Tuesday, October 25, 2016

Getting Keras working with Anaconda Python

I've started using the Anaconda Python distribution for most of my Machine Learning. It has pre-built binaries of Python for many platforms and architectures, has hundreds of pre-built and tested Python packages directly available through the conda package manager, and it allows easy creation of virtual isolated environments - with its own Python version and packages - to experiment with.

You can get an idea of the capabilities of Anaconda by going through their Anaconda Test Drive guide.

Getting Keras (with Theano backend) working on any Python distribution is usually straightforward, but you do run into some errors occasionally based on the platform you're on and your environment settings.

So, here are the steps that worked for me to get Keras working on the Anaconda Python distribution:

First, you need to install Anaconda. It's as easy as getting the binary for your platform from Anaconda download page and running it. Once it's installed, the conda command will be available from your terminal or command prompt.

Now you can create an anaconda environment to install Keras and related packages,
 conda create --name keras-test numpy scipy scikit-learn pillow h5py mingw libpython  

'keras-test' is the name of the environment we're creating. You can give it a different name.
You can also create an environment with a different Python version. For example, if you want to create the environment with Python 2.7,
 conda create --name keras-test python=2.7 numpy scipy scikit-learn pillow h5py mingw libpython  

Once the environment is created, activate it.
 activate keras-test  

Then, we'll install Theano from Git, since we want the latest development version,
 pip install --upgrade --no-deps git+git://github.com/Theano/Theano.git  

And then, we install Keras from PIP,
 pip install keras  

Finally, we setup OpenBLAS and configure Theano to use it. My earlier blog post - Getting Theano working with OpenBLAS on Windows - details how to setup Theano with OpenBLAS in detail.

We can test whether the setup was successful by running the Python interpreter and importing Keras package,
 >>> import keras  
 Using Theano backend.  

Keras loading successfully
Keras loading successfully

If you don't get any errors when the Keras package is loading, then all is set.

Related posts:
Switching between TensorFlow and Theano on Keras
What is the image_dim_ordering parameter in Keras, and why is it important

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