https://github.com/sseung0703/cnn_via_tensorflow2_low-level

Colvolutional neural network implementation with Tensorflow2.0 low level API only
https://github.com/sseung0703/cnn_via_tensorflow2_low-level

convolutional-neural-network low-level-programming tensorflow-2-example

Last synced: about 1 year ago
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Colvolutional neural network implementation with Tensorflow2.0 low level API only

• Host: GitHub
• URL: https://github.com/sseung0703/cnn_via_tensorflow2_low-level
• Owner: sseung0703
• Created: 2019-10-03T19:04:11.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2019-11-20T02:44:05.000Z (over 6 years ago)
• Last Synced: 2025-02-10T05:13:34.788Z (over 1 year ago)
• Topics: convolutional-neural-network, low-level-programming, tensorflow-2-example
• Language: Python
• Size: 41 KB
• Stars: 3
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# CNN_via_Tensorflow2_low-level

This project's purpose is building convolutional neural network implementation with Tensorflow2.0 low-level API only. It looks somewhat useless things because Keras layers are so easy to handle. However, I have many layers that were built in TF1 low-level API, and surely, there is no Keras layer for them. So, I have to do this, and perhaps some guys need it too.

# Require

- Tensorflow > 2.0

# Contents

- nets/tcl.py

    - Build a custom layer and add trainable or untrainable parameters.

    - Add regularization for each trainable parameters.

    - Define update function for the moving mean and the moving standard deviation.

    - Conditioning for training and inference phase.

    - Prototype of arg_scope. (will be updated)

    

- op_util.py

    - Define a loss function with regularization losses.

    - Build optimizer with computing and applying gradients.

    - Define steps for training and inference.

    - Learning rate scheduler

    

- train_and_validate.py

    - Load dataset, pre_processing algorithn, model, and optimizer.

    - Do train and validate.

    - Visualize the log via Tensorboard.

    

- dataloader.py

    - Load dataset

    - Define pre-processing algorithm.

    

- nets/ResNet.py and WResNet.py

    - Build a custom model via custom layers.

    - How to use implemented arg_scope

# To do

- Write Readme and milestones.

- Codes to save and load models without a checkpoint.

- Improve readability of a custom model.

- Find more things to do...