https://github.com/ritchieng/resnet-tensorflow

Modular Residual Networks implemented in TensorFlow. Easily change hyperparameters in a few lines.
https://github.com/ritchieng/resnet-tensorflow

residual-network tensorflow tensorlayer

Last synced: 3 months ago
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Modular Residual Networks implemented in TensorFlow. Easily change hyperparameters in a few lines.

• Host: GitHub
• URL: https://github.com/ritchieng/resnet-tensorflow
• Owner: ritchieng
• License: mit
• Created: 2016-12-15T19:49:00.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2016-12-16T06:51:30.000Z (about 8 years ago)
• Last Synced: 2024-10-03T12:38:21.119Z (4 months ago)
• Topics: residual-network, tensorflow, tensorlayer
• Language: Python
• Homepage:
• Size: 24.4 KB
• Stars: 34
• Watchers: 6
• Forks: 14
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Residual Networks in TensorFlow

## Residual Network in TensorFlow

This entire code is implemented in pure TensorFlow and I have made it simple to run with different settings.

## Simple Instructions

- Running Training and Evaluation

    - `python main.py`

        - If you want to modify any parameters, you can use for example `python main.py --n_epoch==10`

            - The default runs on CIFAR-10 dataset and this configuration is made for that.

            - `n_epoch`: number of epochs

                - Default `10`

            - `n_batch`: batch size

                - Default `64`

            - `n_img_row`: dimension of image (row)

                - Default `32`

            - `n_img_col`: dimension of image (col)

                - Default `32`

            - `n_img_channels`: number of channels

                - Default `3`

            - `n_classes`: number of classes

                - Default `10`

            - `lr`: learning rate (momentum optimizer)

                - Default `0.1`

            - `n_resid_units`: number of residual units

                - Default `5`

            - `lr_schedule`: number of epoch for the learning rate to decrease by `lr_factor`

                - Default `60`

                - This multiplies the LR every 60 epochs by `lr_factor`.

            - `lr_factor`: the factor for reducing LR

                - Default `0.1`.

        

- Running TensorBoard

    - Training logs

        - `tensorboard --logdir=train_log`

    - Evaluation logs

        - `tensorboard --logdir=eval_log`

    - You can use any path you want. 

        - If you encountered a `permission denied` error, you can easily solve it by changing the directory to `tmp/train_log`.

        - I experienced this while running on Amazon AWS and it was solved with this fix.

## Credits

- The original model was obtained from the [official repository](https://github.com/tensorflow/models/tree/master/resnet) by TensorFlow.

- The paper on [Residual Networks](https://arxiv.org/abs/1512.03385) on arXiv.org.

## Dependencies

- To simplify the code, I read the CIFAR dataset using [TensorLayer](https://github.com/zsdonghao/tensorlayer).

    - Simply run `sudo pip install tensorlayer` and you are good to go.

- TensorFlow v0.12

    - If you would like to run this code in a few minutes on Amazon AWS, just use the open-source AMI [TFAMI.v3](https://github.com/ritchieng/tensorflow-aws-ami).

## License

MIT