How would you run libsvm on windows, How to get after training and predicting the error rate?
Regarding installation on Windows, I quote from the libsvm website: "For MS Windows users, there is a subdirectory in the zip file containing binary executable files."
As far as how to use SVM, take a look at their guide.
Related
I am getting this error while I trying to import libraries for Yolo implementation for the practical implementation given in this article https://www.analyticsvidhya.com/blog/2018/12/practical-guide-object-detection-yolo-framewor-python/
If you are using Darknet
In the case of windows you have to Manually build if you want gpu support
in Linux or mac you just have to run make file present in darknet
As I can guess your issue could be because of this make file
I have created a working Python script containing a Tensorflow model that can identify images. I would like to compile this script in to some form of .exe file that can be used on computers without Python and Tensorflow installed. I would appreciate any help in this regard. Which programs and versions to use, how to use them and may be some code lines to guide me.
I have without luck tried py2exe, pyinstaller and cx_freeze. Currently I am using Tensorflow 2.0 and Python 3.7.0.
Thanks in advance.
I know you said you didnt have any luck with cx_Freeze but give it a try
I made a guide here answering someone elses question
Doing a course in Machine Learning and can't get Tensorboard to work. I have saved runs from running a DQN and I write:
tensorboard -logdir runs
With the folliwng result:
2019-12-28 18:32:04.265065: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
TensorBoard 1.7.0 at http://david-linux:6006 (Press CTRL+C to quit)
So I click the link and get:
No dashboards are active for the current data set.
Probable causes:
You haven’t written any data to your event files
TensorBoard can’t find your event files.
I also get this result after having the code running for a while:
"W1228 18:34:34.186506 Thread-2 application.py:272] path /[[_dataImageSrc]] not found, sending 404
W1228 18:34:34.205581 Thread-2 application.py:272] path /[[_imageURL]] not found, sending 404"
Running this on Linux using Anaconda Python version 3.6 because that is what the course book uses. Have no idea what the above errors means, quite new to coding in general and reinforment learning in particular.
It could be caused if the browser isn't updated. You could also try installing the latest version of Tensorboard:
pip uninstall tensorflow-tensorboard
pip install tensorboard
Also try using different browsers.
Can you just try going to http://localhost:6006 instead? It looks like your hostname is not one that actually resolves in DNS.
Environment info
Operating System: El Capitan, 10.11.1
I'm doing this tutorial: https://petewarden.com/2016/09/27/tensorflow-for-mobile-poets/
Trying to classify images using tensorflow on iOS app.
When I try to build my net using bazel:
bazel build tensorflow/examples/label_image:label_image
I get these errors:
https://gist.github.com/galharth/36b8f6eeb12f847ab120b2642083a732
From the related github issue https://github.com/tensorflow/tensorflow/issues/6487 I think we narrowed it down to a lack of resources on the virtual machine. Bazel tends to get flakey with only 2GB of RAM allocated to it.
Recently, tensorflow had add the distribute training module, what's the distribute pre-requirement? I mean the environment like this,
tensorflow >= 0.8 kubernates shared file system, gcloud?
And it had release the example code:
Is there any way to run tensorflow cluster example, when only have hdfs and without any shared file system, where will model file store in?
Each computer will need to have tensorflow installed, (and in my experience, they should all be the same version. I had a few issues mixing versions 8 and 9).
Once that is set up, each computer will need access to the code it is to run (main.py for example). We use an NFS to share this, but you could just as easily git pull on each machine to get the latest copy of your code.
Then you just need to start them up. We would just ssh to each machine in our most basic setup, but if you have a cluster like kubernates, then it may be different for you.
As for checkpoints, I believe only the chief worker writes to checkpoint files if that's what your last question was asking.
Let me know if you have further questions.