https://github.com/activatedgeek/lenet-5
PyTorch implementation of LeNet-5 with live visualization
https://github.com/activatedgeek/lenet-5
cnn convolutional-neural-networks deep-learning deep-neural-networks lenet5 machine-learning pytorch
Last synced: 7 months ago
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PyTorch implementation of LeNet-5 with live visualization
- Host: GitHub
- URL: https://github.com/activatedgeek/lenet-5
- Owner: activatedgeek
- Created: 2017-11-12T04:25:33.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2023-01-27T01:11:49.000Z (almost 3 years ago)
- Last Synced: 2025-03-31T11:05:18.636Z (7 months ago)
- Topics: cnn, convolutional-neural-networks, deep-learning, deep-neural-networks, lenet5, machine-learning, pytorch
- Language: Python
- Homepage:
- Size: 10.7 KB
- Stars: 232
- Watchers: 3
- Forks: 97
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
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README
# LeNet-5
This implements a slightly modified LeNet-5 [LeCun et al., 1998a] and achieves an accuracy of ~99% on the [MNIST dataset](http://yann.lecun.com/exdb/mnist/).
## Setup
Install all dependencies using the following command
```
$ pip install -r requirements.txt
```
## Usage
Start the `visdom` server for visualization
```
$ python -m visdom.server
```
Start the training procedure
```
$ python run.py
```
See epoch train loss live graph at [`http://localhost:8097`](http://localhost:8097).
The trained model will be exported as ONNX to `lenet.onnx`. The `lenet.onnx` file can be viewed with [Neutron](https://www.electronjs.org/apps/netron)
## References
[[1](http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf)] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. "Gradient-based learning applied to document recognition." Proceedings of the IEEE, 86(11):2278-2324, November 1998.