The documentation indicate that tensorflow has a svd operator.
However, I can't find it in my tensorflow with pip for python2.7
I used these command on Mac:
pip install https://storage.googleapis.com/tensorflow/mac/tensorflow-0.8.0-py2-none-any.whl
Any suggestion would be appreciated.
You are trying to install tensorflow 0.8. The SVD was introduced in tensorflow 0.10. Try installing a more recent version. (Current version is 1.2).
Related
I am working on a jupyter notebook script which I used last year to train a neural network.
When I try to import the keras tokenizer:
from keras.preprocessing.text import Tokenizer
I receive this error
I have seen other posts which suggest that I need to update tensorflow. My anaconda environment tells me I have 1.13.1 installed. But when I try to update tensorflow-base to 1.15 in the anaconda navigator, I receive this error:
I can update tensorflow from my command line using:
conda install tensorflow=1.15.0
But this doesn't update tensorflow in my anaconda environment and the error persists in my notebook.
Any help would be much appreciated! As you can probably tell, I am a novice python user.
The error says some packages needed to update Tensorflow/Andaconda requires Python 3.11 or newer. Since not all of the error log can be seen however, I would upgrade to python 3.7 to be safe. You can download this from the official page: https://www.python.org/downloads/
If the problem persists, try using pip to update the packages(In bash) :
pip install tensorflow
pip install conda
If you get an error while using pip, try:
pip3 install tensorflow
pip3 install conda
This same method can be used to update keras:
pip install keras
or if that does not work:
pip3 install keras
If pip is not recognized at a command, Python 3.7 is not added to path. I do not have experience with macOS, but this article should go into enough depth.
https://realpython.com/add-python-to-path/#how-to-add-python-to-path-on-linux-and-macos
I am using Anaconda and Ubuntu 18.04. I installed TensorFlow probability base on Anaconda's command:
conda install -c conda-forge tensorflow-probability
but when I wanted to import TensorFlow probability, I faced with below error:
ImportError: cannot import name 'compiler' from 'tensorflow.python.autograph.pyct' (/home/alireza/anaconda3/envs/tf-gpu/lib/python3.8/site-packages/tensorflow/python/autograph/pyct/init.py)
I check Anaconda by conda list tensorflow and TensorFlow probability version 0.8 was installed.
I appreciate your suggestion.
I think it's been a long time since the conda recipe for TFP was updated (TFP is now at 0.12), so you would need to ensure the version of TensorFlow you have is compatible. FWIW from the 0.8 release notes: "It is tested and stable against TensorFlow version 2.0.0 and 1.15.0rc1."
First of all, I apologize that my English is not good for you to understand.
Currently, I am doing computer vision using tensorflow version 1.14. In the process, the following problem ocurred in the process of rotating the model using GPU.
AttributeError: module 'tensorflow._api.v1.config' has no attribute 'set_visible_devices'
The current development environment is as follows.
Python: 3.7.9
conda: 4.8.3
tensorflow: 1.14.0
keras: 2.3.1
In addition, I currently have 4 gpu, and i want to use 2 gpu as if it were 1 gpu. Can you give me a good idea for this?
thank you.
It seems you need to upgrade the tensorflow because tf.config.set_visible_devices() function is available in latest version of tensorflow, you can use below code to upgrade the tensorflow:
!pip install --upgrade pip.
!pip install --upgrade tensorflow
You can follow the link to install the CPU/GPU version of tensorflow as per requirement and for tf.config.set_visible_devices() function related details, check here
I want to ask about compatibility issues between keras and tensorflow.
Specifically, how can I find the latest compatible version of keras and tensorflow? I found a lot of places including the official website of keras and did not find how to find the tensorflow version compatible with the latest keras2.24.
My current version of tensorflow+keras is 1.4.0+2.1.0, which does not support some new features.
If you don't care about having the latest Keras version, you can use the tf.Keras module that is already available with your Tensorflow installation. Check here
As per the doc:
tf.keras can run any Keras-compatible code, but keep in mind:
The tf.keras version in the latest TensorFlow release might not be the
same as the latest keras version from PyPI. Check tf.keras.version.
When saving a model's weights, tf.keras defaults to the checkpoint
format. Pass save_format='h5' to use HDF5.
I have tensorflow 1.12.0 with keras 2.2.4. I got this by downloading the latest (Anaconda3-2018.12-Windows-x86_64.exe) anaconda which has conda 4.6.4 and python 3.6.8.
I believe I then did a conda update conda and a conda update anaconda.
I then did a conda install tensorflow and a conda install keras.
Activate your environment and update your anaconda version using
conda update anaconda
Next uninstall tensorflow and keras as below
pip uninstall keras
pip uninstall tensorflow
Install Keras and tensorflow again
pip install tensorflow
pip install keras
check the version is updated.If it is not possible comment below.
I am using pip3 install tensorflow==1.8.0, but it doesn't have GPU support.
So I am using pip3 install tensorflow-gpu==1.8.0, but it still raises an exception
libcudart.so.VERSION No such file.
Should I use colab to install tensorflow from source?
After pip3 list:
tensorboard 1.10.0
tensorflow 1.10.0
tensorflow-hub 0.1.1
Google recommends you not to do pip installs!!!!
use this instead: %tensorflow_version 1.x
Restart the Runtime and check if its changed:
import tensorflow
print(tensorflow.__version__)
Here is a link to the main article:
https://colab.research.google.com/notebooks/tensorflow_version.ipynb#scrollTo=8UvRkm1JGUrk
You can downgrade Tensorflow to a previous version without GPU support on Google Colab. I ran:
!pip install tensorflow==1.14.0
import tensorflow as tf
print(tf.__version__)
which initially returned
2.0.0-dev20190130
but when I returned to it after a few hours, I got the version I requested:
1.14.0
Trying to downgrade to a version with GPU support:
!pip install tensorflow-gpu==1.14.0
requires restarting the runtime and fails, as importing import tensorflow as tf returns:
ImportError: libcublas.so.9.0: cannot open shared object file: No such file or directory
Update
When the import fails you can always downgrade CUDA to version 9.0 using following commands
!wget https://developer.nvidia.com/compute/cuda/9.0/Prod/local_installers/cuda-repo-ubuntu1604-9-0-local_9.0.176-1_amd64-deb
!dpkg -i cuda-repo-ubuntu1604-9-0-local_9.0.176-1_amd64-deb
!apt-key add /var/cuda-repo-9-0-local/7fa2af80.pub
!apt-get update
!apt-get install cuda=9.0.176-1
You can check the version of CUDA by running:
!nvcc --version
Second update
This code now seems to fail, see the follow-up question at How to downgrade to tensorflow-gpu version 1.12 in google colab
Google gives quite a simple solution to downgrade to the previously used Colab tf v.1.15.2. Just run the following magic line in Colab:
%tensorflow_version 1.x
Ther recommend "against using pip install to specify a particular TensorFlow version for both GPU and TPU backends. Colab builds TensorFlow from the source to ensure compatibility with our fleet of accelerators. Versions of TensorFlow fetched from PyPI by pip may suffer from performance problems or may not work at all". This means if you need GPU support, use one of the two given TF versions. The other versions will not necessary work I guess even for CPU.
The build process for GPU-enabled tensorflow is involved. In particular, old versions of TensorFlow use (or require) older versions of CUDA, which itself depends on system libraries and configuration beyond the scope of a pip install.
I suspect that downgrading TensorFlow on a VM configured for a newer version is going to be an involved process, perhaps involving downgrades / reinstalls of system libraries.
If it's practical, it might be simpler to update your code to use the latest version of TensorFlow, at least until Colab supports persistent backend enivronments.
It seems that only tensorflow 2 is supported by Colab, but that's not true, you still can use pip to uninstall tensorflow 2 and install a specific version of tf1. !yes|pip uninstall tensorflow, !pip install tensorflow==1.15.5 Maybe you should install other dependencies. So use !pip install -r requirements.txt Attention! You must restart the runtime in order to use newly installed versions.
%tensorflow_version 1.x no longer works.
%tensorflow_version 1.x
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-2-8d2919c1d33c> in <module>
----> 1 get_ipython().run_line_magic('tensorflow_version', '1.x')
1 frames
/usr/local/lib/python3.8/dist-packages/google/colab/_tensorflow_magics.py in _tensorflow_version(line)
33
34 if line.startswith("1"):
---> 35 raise ValueError(
36 # pylint: disable=line-too-long
37 textwrap.dedent("""\
ValueError: Tensorflow 1 is unsupported in Colab.
Your notebook should be updated to use Tensorflow 2.
See the guide at https://www.tensorflow.org/guide/migrate#migrate-from-tensorflow-1x-to-tensorflow-2.