keras-io | Keras documentation, hosted live at kerasio | Machine Learning library

 by   keras-team Jupyter Notebook Version: Current License: Apache-2.0

kandi X-RAY | keras-io Summary

kandi X-RAY | keras-io Summary

keras-io is a Jupyter Notebook library typically used in Artificial Intelligence, Machine Learning applications. keras-io has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

Keras documentation, hosted live at

            kandi-support Support

              keras-io has a medium active ecosystem.
              It has 2322 star(s) with 1861 fork(s). There are 50 watchers for this library.
              It had no major release in the last 6 months.
              There are 347 open issues and 151 have been closed. On average issues are closed in 110 days. There are 60 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of keras-io is current.

            kandi-Quality Quality

              keras-io has no bugs reported.

            kandi-Security Security

              keras-io has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              keras-io is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              keras-io releases are not available. You will need to build from source code and install.
              Installation instructions are not available. Examples and code snippets are available.

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            keras-io Key Features

            No Key Features are available at this moment for keras-io.

            keras-io Examples and Code Snippets

            No Code Snippets are available at this moment for keras-io.

            Community Discussions


            Gradients are None while implementing GradCam
            Asked 2022-Mar-09 at 14:22

            I am trying to implement grad-cam for a tensorflow 2.0 model (created using the keras-api), but the gradients returned from the tape are always None.

            I am following the example given on

            My model is fairly simple, but I swapped it out for the builtin Xception model provided by tf.keras.applications in order to debug (no difference in behavior, so the problem must be with my code).



            Answered 2022-Mar-09 at 14:22

            Like @AloneTogether mentionned, the result of argmax is not differentiable, thus the None result after applying tape.gradients(...) is normal, since no gradient can be computed.

            While the result of argmax cannot be differentiated, it can be used to select the correct activation in the following way:



            AttributeError: module 'transformers' has no attribute 'TFBertModel'
            Asked 2021-Sep-06 at 03:34

            My env is created by pycharm, it has the followings packages: tensorflow==1.13.0rc1, CUDA = 11 andcorresponding cudn.but run from 1 to 269 line,print(f"Strategy: {strategy}") model.summary() After execution, the following error is reported:



            Answered 2021-Sep-06 at 03:34

            Tensorflow 2.6.0 works



            How to display Softmax highest probability
            Asked 2021-Jun-21 at 01:19

            I am running the following code. I am trying to display the probability of the predicted label next to the prediction but I have a hard time trying in doing so.



            Answered 2021-Jun-20 at 16:43

            I did not run the code, but I beleive preds variable will be having all the probabilities.

            For a particular input that you are trying to predict, if you want to see the maximum prediction value, you can do max(pred).


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


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