Tensorflow, for now only works with Python 3.6, 3.7 support is still in active development, make sure to install python 3.6 and try to set it up again, it should work Sorry i am only posting the question and i am only giving the answer for it.
You should be thinking “Which python version does tensorflow support?”
As Tensorflow only supports Python 3.6 as of now, you can install a different version of python alongside your standard one. Here are the steps I followed: Download the Python3.6 tgz file from the official website (eg. Python-3.6.6.tgz) You’ll normally find your new python install under /usr/local/bin.
You may be asking “What version of TensorFlow does Ubuntu support?”
One answer is, ubuntu and Windows include GPU support., for tensor Flow 1.x, CPU and GPU packages are separate: Python 3.8 support requires Tensor. Flow 2.2 or later., mac OS 10.12.6 (Sierra) or later (64-bit) (no GPU support) Note: Installing Tensor. Flow 2 requires a newer version of pip .
What you can do is install Keras 2.3.1, which supports Tensor. Flow 2.x and 1.x, and is the latest real releases of Keras. You can also install Keras 2.2.4 which only supports Tensor, and flow 1x. You can install specific versions like this:.
How to use TensorFlow with Python?
Install the Python development environment on your system 2. Create a virtual environment (recommended) 3. Install the Tensor. Flow pip package tensorflow —Latest stable release with CPU and GPU support (Ubuntu and Windows) tf-nightly —Preview build (unstable). Ubuntu and Windows include GPU support .
You may be asking “Is TensorFlow backwards compatible with Python?”
Only the public APIs of Tensor. Flow are backwards compatible across minor and patch versions. The public APIs consist of All the documented Python functions and classes in the tensorflow module and its submodules, except for.
What are the versioning requirements for TensorFlow?
Our versioning scheme has three requirements: Backward compatibility to support loading graphs and checkpoints created with older versions of Tensor, and flow. Forward compatibility to support scenarios where the producer of a graph or checkpoint is upgraded to a newer version of Tensor. Flow before the consumer.