MTS (MLP-Torch-Sklearn): Pytorch MLP implementation for Sklearn-like datasets classification and regression.
- Regression/classification using sklearn-like (numeric csv) datasets
- Logging, model loading and saving, hyper-parameter tuning, easy model configuration.
- Well-deigned for pytorch-preferred users who just stepped to the world of deep learning (DL) and want to understand important DL concepts with some toy examples.
The output will be like:
*******************eval on test set of iris*******************
0.0 1.00 1.00 1.00 6
1.0 1.00 1.00 1.00 3
2.0 1.00 1.00 1.00 6
accuracy 1.00 15
macro avg 1.00 1.00 1.00 15
weighted avg 1.00 1.00 1.00 15
** will add this to a Colab notebook
- Install
git clone https://github.com/wangcongcong123/mts.git
cd mts
pip install -r requirements.txt
- Obtain and split a data set. This downloads datasets from scikit-learn lib and convert them to csv format that this repo requires.
python obtain_split_data.py --dataset_name iris --test_size 0.1
python obtain_split_data.py --dataset_name covtype --test_size 0.1
python obtain_split_data.py --dataset_name digits --test_size 0.1
python obtain_split_data.py --dataset_name boston --test_size 0.1
** try python obtain_split_data.py --help
for the details of each parameter.
- Train and eval
python run.py --dataset_name iris --task cls --train_epochs 100 --train_batch_size 130 --device cpu --lr 0.03 --do_train --do_eval
python run.py --dataset_name covtype --task cls --train_epochs 120 --train_batch_size 128 --device cuda --lr 0.003 --do_train --do_eval
python run.py --dataset_name digits --task cls --train_epochs 100 --train_batch_size 128 --device cpu --lr 0.003 --do_train --do_eval
python run.py --dataset_name boston --task reg --train_epochs 100 --train_batch_size 128 --device cpu --lr 0.03 --do_train --do_eval
** try python run.py --help
for the details of each parameter.
- Tracking the training process using tensorboard
tensorboard dev upload --logdir runs
.
** There are many others, which encourages the community to incorporate them into this repository.