GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Already on GitHub? Sign in to your account. I have download the recent tensorflow from github, but it is not work for current keras, what should do?
How could I know which version of tensorflow has been used for current keras?
I guess you are on r0. If you go back to 0. I have the same problem with current Keras master and Tensorflow 0. Both give me the same error:. Instead of v0. If it still gives the same error, you might have some of the old install files lying around in your pythonpath. This issue has been automatically marked as stale because it has not had recent activity. It will be closed after 30 days if no further activity occurs, but feel free to re-open a closed issue if needed.
I think the issue still persists since I have not found a way to determine, which versions of Tensorflow does keras support. For example, Tensorflow 1. FailedPreconditionError: Attempting to use uninitialized value. I have no way of determining whether the error is due to Tensorflow version mismatch or some other GPU problem.
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Active 9 months ago. Viewed k times. Question is the same as the title says. Aerin Aerin Active Oldest Votes. You can print it by running this on the command line: python -c 'import keras; print keras. Om Sao 3, 1 1 gold badge 25 25 silver badges 40 40 bronze badges. For python3 version use this You can write: python import keras keras. Noosh Noosh 5 5 silver badges 6 6 bronze badges. The simplest way is using pip command: pip list grep Keras.
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Email Required, but never shown. The Overflow Blog. Podcast Ben answers his first question on Stack Overflow. The Overflow Bugs vs. Featured on Meta. Responding to the Lavender Letter and commitments moving forward.
Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. How can I check which one is installed I use linux.
According to the documentation.
Also as suggested in this answer. If you are running this command in jupyter notebook, check out the console from where you have launched the notebook. If you are sceptic whether you have installed the tensorflow gpu version or not. You can install the gpu version via pip. Learn more. Asked 3 years, 3 months ago. Active 2 years, 10 months ago. Viewed k times. SalvadorDali I have tried the answers to that question, but it does not print out anything. Have you tried my answer there. It is either printing something or failing and the answer explain what each of these steps mean.
Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. This also didn't update Keras. Both of the two packages on Anaconda Cloud are not built the keras team, which explains why the package is outdated.
For the time being,package keras 2. If a "module is not found" error is thrown out, reinstall keras with --strict-channel-priority to make sure dependencies of keras are install from conda-forge as well. Learn more. How to update Keras with conda Ask Question. Asked 1 year ago. Active 9 months ago. Viewed 8k times. I'd like to update Keras to version 2. Currently, I've got Keras 2. First, I tried conda update keras which didn't work. Any ideas? MJimitater MJimitater 3 3 silver badges 11 11 bronze badges.
Active Oldest Votes. Solution: To get the keras 2. MJimitater 3 3 silver badges 11 11 bronze badges. Simba Simba 8, 2 2 gold badges 27 27 silver badges 40 40 bronze badges. Using conda in the command line, the command below would install keras 2. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown.
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In this future, the keras package on PyPI will be the same as tf. This release 2. This helps address user confusion regarding differences and incompatibilities between tf.
There is now only one Keras: tf. Keras 2. In particular, it fixes an issue with using Keras models across multiple threads. It maintains compatibility with TensorFlow 1. This release brings the API in sync with the tf. However note that it does not support most TensorFlow 2. If you need these features, use tf. This is also the last major release of multi-backend Keras. Going forward, we recommend that users consider switching their Keras code to tf. It implements the same Keras 2.
It is also better maintained. Development will focus on tf. We will keep maintaining multi-backend Keras over the next 6 months, but we will only be merging bug fixes. API changes will not be ported. The next release will be 2. The 2. Multi-backend Keras is superseded by tf. At this time, we recommend that Keras users who use multi-backend Keras with the TensorFlow backend switch to tf. See here for the changelog since 2. BertrandDechouxChrisGllDrefJamesHinshelwoodMarcoAndreaBuchmannageronalfasstblue-atomchasebrignaccshubhamraodanFromTelAvivdatumboxfarizrahman4ufcholletfuzzythecatgabrieldemarmiessehadifarheytitlehsgkimjankrepljoelthchaoknightXunkoumllinjinjinlvapeabnikoladzeozabludaqlzhroyweirvinassriyogesh94tacaswelltaehoonleetedyuxuhdevyanboliangyongzxyuanxiaosc.
For changes since version 2. Ajk4Anner-deJongAtcoldDrefEyeBoolageronbriannemsickcclaussdavidtvsdstineeTomateebatuhankaynakeliberisfarizrahman4ufcholletfuzzythecatgabrieldemarmiessejlopezpenakamil-kaczmarekkbattocchikmaderkvecheramaxpumperlamkazepavithrasvrvinassachinrukseriousmac, soumyactaehoonleeyanboliangyongzxyuyang-huang.Keras is an API designed for human beings, not machines.
It also has extensive documentation and developer guides. Keras is the most used deep learning framework among top-5 winning teams on Kaggle. Because Keras makes it easier to run new experiments, it empowers you to try more ideas than your competition, faster. And this is how you win.
Built on top of TensorFlow 2. It's not only possible; it's easy. Take advantage of the full deployment capabilities of the TensorFlow platform. It's also easy to serve Keras models as via a web API. Keras is a central part of the tighly-connected TensorFlow 2. Keras has the low-level flexibility to implement arbitrary research ideas while offering optional high-level convenience features to speed up experimentation cycles.
Because of its ease-of-use and focus on user experience, Keras is the deep learning solution of choice for many university courses.Install Tensorflow GPU Keras and Theano for Anaconda Navigator in Windows. Step by step
It is widely recommended as one of the best ways to learn deep learning. Deep learning for humans. Iterate at the speed of thought. Exascale machine learning. Deploy anywhere. A vast ecosystem. State-of-the-art research. An accessible superpower. Take it from our users. Aakash Nain Research Engineer " Keras is that sweet spot where you get flexibility for research and consistency for deployment.This chapter explains about how to install Keras on your machine.
Before moving to installation, let us go through the basic requirements of Keras. Keras is python based neural network library so python must be installed on your machine. If python is properly installed on your machine, then open your terminal and type python, you could see the response similar as specified below. If Python is not installed, then visit the official python link - www. Virtualenv is used to manage Python packages for different projects. This will be helpful to avoid breaking the packages installed in the other environments.
So, it is always recommended to use a virtual environment while developing Python applications. Linux or mac OS users, go to your project root directory and type the below command to create virtual environment. Move to the folder and type the below command. Hopefully, you have installed all the above libraries on your system. If these libraries are not installed, then use the below command to install one by one. It is an open source machine learning library.
It is used for classification, regression and clustering algorithms. Seaborn is an amazing library that allows you to easily visualize your data. As of now, we have completed basic requirements for the installtion of Kera. We believe that you have installed anaconda cloud on your machine. If anaconda is not installed, then visit the official link, www. Launch anaconda prompt, this will open base Anaconda environment. Let us create a new conda environment. This process is similar to virtualenv.