------------+
|
17on5 my-feature HEAD
|
6on5
|
5 master
|
4
|
3
|
2
|
1For the strong.
git log --abbrev-commit --decorate --graph --pretty=oneline master HEADOutput:
* b4ec057 (master) 5
* 0b37c1b 4
| * fbfbfe8 (HEAD -> my-feature) 7
| * 7b0f59d 6
|/
* 661cfab 3
* 6d748a9 2
* c5f8a2c 1If we also add the As we can see, this removes any commit that is neither:
--simplify-by-decoration, which you very often want want on a real repository with many commits:* b4ec057 (master) 5
| * fbfbfe8 (HEAD -> my-feature) 7
|/
* c5f8a2c 1- under a branch or tag
- at the intersection of too branches or tags
- stackoverflow.com/questions/600079/how-do-i-clone-a-subdirectory-only-of-a-git-repository/52269934#52269934
- summaries:
- dupes:
- file or directory
- file
- only small files:
The Quora question: www.quora.com/Are-there-any-PhD-programs-in-training-an-AI-system-to-play-computer-games-Like-the-work-DeepMind-do-combining-Reinforcement-Learning-with-Deep-Learning-so-the-AI-can-play-Atari-games
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