Running TensorFlow in Clojure

TensorFlow is a library for running matrix computation very fast. This makes it perfect for data science and in particular, machine learning.

The power of TensorFlow comes from a bunch of optimised C code under the hood. TensorFlow can also be compiled to run on the GPU, which can allow for enormous speedups. This is important because the machine learning is becoming more reliant on big data and big networks which take a lot of computing power.

At the beginning of the year TensorFlow began work on a Java api, and that means Clojure gets one for free.

For now, the Java api is still in active development and is very sparse. But don’t let that stop you getting your hands dirty. The Java api already provides everything we need to work with TensorFlow. With just java interop and a couple of helper functions we can start writing idiomatic clojure code.

This post will cover:

How to install TensorFlow with lein

To use TensorFlow with lein just add [org.tensorflow/tensorflow "1.1.0-rc1"] to your dependencies in project.clj .

We can test our installation by firing up a repl and running the version method on the TensorFlow class.

( . org.tensorflow.TensorFlow version ) ;; => "1.x.x-rc2"

troubleshooting

If you get any errors first make sure your are running Java 8.

( System/getProperty "java.version" ) ;; => "1.8.0_101"

Your can force lein to use Java 8 by adding :java-cmd "/path/to/java" to your project.clj .

If you’re still getting errors after that follow the instructions here to build from source.

Thinking in TensorFlow

Before we get started with the actual code, there are a few concepts I need to explain otherwise none of this is going to make sense.

The main object of computation in TensorFlow is the tensor. A Tensor is just a typed multi-dimensional array. Nothing scary here.

When we write code for TensorFlow, we’re not actually running computations. Instead we’re composing a data structure which describes the flow of data. In TensorFlow this is called a graph. The graph will describe the flow of our data through a series of operations (ops for short). Nothing will actually be computed until we launch our graph in a session. The session handles the execution of our graph on the CPU or GPU and returns the resulting tensors.

In short, our clojure code will assemble a graph and fire off commands to the C code using a Session object.

Direct interop with the Java api

To get started I’m going to use only direct interop with the Java api. It ain’t pretty, but it should give you a sense for what is actually happening under the hood, and hopefully make it a bit easier to understand the abstractions we user later on.

First we need to initialise a new Graph object.

( def graph ( new Graph ))

Next we’re going to need some example tensors to work with. Because the computation isn’t running in clojure we can’t just define our values. Instead we’re defining an operation node in the graph that generates a constant. First I’m creating a tensor object using the class’ .create method. Because we’re interopping with the Java class we first need to turn our clojure persistant vector into an array of 32bit Integers. Using the arrow macro for clarity, we call the .opBuilder method on our graph. The first argument is the binary operation which will be added to the graph. In this case its “Const” (which is short for constant (obviously)). This is one of a big set of possible binary ops which TensorFlow have implemented in native code and which we reference when building our graph. The second argument is a unique name for the operation. I went with “tensor-1” for simplicity, but “Joaquin Phoenix” would have also worked. The only requirement is that it is unique to the graph. Next we set the value and datatype attributes that are required for the Const operation. Finally we build our operation based on the attributes and use the output method to return it. It is this returned operation that gets saved in clojure.

( def tensor-1 ( let [ tensor ( Tensor/create ( int-array [ 360 909 216 108 777 132 256 174 999 228 324 800 264 ] ))] ( -> graph ( .opBuilder "Const" "tensor-1" ) ( .setAttr "dtype" ( .dataType tensor )) ( .setAttr "value" tensor ) .build ( .output 0 )))) ( def tensor-2 ( let [ tensor ( Tensor/create ( int-array [ 5 9 2 1 7 3 8 2 9 2 3 8 8 ]))] ( -> graph ( .opBuilder "Const" "tensor-2" ) ( .setAttr "dtype" ( .dataType tensor )) ( .setAttr "value" tensor ) .build ( .output 0 ))))

Now lets add a more exciting operation to our graph. Again we will call the .opBuilder method on our graph object. I’m going to use the “Div” (division) operation this time. Next we call the .addInput method to add our two example tensors as input to the operation. Again we build and output our operation object, saving it as “divide” in clojure land.

( def divide ( -> ( .opBuilder graph "Div" "my-dividing-operation" ) ( .addInput tensor-1 ) ( .addInput tensor-2 ) .build ( .output 0 ) ))

To run our newly built operations, we need to create a session object based on our graph.

( def session ( new Session graph ))

We’ll call the .runner method on our session to get the engine running. We use the .fetch method to retrieve the divide operation by name; in this case we want to pass it the name we gave to the divide operation just before (“my-dividing-operation”). The .get method gets our result from the returned array, this gives us a Tensor object which has all the data but cannot be read easily, so finally to read our results, we call the .copyTo method on the Tensor to copy the contents to an integer array.

( def result ( -> session .runner ( .fetch "my-dividing-operation" ) .run ( .get 0 ) ( .copyTo ( int-array 13 )) ))

Finally we can read our results.

( apply str ( map char result )) ;; => "Hello, World!"

Writing idiomatic Clojure for TensorFlow

So we have successfully run a basic TensorFlow graph, which is cool, but the code made my eyes bleed. This is partially because the TensorFlow Java api is so new and doesn’t have the multitudes of helper functions that python has yet. But I think the main reason is that writing methods isn’t why I came to clojure.

TensorFlow’s Java api is still extremely barebones, but it already provides everything that we need to do useful things.

Better yet, when we’re writing for TensorFlow we’re really only building operations and running them; thus we really only need a couple of helper function to cover all bases. We can get by without an api altogether.

Lets actually do some machine learning. For simplicity’s sake, I’m going to write a very shallow neural network. From here on, I’m going to start using a very light layer on top of the interop that I defined in helpers.clj.

First, we’ll need some training data.

( def training-data ;; input => output [ [ 0 . 0 . 1 . ] [ 0 . ] [ 0 . 1 . 1 . ] [ 1 . ] [ 1 . 1 . 1 . ] [ 1 . ] [ 1 . 0 . 1 . ] [ 0 . ] ])

We can split out training data into inputs and outputs like so. Note the use of tf/constant . This simply wraps the operationBuilder and takes care of adding the Const operation to the default graph.

( def inputs ( tf/constant ( take-nth 2 training-data ))) ( def outputs ( tf/constant ( take-nth 2 ( rest training-data ))))

We want to initialise our weights as a random value between -1 and 1. Because training our network means changing the state of our weights, we use tf/variable which creates a variable node on the graph.

( def weights ( tf/variable ( repeatedly 3 ( fn [] ( repeatedly 1 # ( dec ( rand 2 )))))))

Even though we’re defining nodes for the TensorFlow graph, we can still define our flow with functions. This is great because it feels just like we’re writing clojure code.

( defn network [ x ] ( tf/sigmoid ( tf/matmul x weights )))

For our network to learn we need to measure the difference between the training outputs and our network’s outputs. Most of the complexity here comes from the neural network itself so if the next few functions don’t make sense don’t worry. For an explanation of basic neural network code checkout this post.

( defn error [ network-output ] ( tf/div ( tf/pow ( tf/sub outputs network-output ) ( tf/constant 2 . )) ( tf/constant 2 . )))

For back-propagation, we need use derivative of our error and sigmoid functions. Note here, the use of tf/assign to set the variable weights to their new value. Also notice how we can abstract our TensorFlow operations just like clojure code, so that all the complexity of derivatives and deltas is wrapped up in the train-network operation.

( defn error ' [ network-output ] ( tf/sub network-output outputs )) ( defn sigmoid ' [ x ] ( tf/mult x ( tf/sub ( tf/constant 1 . ) x ))) ( defn deltas [ network-output ] ( tf/matmul ( tf/transpose inputs ) ( tf/mult ( error ' ( network inputs )) ( sigmoid ' ( network inputs ))))) ( def train-network ( tf/assign weights ( tf/sub weights ( deltas ( network inputs )))))

So far we seem to have used a whole bunch of functions to build our operations. But really we’ve only been using one. The function op-builder which is defined in helpers.clj simply wraps up all the messy object-oriented code from the Java api to add operations to the graph. All the other tf scoped functions we have used, just pass arguments to op-builder . This is why we can safely wrap so much functionality without worrying that the Java api will change on us.

Running our Operations

The other thing that our helpers.clj file defines is a couple of functions to make running operations a bit easier.

The tf/session-run helper function takes care of setting up a session and running a list of operations. tf/session-run returns the results of the last operation in the list. In this case the it will return the results of the network without training.

( tf/session-run [( tf/global-variables-initializer ) ( network inputs )])

Note also the use of tf/global-variables-initializer . This is needed when we are using one or more variables in our graph. There are other ways of approaching the variable initialisation problem for TensorFlow graphs, but for now I’ve just gone with the standard solution from the python TensorFlow api. Despite the “global” in the function name the variable initialisation is scoped to the tf/session-run function and won’t affect other sessions. You can think of it like a let function.

The pattern above is great for testing small parts of your graph or a couple of operations here and there. But when we train our network we want its trained weights to be preserved so we can actually use the trained network to get shit done.

For this we want to create a session object that we can hold on to.

( def sess ( tf/session ))

We can make a partial of the session-run function to get the best of both worlds.

( def sess-run ( partial tf/session-run tf/default-graph sess ))

Now we can break up our operations steps into logical breaks initialise variables and run the untrained network

( sess-run [( tf/initialise-global-variables )])

Run the train-network operation 10000 times and then check the error.

( sess-run [( repeat 10000 train-network ) ( tf/mean ( error ( network inputs )))])

Run the network on a new example

( sess-run [( network ( tf/constant [[ 1 . 1 . 1 . ]]))]) ;; => [[0.99740285]]

And that’s about it. We’ve converted our eyesore object-oriented interop code to something perfectly readable with just a couple of functions. The code base is tiny enough to allow immediate changes if the Java api changes on us and the system is flexible enough that we don’t need to wait for the Java api to get fleshed out to jump in and get our hands dirty.

Final Thoughts

Machine learning is shaping up to be the most important class of algorithms of this decade. It’s important that Clojure has a good story for ML if we want it to be around for a long time. Cortex is definitely making important progress on this front and as it approaches its 1.0.0 release I fully expect it to become Clojure’s go to library for this sort of thing. But TensorFlow is the biggest name in ML, and with Google’s backing that isn’t going to change anytime soon. The fact that we can so easily extend Java’s api and write clearer code with than in Java itself (don’t believe me? read the example code) is a pretty incredible display of what makes Clojure a great language to write.

***

The full code for this post is available here.

I’ve made a library to contain the helper functions used in this post. This is not intended to become a TensorFlow api for clojure and will remain as a very light layer over the interop. If you would like to use or contribute, you can find it here.

← Back to Posts