To play around with tensor flow I used vagrant. The first thing that happend was a bug due to an older version of virtualbox; which didn’t solve itself as I needed to upgrade my vagrant installation…
30 minutes later, vagrant up finally works, and another 30 minutes later everything is ready and installed.
We can then login via ssh:
Host: 127.0.0.1
Port: 2222
Username: vagrant
Private key: C:/Users/XXXXX/.vagrant.d/insecure_private_key
Now you can run through the tensorflow examples! I would recommend the tutorials located here. Also check out the tensoflow site to upgrade to the latest version. At the time of writing you could upgrade using:
sudo pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.6.0-cp27-none-linux_x86_64.whl
We can give skflow a try which tries to mimic Scikit learn. To install this follow the instructions:
sudo pip install git+git://github.com/google/skflow.git
Which should also install dependencies like sklearn (this might have to be done before installing skflow. This can be done using:
sudo apt-get install build-essential python-dev python-numpy python-setuptools python-scipy libatlas-dev libatlas3-base
sudo apt-get install python-matplotlib
sudo apt-get install python-sklearn
We can create a simple classifier example using Iris dataset by following the examples on the website:
import skflow
from sklearn import datasets, metrics
iris = datasets.load_iris()
classifier = skflow.TensorFlowClassifier(n_classes=3)
classifier.fit(iris.data, iris.target)
score = metrics.accuracy_score(classifier.predict(iris.data), iris.target)
print "Accuracy:", score
For some reason I couldn’t get the deep neural net working; I will check it out a bit later!
Update
I have set up a repository with my own vagrant instance. You can check it out here: