Beginning Machine Learning: The "Hello World" of Tensor Flow

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This tutorial is intended for readers who are new to both machine learning and TensorFlow. If you already know what MNIST is, and what softmax (multinomial logistic) regression is, you might prefer this faster paced tutorial. Be sure to install TensorFlow before starting either tutorial.

When one learns how to program, there's a tradition that the first thing you do is print "Hello World." Just like programming has Hello World, machine learning has MNIST.

MNIST is a simple computer vision dataset. It consists of images of handwritten digits like these:

5041 image sample

It also includes labels for each image, telling us which digit it is. For example, the labels for the above images are 5, 0, 4, and 1.

In this tutorial, we're going to train a model to look at images and predict what digits they are. Our goal isn't to train a really elaborate model that achieves state-of-the-art performance — although we'll give you code to do that later! — but rather to dip a toe into using TensorFlow. As such, we're going to start with a very simple model, called a Softmax Regression.

The actual code for this tutorial is very short, and all the interesting stuff happens in just three lines. However, it is very important to understand the ideas behind it: both how TensorFlow works and the core machine learning concepts. Because of this, we are going to very carefully work through the code.

About this tutorial

This tutorial is an explanation, line by line, of what is happening in the code.

You can use this tutorial in a few different ways, including:

  • Copy and paste each code snippet, line by line, into a Python environment as you read through the explanations of each line.
  • Run the entire Python file either before or after reading through the explanations, and use this tutorial to understand the lines of code that aren't clear to you.

What we will accomplish in this tutorial:

  • Learn about the MNIST data and softmax regressions
  • Create a function that is a model for recognizing digits, based on looking at every pixel in the image
  • Use TensorFlow to train the model to recognize digits by having it "look" at thousands of examples (and run our first TensorFlow session to do so)
  • Check the model's accuracy with our test data

The MNIST Data

The MNIST data is hosted on Yann LeCun's website. If you are copying and pasting in the code from this tutorial, start here with these two lines of code which will download and read in the data automatically:

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

The MNIST data is split into three parts: 55,000 data points of training data (mnist.train), 10,000 points of test data (mnist.test), and 5,000 points of validation data (mnist.validation). This split is very important: it's essential in machine learning that we have separate data which we don't learn from so that we can make sure that what we've learned actually generalizes!

As mentioned earlier, every MNIST data point has two parts: an image of a handwritten digit and a corresponding label. We'll call the images "x" and the labels "y". Both the training set and test set contain images and their corresponding labels; for example the training images are mnist.train.images and the training labels are mnist.train.labels.

Each image is 28 pixels by 28 pixels. We can interpret this as a big array of numbers:

MNIST matrix

We can flatten this array into a vector of 28x28 = 784 numbers. It doesn't matter how we flatten the array, as long as we're consistent between images. From this perspective, the MNIST images are just a bunch of points in a 784-dimensional vector space, with a very rich structure (warning: computationally intensive visualizations).

Flattening the data throws away information about the 2D structure of the image. Isn't that bad? Well, the best computer vision methods do exploit this structure, and we will in later tutorials. But the simple method we will be using here, a softmax regression (defined below), won't.

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