Category: Flatiron

A few weeks ago I got started with Tensorflow and covered Tensors and operations. This week I’m going to continue to cover the basic building blocks of Tensorflow and then go over an interactive example that incorporates these elements.

Tensors – These are basically shaped collections of numbers. They can be multi-dimensional (array of arrays) or a single value. Tensors are all immutable which means they cant be changed once created and require manual disposal to avoid memory leaks in your application.

// 2x3 Tensor
const shape = [2, 3]; // 2 rows, 3 columns
const a = tf.tensor([1.0, 2.0, 3.0, 10.0, 20.0, 30.0], shape);
a.print(); // print Tensor values
// Output: [[1 , 2 , 3 ],
// [10, 20, 30]]

const c = tf.tensor2d([[1.0, 2.0, 3.0], [10.0, 20.0, 30.0]]);
c.print();
// Output: [[1 , 2 , 3 ],
// [10, 20, 30]]

Operations – An operation is just a mathematical function that can be used on a tensor. These include multiplication, addition, and subtraction.

const d = tf.tensor2d([[1.0, 2.0], [3.0, 4.0]]);
const d_squared = d.square();
d_squared.print();
// Output: [[1, 4 ],
// [9, 16]]

 

Models & Layers –  A model is a function that performs some set of operations on tensors to produce a desired output. These can be constructed using plain operations but there are also a lot of built in models with Tensorflow,js that rely on established learning and statistical methods.

// Define function
function predict(input) {
// y = a * x ^ 2 + b * x + c
// More on tf.tidy in the next section
return tf.tidy(() => {
const x = tf.scalar(input);

const ax2 = a.mul(x.square());
const bx = b.mul(x);
const y = ax2.add(bx).add(c);

return y;
});
}

// Define constants: y = 2x^2 + 4x + 8
const a = tf.scalar(2);
const b = tf.scalar(4);
const c = tf.scalar(8);

// Predict output for input of 2
const result = predict(2);
result.print() // Output: 24

 

Memory Management – Tensorflow.js uses the GPU on your computer to handle most of the operations which means that typical garbage collection isn’t available. Tensorflow therefore includes the tidy and dispose methods that allow you to dump unused tensors out of memory

// tf.tidy takes a function to tidy up after
const average = tf.tidy(() => {
// tf.tidy will clean up all the GPU memory used by tensors inside
// this function, other than the tensor that is returned.
//
// Even in a short sequence of operations like the one below, a number
// of intermediate tensors get created. So it is a good practice to
// put your math ops in a tidy!
const y = tf.tensor1d([1.0, 2.0, 3.0, 4.0]);
const z = tf.ones([4]);
return y.sub(z).square().mean();
});
average.print() // Output: 3.5

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In my previous blog post I discussed perceptrons, a very early example of machine learning. As a recap, perceptrons are simple learning algorithms that can solve linearly separable problems.

This is cool,  but not very useful. As far as I can tell most of the problems a perceptron can solve can be done much more quickly by passing your data through a well considered IF statement (I.e. If coffee mug is in photo then it is a photo of coffee). These days we can see all sorts of applications of machine learning that seem to solve much more complicated problems. Self driving cars are learning what a person looks like, can make assumptions about how they’ll move and can direct a car to respond based on this information. Much of this more advanced machine learning is through multilayer perceptrons, neural nets and other advanced methods.

One of the best way to get started working with these advance machine learning algorithms is through Google’s tensorflow library. This has been available as a python library for some time and was recently updated to include a Javascript library as well. In this post I’m going to cover how to quickly get this running and some basic concepts that you need to understand as you get started. Much of this material is covered in the getting started section on the tensorflow.js website as well.

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Machine learning(ML) refers to a collection of programming techniques that allow computers to make distinctions or recognize patterns without explicit commands. This field is based on statistical methods and emerged from artificial intelligence research in the late 1950s and early 1960s. Applications of ML include optical character recognition, sentiment analysis, computer vision and prediction making. People with experience in ML are highly desired in the job market and learning based algorithms are making more and more important decisions in our society. So as an emerging programer its probably worth while to learn a bit about how machines learn.

 

As an introduction to ML this post will walk through how to build a single layer perceptron in Ruby. The perceptron was one of the first functional ML algorithms. It was developed by Frank Rosenblatt in 1957 and was used to build a machine that could identify certain objects. At the time Rosenblatt stated that the “perceptron [is] “the embryo of an electronic computer that [the Navy] expects will be able to walk, talk, see, write, reproduce itself and be conscious of its existence.”

I am far from an expert in this field but luckily perceptrons are relatively straight forward models to build. I have seen them written in python, Java, and javascript but had a hard time finding a ruby version. Attempting to build this out in ruby seemed like a decent contribution that I could make.

Using a common biological analogy, a perceptron is like a single neuron in a larger brain. It is designed to take in inputs and based on those inputs generate an output for other neurons.

 

 

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