= activatedgeek/LeNet-5
{tag=PyTorch model}
https://github.com/activatedgeek/LeNet-5
This repository contains a very clean minimal <PyTorch> implementation of <LeNet-5> for <MNIST>.
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>{file} adding better automation for:
* <extracting MNIST images> as PNG
* <ONNX> CLI inference taking any image files as input
* a <Python tkinter> GUI that lets you draw and see inference live
* running on <GPU>
Install on <Ubuntu 24.10> with:
``
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 \
;
``
We use our own `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:
``
python run.py
``
This script:
* does a fixed 15 <epoch (deep learning)>[epochs] on the <training data>
* it then uses the trained net from memory to check accuracy with the <test data>
* then it also produces a `lenet.onnx` <ONNX> file which contains the trained network, nice!
It 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: 0.989300
``
And the runtime on <Ubuntu 22.10>, <Ciro Santilli's hardware/P51> was:
``
real 2m10.262s
user 11m9.771s
sys 0m26.368s
``
One of the benefits of the <ONNX> output is that we can nicely visualize the <neural network> on <Netron>:
\Image[https://raw.githubusercontent.com/cirosantilli/media/e9225ddf4bb8ce4bad8cc2a9d6503d683dec5db6/activatedgeek_LeNet-5_onnx.svg]
{title=<Netron> visualization of the <activatedgeek LeNet-5> <ONNX> output}
{description=
From this we can see the bifurcation on the computational graph as done in the code at:
``
output = self.c1(img)
x = self.c2_1(output)
output = self.c2_2(output)
output += x
output = self.c3(output)
``
This doesn't seem to conform to the original <LeNet-5> however?
}
{height=1200}
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