I am trying to import Silhouette Visualizer and always get some errors, I have already updated the version of python and pip and tried uninstalling and installing scikit-learn and nothing works. I am getting this error:
Thanks for checking out Yellowbrick!
This problem is occurring because scikit-learn recently changed their public/private API, so utils.safe_indexing is now called utils._safe_indexing. In order for the utility to work in Yellowbrick, we are working on a change to our usage of safe_indexing to support users with versions of scikit-learn >= 0.24 without deprecating support for users with versions of scikit-learn < 0.24. That change will be available in the next version of Yellowbrick; you can track progress on the issue here.
In the meantime, you can avoid the error by downgrading your version of scikit-learn to <0.24 (e.g. pip install scikit-learn==0.23)!
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I'm currently working on a program to play a game similar to atari-games. I'm using keras (python 3). I finished writing the code and I want to test it, and I have few questions about the process:
first of all, I have trouble importing tesnorflow for some reason. I've installed it using pip. I've made sure to created new env. before the installation (which finished successfully), but when I try to run my program it says:
ModuleNotFoundError: No module named 'tensorflow'
I also, tried to install the package from within pycharm, but then I get this error:
Could not find a version that satisfies the requirement tensorflow (from versions: )
No matching distribution found for tensorflow
I've checked program requirements (such as pip, python, virtualenv and setuptools versions) and everything seems up to date. perhaps someone could point out what else might be the problem?
Is there any other way I can test the performance of my program?
Thank you very much for your time and attention.
Anaconda is a complete time-saver. I suggest create an enviornment using Anaconda and install the tensorflow by conda install tensorflow If you would like to use the gpu version, conda automatically installs the CUDA and cudnn for you too.
I have just installed the stable version of TensorFlow 2.0 (released on October 1st 2019) in PyCharm.
The problem is that the keras package is unavailable.
The actual error is :
"cannot import name 'keras' from tensorflow"
I have installed via pip install tensorflow==2.0.0 the CPU version, and then uninstalled the CPU version and installed the GPU version , via pip install tensorflow-gpu==2.0.0.
Neither of the above worked versions of TensorFlow were working properly(could not import keras or other packages via from tensorflow.package_X import Y).
If I revert TensorFlow to version 2.0.0.b1, keras is available as a package (PyCharm recognises it) and everything runs smoothly.
Is there a way to solve this problem? Am I making a mistake in the installation process?
UPDATE --- Importing from the Python Console works and allows the imports without any error.
For PyCharm Users
For those who use PyCharm. Install future (EAP) release 2019.3 EAP build 193.3793.14 from here. With that, you will be able to use autocomplete for the current stable release of TensorFlow (i.e. 2.0). I have tried it and it works :).
For other IDEs
For users with other IDEs, this will be resolved only after the stable version is released, which is anyways the case now. But this might take some more time for a fix. See the comment here. I assume it will be wise to wait and keep using version 2.0.0.b1. On the other hand avoid imports from tensorflow_core if you do not want to refactor your code in the future.
Note: for autocomplete to work use import statement as below
import tensorflow.keras as tk
# this does not work for autocomplete
# from tensorflow import keras as tk
The autocomplete works for TensorFlow 2.0.0 on CPU version, but the autocomplete does not work for the GPU version.
SOLVED --- See the answers to this problem below.
SOLUTION 1 (best solution)
Is the accepted answer provided above. It works on EAP version, I tested it on several machines with Windows.
SOLUTION 2
Although PyCharm does not recognise the modules, running the .py file works. I still do not know if this is a problem of TensorFlow or PyCharm, but this is the solution that I have found, many people have run into this problem.
SOLUTION 3
Import the modules from tensorflow_core instead of tensorflow
Example: from tensorflow_core.python.keras.preprocessing.image import ImageDataGenerator
However, as mentioned by #Nagabhushan S N in the comment below and above in the accepted answer:
On the other hand avoid imports from tensorflow_core if you do not
want to refactor your code in the future.
I'm running a Windows computer with just a CPU (no GPU). When I run pip install tensorflow -vvv in order to see what pip is doing, it lists a lot of links, but for all of them, it says "Skipping link ... it is not compatible with this Python."
Does tensorflow support Python 3.6.4 on Windows? If so, what binary URL should I use to install it?
(I previously installed with this version due to reading this, but ran into this error without the DLL load failed message, so I'm wondering if there's a better version I should use.)
Also, I'm aware that Tensorflow says they support Python 3.x, but right now it hasn't been working for me.
You have probably installed Python 32bits, you need the 64bits version
I'm new to tensorflow and I'm having some problems with the installation. I searched through the official website, without any success. My computer runs on windows, with python version 3.4. None of the sources on the internet seemed to have any command lines for this specific case.
I would greatly appreciate your help:)
I'm pretty sure they added support for python 3.5 only,
But lately they added support for python 3.6 as well.
The only way i can see is that you would have to upgrade, I'm not such a pro with this but that's all i know because i had an import problem with tensorflow which i haven't been able to solve since
You can get the full instructions at Install TF on Windows
I hope you already installed python3 and pip3, if not follow
C:\> pip3 install --upgrade tensorflow
I have installed virtualenvwrapper in a 3.5.0b1 virtualenv, called setupenv, to be able to generate new python 3.5 test environments easily.
Looking over the list of installed packages, I did see argparse version 1.3.0 installed. This (latest) version of argparse has not been tested with 3.5.
Is this dangerous?
As far as I know 3.2+ comes with its own argparse. Could this install break other packages relying on argparse? Why is this installed at all?
This is probably not dangerous. If you run:
python3.5 -c "import argparse; print(argparse.__file__)"
, you can see that the arparse.py installed with the interpreter takes precedence over the superfluously installed argparse package.
A bit of digging (or using the pipdeptree package) will show you that stevedore is dependent on argparse. This is just sloppy programming (or disregard of possible bandwidth issues).
In a package's setup.py you can easily test if you are running python < 2.7 or 3.0 <= python < 3.2 and only install argparse for those cases.
I would just de-install argparse from your setupenv virtualenv (pip uninstall argparse -y), virtualenvwrapper is not affected by the removal in my experience.
This is actually a bug in stevedore, it uses the pbr package and that supports specification of the python version using environment markers
but stevedore is not using that. The irony is that the example for this in pbr is with argparse, by specifying in the requirements.txt:
argparse; python=='2.6'
A bug report against stevedore was filed, but although the fix was trivial, it was not implemented for several releases. Finally the issue was
set to won't fix, probably because dropped support for 2.6 removed the
need for argparse altogether.