I am assuming you are using TensorFlow 2.1. Check with pip command: pip list, pip freeze, pip show. Examples Do you publish at NeurIPS and push the state-of-the-art in CV and NLP? Hi, To kown the Keras or/and Tensor flow (indirectly the tf.keras version), you can use the command line tools. If your issue is an implementation question, please ask your question on StackOverflow or join the Keras Slack channel and ask there instead of filing a GitHub issue. TensorFlow version check. [root@host conf]# pip list | grep Keras Keras 2.3.1 Keras-Applications 1.0.8 Keras-Preprocessing 1.1.0 [root@host conf]# Also note that the previous versions are not available in conda. Also, we dont want to change any other dependencies as they are working well. This short video presents ways to check whether TensorFlow or Keras is using GPU to train the model. Are you a machine learning researcher? conda install linux-64 v2.3.1; win-32 v2.1.5; noarch v2.4.3; osx-64 v2.3.1; win-64 v2.3.1; To install this package with conda run one of the following: conda install -c conda-forge keras A simple trick is to downgrade your keras version from 2.1.x to 2.0.y where y<=5. Value. Neural Network Tools for STM32 v1.1.0 (AI tools v4.1.0) Keras is a high-level neural networks API developed with a focus on enabling fast experimentation. If you are using the Python package management system pip, you can check the information of the installed package with the following command. Check out our Introduction to Keras for researchers. Check out our Introduction to Keras for engineers. > stm32ai --tools_version. Execute commands at the command prompt or terminal. As previously announced, we have discontinued multi-backend Keras to refocus exclusively on the TensorFlow implementation of Keras.. In the future, we will develop the TensorFlow implementation of Keras in the present repo, at keras-team/keras.For the time being, it is being developed in tensorflow/tensorflow and distributed as tensorflow.keras.In this future, the keras package on PyPI … Keras has the following key features: Allows the same code to run on CPU or on GPU, seamlessly. Check that you are up-to-date with the master branch of Keras. Further starter resources. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. GitHub Gist: instantly share code, notes, and snippets. The following are 30 code examples for showing how to use keras.__version__().These examples are extracted from open source projects. For this we have to check which version we currently have. Step-by-step procedure starting from creating conda environment till testing if TensorFlow and Keras Works. 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