Awesome
<img height="23" src="https://raw.githubusercontent.com/lh9171338/Outline/master/icon.jpg"/> MNIST Classify
Introduction
The repository contains the PyTorch implementation of image classification models for MNIST dataset, based on MLP, CNN, RNN, LSTM, and GRU.
Results
Metrics
| Model | #FLOPs (M) | #Params (K) | Acc (%) |
|---|---|---|---|
| RNN | 0.3 | 6.5 | 94.4 |
| LSTM | 1.3 | 24.1 | 98.5 |
| GRU | 1.0 | 18.3 | 98.9 |
| MLP | 0.5 | 238.0 | 98.7 |
| CNN | 0.6 | 43.8 | 99.3 |
Loss & Accuracy Curves
<p align="center"> <img width="80%" src="figure/loss.png"/> </p> <p align="center"> <img width="80%" src="figure/accuracy.png"/> </p>Requirements
pip install -r ./requirements.txt
Training & Testing
Training
python train.py --arch <ARCH> [--model_name <MODEL_NAME>] [--gpu <GPU_ID>]
Test
# Test one model
python test.py --arch <ARCH> [--model_name <MODEL_NAME>] [--gpu <GPU_ID>]
# Test all models
./run.sh