Skip to main content

pytorch❤️keras

Project description

Pytorch❤️Keras

The torchkeras library is a simple tool for training neural network in pytorch jusk in a keras style. 😋😋

1, Introduction

With torchkeras, You need not to write your training loop with many lines of code, all you need to do is just

like these two steps as below:

(i) create your network and wrap it and the loss_fn together with torchkeras.KerasModel like this: model = torchkeras.KerasModel(net,loss_fn=nn.BCEWithLogitsLoss()) a metrics_dict parameter is optional.

(ii) fit your model with the training data and validate data.

The main code of use torchkeras is like below.

import torch 
import torchkeras

#use torchkeras.KerasModel 
model = torchkeras.KerasModel(net,
                              loss_fn = nn.BCEWithLogitsLoss(),
                              optimizer= torch.optim.Adam(net.parameters(),lr = 0.001),
                              metrics_dict = {"acc":torchmetrics.Accuracy(task='binary')}
                             )
dfhistory=model.fit(train_data=dl_train, 
                    val_data=dl_val, 
                    epochs=20, 
                    patience=3, 
                    ckpt_path='checkpoint.pt',
                    monitor="val_acc",
                    mode="max")

This project seems somehow powerful, but the source code is very simple.

Actually, less than 200 lines of Python code.

If you want to understand or modify some details of this project, feel free to read and change the source code!!!


2, Features

Besides the basic torchkeras.KerasModel, another much more powerful class torchkeras.LightModel is created to support many other features.

The KerasModel is much simpler, and is recommended for beginner users.

The LightModel borrows many features from the library pytorch_lightning and shows a best practice.

Although different, the usage of torchkeras.KerasModel and torchkeras.LightModel is very similar.

features torchkeras.KerasModel torchkeras.LightModel
progress bar
early stopping
metrics from torchmetrics
gpu training
multi-gpus training(ddp)
tensorboard callback

3, Basic Examples

You can follow these full examples to get started with torchkeras.

Have fun!😋😋

If you want to understand or modify some details of this project, feel free to read and change the source code!!!

Any other questions, you can contact the author form the wechat official account below:

算法美食屋

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

torchkeras-3.3.2.tar.gz (15.9 kB view details)

Uploaded Source

Built Distribution

torchkeras-3.3.2-py3-none-any.whl (16.6 kB view details)

Uploaded Python 3

File details

Details for the file torchkeras-3.3.2.tar.gz.

File metadata

  • Download URL: torchkeras-3.3.2.tar.gz
  • Upload date:
  • Size: 15.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.0

File hashes

Hashes for torchkeras-3.3.2.tar.gz
Algorithm Hash digest
SHA256 b6ce16d898d5e6777b66fbf9f2e7a3c0eef40f5de8e345308c358f5255476150
MD5 ea5bcccec3f0ad06507b27753b4d2428
BLAKE2b-256 fdac07e2bb43801b15aec32e7eb57fe50e11d0679c6ff9c1f5e40b3175ba0da9

See more details on using hashes here.

File details

Details for the file torchkeras-3.3.2-py3-none-any.whl.

File metadata

  • Download URL: torchkeras-3.3.2-py3-none-any.whl
  • Upload date:
  • Size: 16.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.0

File hashes

Hashes for torchkeras-3.3.2-py3-none-any.whl
Algorithm Hash digest
SHA256 76ba27b476c2afeebaef49334fd6425a498e1c52493ae8bca6f1a5974e6caf33
MD5 1888596ed2f461b833bb2d177d09d62f
BLAKE2b-256 7392ccd7d8df5cc359525e13b8dd062310e72cf2f2f6eda3d5d614409c14a1f0

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page