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Deep Learning on Windows, Redux

Why the Rewrite?

When I wrote my initial Deep Learning on Windows guide, it was a year ago - TensorFlow was just starting to surface and was far from running on Windows, CNTK didn't run in Python, and Theano was the current king of the heap. Seems like a lifetime ago in the Deep Learning world, and the whole universe of DL has changed since then. For one, it's dramatically easier to get up and running, and my example is woefully out of date. It's time for an update.

A Quick Guide to Deep Learning

Deep Learning is a newly-popular set of techniques in the field of Machine Learning that have shown the power to generalize well over a whole host of problems, and even solve some problems that were considered unsolvable just a few years before. There are many tutorials and write-ups on these techniques online, so I won't go too deep, but I'll lay out the basics.

People have known for some time that a particular Machine Learning technique - Neural Networks - has the capability to learn complex mappings from inputs (e.g. images) to outputs (e.g. "which digit is this?") through the use of a "hidden layer" that mapped between the two.

A Simple MLP

With the advent of Big Data, we suddenly have incredibly large sets of input data we could feed to these models, and with the increasing power of GPUs we have the compute capacity to do so. We had seen Neural Networks with multiple hidden layers in the past, but training them was so expensive and they didn't seem to outperform other techniques, so they'd been discarded. Someone realized that with the amount of data we now have and the compute resources available to us, these constraints no longer applied and these older networks were brought back.

Denser, Deeper Neural Network

Since then, networks have only gotten deeper (more hidden layers) and their shapes have only gotten stranger (convolutional neurons, feedback loops, and layer skipping). At this point we know they are powerful, but we don't really know how powerful they can get and the field is wide open for advancement. For a more detailed and far more eloquent history of the field, see NVidia's great post.

In this post I'll walk you through how to get one of the most popular toolkits up and running on Windows, and run through and explain some fun examples. Speaking of NVidia, this post assumes you have an NVidia GPU which will make your neural networks train MUCH faster.

Deep Learning Toolkits

Machine Learning has been around for a long time and there are dozens of frameworks out there written in everything from low-level C code to AzureML. In the Deep Learning space several frameworks have risen to prominence only to gradually lose ground to the "next big thing". It's hard to say who has the best framework, and a lot of it right now comes down to choices about whether it supports your current and expected needs, whether it runs on your platform and whether you can code to it in a language you enjoy. TensorFlow seems to be the current king of the heap and has a lot of mind-share behind it, but PyTorch is coming up as a strong contender and of course Microsoft's Cognitive Toolkit is now a player with v2's support for Python.

Caffe is one of the elders of the field, and with their "Model Zoo" of pre-trained models makes a compelling case for continued usefulness. With plenty of tutorials, good documentation, and a binding for Python it's a solid choice. It was supplanted by Torch, Facebook's framework for Lua. This was popular for a while until it started being replaced by more modern Tensor-based variants. These Tensor-based networks allow for networks of computations instead of networks of layers and have proven to be more flexible for modern deep learning models. Since this is all just math, both of these have been revised to more gracefully support computation networks at this point, and with PyTorch and Caffe2 both of these frameworks have a new lease on life.

Microsoft's Cognitive Toolkit and Google's TensorFlow are both Tensor-based systems - both run on Linux and Windows. Keras is a wrapper around existing Tensor toolkits that allows for easier model building, training, and evaluation - it easily runs atop TensorFlow and CNTK, as well as an older alternate I used in the previous version of this article (Theano).

Keras, TensorFlow, and CNTK

Installing on Windows

Keras, TensorFlow and CNTK all (can) run as Python libraries, so the first thing we'll need to do is get a Python installation on Windows. For this, there are a couple of great options out there, but I'll go with Anaconda - this differs from my previous post where I used WinPython. Why did I change? Primarily because I'm now on so many different projects that I need to maintain a large number of environments sometimes using different Python versions, and I find that Anaconda makes that easier. My point about WinPython having a side-effect-free installation, however, still holds true.

Installing Anaconda, Using Conda

Navigate to the Anaconda download page and download the latest 64-bit installer. Once you've installed it into your location of choice, start up a new command window and we'll be ready to install the rest of the tools. First, though, you should take a brief detour to learn about conda - we'll be using conda to manage our environment, consider it like a virtualenv++. If you already have Anaconda installed, consider trying conda update conda and potentially conda update python to make sure you're up to date.

You should create an environment to isolate your baseline Python installation from any packages you install - this is both good practice for maintaining your installation, and for allowing you to document it and replicate it elsewhere (e.g. Docker, other VMs). To do so, pick a name (e.g. "deep-windows") and create a new environment using conda create -n deep-windows (or whatever name you've chosen). You can then activate it using activate deep-windows.

Adding the Baseline

Within your new environment we need to install the modern Deep Learning toolkits we'll be using, but first let's install several packages that we'll commonly need. Anaconda's repos tend to be a bit out of date, but they contain MKL-optimized versions of several packages that are useful. As such, I typically install those using conda install and then use pip install for all others. We'll be installing Numpy, Scipy, Pandas, Scikit-Learn, and Jupyter Notebooks. We'll need h5py because H5 is a common format for serialized models (including the Keras model we will be using below), and PIL (well, Pillow) for processing images. We should also consider installing Bokeh and/or Dash for better charting.

> conda install numpy scipy
> pip install pandas scikit-learn jupyter h5py Pillow
> pip install bokeh dash

But First, a Word From Our GPU Overlords

We could install and run our Deep Learning tools right now, but as soon as you tried to train any model of complexity you'd grow old before it completed. For any reasonable training time, you'll need GPU support. I mentioned in the beginning that you should have an NVidia GPU for training, so let's set it up for Deep Learning Toolkit support. Head to the NVidia CUDA Toolkit home and download and install it. Then hit up the NVidia Deep Learning center and sign up for a developer account. Download the CUDnn library and install it - currently TensorFlow wants CUDnn v5.1 with CUDA Toolkit v8.0, but that changes fast so click through and choose appropriately.

NOTE: Once you install the CUDA Toolkit and CUDnn, you will likely need to restart your console and reactivate your environment to refresh your paths.

Adding the Latest Versions of CNTK, TensorFlow, and Keras

Now that your environment has a solid baseline to build upon, we'll install the latest releases of some of the modern Deep Learning toolkits I mentioned above. CNTK installs via Wheel files which you can find on their installation page (Note that if you want to use CNTK with BrainScript and/or the command line, you'll want the full install instead). TensorFlow installs via Pip and we'll be using the GPU version. Keras installs via Pip as well - it used to depend on Theano (see my previous post) and as of this writing it still winds up installing it, so expect some compilation here. If anything fails, this is likely where it'll go wrong.

> # Choose the install wheel from 
> pip install
> pip install tensorflow-gpu
> pip install keras

Testing Your Installation

With the latest CNTK, TensorFlow and Keras installed, ensure you can import the libraries:

> python
>>> import cntk
>>> import tensorflow
>>> import keras

Deep Dreaming

Deep Dream has evolved some since my previous version of this post, so let's go straight to the (Keras) source, grab their example, and use that as our baseline. The Keras version now uses Inception V3 as opposed to the previous version using VGG16 - let's grab it and run it on our example image:


Download the example from the Keras GitHub repo and run it on our example image. Assume you've downloaded the image above (the large version), you should be able to run the below and take a look at the result.

> wget
> python ninjacat_large.png ninjadream
> explorer ninjadream.png

And this would turn



Ninjacat, dreamy edition

NOTE: For those of you who don't have wget, might I recommend installing Gnu tools for Windows from GOW (and potentially checking out my earlier post on Windows tools).

Neural Artistry

Neural Artistry first surfaced with a paper from Germany and has since become another big showcase example for the power of Deep Learning. Essentially, the way it works is to take an existing trained Convolutional Neural Network and use it to convolve two images together, by joining the outputs of different convolutional layers from each image. The Keras example (as of now) still uses VGG16 for its network, so download the script and we'll use it to merge our favorite NinjaCat with a lesser known Norwegian work.

> wget -O the_scream.jpg
> wget
> python ninjacat_large.png the_scream.jpg ninjascream

And that'd turn



Ninjacat, screamy edition

Conclusion and Future Work

Setting up deep learning toolkits on Windows has become substantially easier in just the short time since I wrote my initial version of this article. TensorFlow now runs on Windows (finally), CNTK v2 works with Python and installs much more easily, and Keras runs against both TensorFlow (its new default) and CNTK. I'll write a follow-up to this with information on using Keras to switch between CNTK and TensorFlow, and how to use Docker containers to manage your Deep Learning training.

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