Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/gralliry/pytorch-nn-model-design
Many tasks of pytorch for Network-Model-Design
https://github.com/gralliry/pytorch-nn-model-design
deep-learning model-design neural-network project python pytorch
Last synced: about 3 hours ago
JSON representation
Many tasks of pytorch for Network-Model-Design
- Host: GitHub
- URL: https://github.com/gralliry/pytorch-nn-model-design
- Owner: gralliry
- License: gpl-3.0
- Created: 2024-02-01T02:58:54.000Z (9 months ago)
- Default Branch: master
- Last Pushed: 2024-08-18T10:03:19.000Z (3 months ago)
- Last Synced: 2024-08-18T11:23:40.148Z (3 months ago)
- Topics: deep-learning, model-design, neural-network, project, python, pytorch
- Language: Python
- Homepage:
- Size: 10.4 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Pytorch-Network-Model-Design
## Description
Many tasks of ```Pytorch``` for learning Network-Model-Design included.
Some of the tasks can be found in [kaggle.com](https://kaggle.com):
* This is just a practice project, and many of the tasks on kaggle.com don't have great results.
* In fact, there is no need to use __pytorch__ for some __machine learning__ tasks (unless you have a high understanding of
the underlying principles of machine learning), which can lead to some less efficient work.
* If you can, try to use machine learning to complete tasks on kaggle.
I'm putting together a collection of simple and helpful tasks that I hope will help myself and others who
want to learn deep learning. (Or just for a simple deep learning task.)
Consider introducing tasks for ```Scikit-Learn```, ```TensorFlow```, etc. later. (just consider)
## Examples / Tasks
* Chinese to English
* Next Frame Prediction
* Sentiment Analysis on Movie Review
* Binary Classification with a Bank Churn Dataset
* Multi-Class Prediction of Obesity Risk
* Regression with a Mohs Hardness Dataset
* ......
## Structure
The sample files are in the ```template``` folder.
Note:
* Since the general model parameter file is large, it will not be uploaded to __GitHub__, if the code does not
have the logic to create this folder, please create your own ```checkpoint``` folder.
* If the dataset file is small ( <5M ), it is uploaded directly to __GitHub__ in the ```dataset``` folder. Some cleaned data is also placed / generated in this folder.
```
dataset/ # folder to store the data set
checkpoint/ # folder to store model parameters
criterion.py # custom loss function
dataset.py # custom dataset
model.py # model
train.py # train the model
test.py # test the model
```
## Contributors
* [gralliry(Liang Jianye)](https://github.com/gralliry)
* zuozuo
## Contact
Email: [email protected]
## License
```GNU General Public License v3.0```
This project has open source tasks, according to the provisions of the open source agreement, this project is open
source