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COVNet | Artificial Intelligence Distinguishes COVID-19 | Machine Learning library

 by   bkong999 Python Version: Current License: Non-SPDX

 by   bkong999 Python Version: Current License: Non-SPDX

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kandi X-RAY | COVNet Summary

COVNet is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. COVNet has no bugs, it has no vulnerabilities and it has low support. However COVNet build file is not available and it has a Non-SPDX License. You can download it from GitHub.
This is a PyTorch implementation of the paper "Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT". It supports training, validation and testing for COVNet.
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Support
Quality
Quality
Security
Security
License
License
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kandi-support Support

  • COVNet has a low active ecosystem.
  • It has 160 star(s) with 61 fork(s). There are 12 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 4 open issues and 24 have been closed. On average issues are closed in 15 days. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of COVNet is current.
COVNet Support
Best in #Machine Learning
Average in #Machine Learning
COVNet Support
Best in #Machine Learning
Average in #Machine Learning

quality kandi Quality

  • COVNet has 0 bugs and 0 code smells.
COVNet Quality
Best in #Machine Learning
Average in #Machine Learning
COVNet Quality
Best in #Machine Learning
Average in #Machine Learning

securitySecurity

  • COVNet has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • COVNet code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
COVNet Security
Best in #Machine Learning
Average in #Machine Learning
COVNet Security
Best in #Machine Learning
Average in #Machine Learning

license License

  • COVNet has a Non-SPDX License.
  • Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.
COVNet License
Best in #Machine Learning
Average in #Machine Learning
COVNet License
Best in #Machine Learning
Average in #Machine Learning

buildReuse

  • COVNet releases are not available. You will need to build from source code and install.
  • COVNet has no build file. You will be need to create the build yourself to build the component from source.
  • Installation instructions are not available. Examples and code snippets are available.
  • COVNet saves you 155 person hours of effort in developing the same functionality from scratch.
  • It has 386 lines of code, 17 functions and 6 files.
  • It has high code complexity. Code complexity directly impacts maintainability of the code.
COVNet Reuse
Best in #Machine Learning
Average in #Machine Learning
COVNet Reuse
Best in #Machine Learning
Average in #Machine Learning
Top functions reviewed by kandi - BETA

kandi has reviewed COVNet and discovered the below as its top functions. This is intended to give you an instant insight into COVNet implemented functionality, and help decide if they suit your requirements.

  • Evaluate the model
    • Prints progress bar for training
    • Calculates the accuracy for each class
    • Compute the classifier
  • Train the model
    • Print training progress
      • Prints the epoch progress
        • Parse command line arguments
          • Generate weights for balanced classification
            • Get the learning rate of an optimizer

              Get all kandi verified functions for this library.

              Get all kandi verified functions for this library.

              COVNet Key Features

              Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT

              Citation

              copy iconCopydownload iconDownload
              @article{li2020artificial,
                title={Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT},
                author={Li, Lin and Qin, Lixin and Xu, Zeguo and Yin, Youbing and Wang, Xin and Kong, Bin and Bai, Junjie and Lu, Yi and Fang, Zhenghan and Song, Qi and Cao, Kunlin and others},
                journal={Radiology},
                year={2020}
              }
              

              Prepare data

              copy iconCopydownload iconDownload
              data
              ├── caseid1
              |   ├── masked_ct.nii
              |   └── mask.nii.gz
              ├── caseid2
              |   ├── masked_ct.nii
              |   └── mask.nii.gz
              ├── caseid3
              |   ├── masked_ct.nii
              |   └── mask.nii.gz
              ├── caseid4
              |   ├── masked_ct.nii
              |   └── mask.nii.gz
              ├── train.csv
              └── val.csv
              

              Training

              copy iconCopydownload iconDownload
              python main.py
              

              Validation and Testing

              copy iconCopydownload iconDownload
              python test.py
              

              Correct way to apply gradients in TF2 custom training loop with multiple Keras models

              copy iconCopydownload iconDownload
              trainables = model_a.trainable_weights + model_b.trainable_weights + model_c.trainable_weights
              

              Community Discussions

              Trending Discussions on COVNet
              • Correct way to apply gradients in TF2 custom training loop with multiple Keras models
              Trending Discussions on COVNet

              QUESTION

              Correct way to apply gradients in TF2 custom training loop with multiple Keras models

              Asked 2020-Apr-10 at 07:37

              I am working to implement a custom training loop with GradientTape involving multiple Keras models. I have 3 networks, model_a, model_b, and model_c. I have created a list to hold their trainbale_weights as:

              trainables = list() 
              trainables.append(model_a.trainable_weights) # CovNet 
              trainables.append(model_b.trainable_weights) # CovNet 
              trainables.append(model_c.trainable_weights) # Fully Connected Network
              

              I then calculate loss and try to apply gradients as:

              loss = 0.
              optimizer = tf.keras.optimizers.Adam()
              for _, (x, y) in enumerate(train_dataset):
                 with tf.GradientTape() as tape:
                   y = ...
                   loss = ... # custom loss function!
              gradients = tape.gradient(loss, trainables)
              optimizer.apply_gradients(zip(gradients, trainables))    
              

              But I get a following error I am not sure where's the mistake:

              AttributeError: 'list' object has no attribute '_in_graph_mode'
              

              If I iterate over gradients and trainables and then apply gradients it works but I am not sure if this is the right way to do it.

              for i in range(len(gradients)):
                 optimizer.apply_gradients(zip(gradients[i], trainables[i]))
              

              ANSWER

              Answered 2020-Apr-10 at 07:37

              The problem is that tape.gradient expects trainables to be a flat list of trainable variables rather than a list of lists. You can solve this issue by concatenating all the trainable weights into a flat list:

              trainables = model_a.trainable_weights + model_b.trainable_weights + model_c.trainable_weights
              

              Source https://stackoverflow.com/questions/60395398

              Community Discussions, Code Snippets contain sources that include Stack Exchange Network

              Vulnerabilities

              No vulnerabilities reported

              Install COVNet

              You can download it from GitHub.
              You can use COVNet like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

              Support

              For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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