chexpert | before add weights to each model , my best auc | Machine Learning library

 by   inisis Shell Version: Current License: No License

kandi X-RAY | chexpert Summary

kandi X-RAY | chexpert Summary

chexpert is a Shell library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Docker applications. chexpert has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

before add weights to each model, my best auc is 0.922@2.2. after add weights to each model,my best auc is 0.923@2.8.
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              chexpert has a low active ecosystem.
              It has 5 star(s) with 1 fork(s). There are 2 watchers for this library.
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              It had no major release in the last 6 months.
              chexpert has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of chexpert is current.

            kandi-Quality Quality

              chexpert has no bugs reported.

            kandi-Security Security

              chexpert has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              chexpert does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
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              Without a license, all rights are reserved, and you cannot use the library in your applications.

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              chexpert releases are not available. You will need to build from source code and install.

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            chexpert Key Features

            No Key Features are available at this moment for chexpert.

            chexpert Examples and Code Snippets

            No Code Snippets are available at this moment for chexpert.

            Community Discussions

            QUESTION

            Binary DenseNet 121 Classifier only predicting positive with probability >0.5
            Asked 2020-Apr-18 at 04:07

            I borrowed code from this github repo for training of a DenseNet-121 [https://github.com/gaetandi/cheXpert/blob/master/cheXpert_final.ipynb][1]

            The github code is for 14 class classification on the CheXpert chest X-ray dataset. I've revised it for binary classification.

            ...

            ANSWER

            Answered 2020-Apr-18 at 04:07

            It looks like you have adapted the training correctly for the binary classification, but the prediction wasn't, as you are still trying it as if it were a multi-class prediction.

            The output of your model (varOutput) has the size (batch_size, 1), since there is only one class. The maximum across that dimension will always be 0, since that is the only class available, there is no separate class for 1.

            This single class represents both cases (0 and 1), so you can consider it is a the probability of it being positive (1). To get the distinct value of either 0 or 1, you simply use a threshold of 0.5, so everything below that receives the class 0 and above that 1. This can be easily done with torch.round.

            But you also have another problem, you're applying the sigmoid function twice in a row, once in the classifier nn.Sigmoid() and then afterwards again torch.sigmoid(varOutput). That is problematic, because sigmoid(0) = 0.5, hence all your probabilities are over 0.5.

            The output of your model are already the probabilities, the only thing left is to round them:

            Source https://stackoverflow.com/questions/61282680

            QUESTION

            Can't replace classifier on Densenet121 in pytorch
            Asked 2019-Sep-05 at 19:30

            I am trying to do some transfer learning using this github DenseNet121 model (https://github.com/gaetandi/cheXpert.git). I'm running into issues resizing the classification layer from 14 to 2 outputs.

            Relevant part of the github code is:

            ...

            ANSWER

            Answered 2019-Sep-05 at 19:18

            If you want to replace the classifier inside densenet121 that is a member of your model you need to assign

            Source https://stackoverflow.com/questions/57807050

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

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            You can download it from GitHub.

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            https://github.com/inisis/chexpert.git

          • CLI

            gh repo clone inisis/chexpert

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            git@github.com:inisis/chexpert.git

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