This chapter of the guide includes many interactive elements that currently only run well in the Chrome browser. This chapter relies upon there being a camera that the browser can access. Note that the blocks described here create a second browser window for "training" the system. Snap! and training window are both in the same browser tab but only one is visible at any time.
In the previous chapters the new Snap! blocks used services that had been trained to recognise speech and images. In this chapter you do the training. This training is done by a deep learning system. Deep learning is a very successful set of machine learning techniques. It is called "deep" because there are many layers of simulated neurons that do the processing. Typically the lowest layers pay attention to tiny details but higher layers can use the output of lower layers that capture some aspect of the image such as texture or edges. Here is a very nice 5 minute video that introduces the big ideas behind machine learning:
In this chapter we focus upon "supervised learning" which means that during training labels for the input are provided. The machine learns to then choose among training labels when presented with a new image (or other kinds of input). Rarely is the system completely confident that its choice of label is correct so instead it computes "confidence scores". The scores are between 0 (certain it is not this label) to 1 (certain it is). The sum of the scores of all the labels always adds up to 1. You may prefer to display confidence scores as percentages by multiplying the scores by 100. For example, a .8 score for label X can be thought of as saying the system is "80% sure the new input should have label X".
Unlike the previous chapters that rely upon pre-trained models here you can experiment with machine learning. You can learn what kinds of inputs the system can easily tell apart and which ones confuse it. You can experiment to answer questions such as how many examples of each label are needed to recognise them reliably and how does recognition degrade as more labels are introduced.
The Train with image buckets ... block opens a tab where you can train the system to label what is in front of the camera. (If the tab is blocked by your browser read the troubleshooting guide.) The first argument is the list of labels. A "bucket" is the collection of all the images with the same label. The system will attempt to determine the confidence that an image matches the images in each bucket. If you are training it to distinguish between two things (e.g. cats and dogs) then it is a good idea to add a third bucket for "everything else". During training then produce images of lots of different things that aren't cats or dogs while pressing the button to train for the "everything else" bucket.
Here there are only two labels: "leaning to the left" and "leaning to the right". You can edit the list to instead recognise different facial expressions, different people's faces, objects held in front of the camera, and much more. When the training tab launches you'll see two buttons:
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The Train with image buckets ... block only needs the first argument telling it the bucket labels. It has three additional optional arguments:
When training the system try to provide a variety of images that match the label. If you are training "leaning left" then lean different amounts and at different distances to the camera. Then when training "leaning right" don't allow the system to learn some difference that shouldn't matter. For example, don't change the lighting or background between training different labels. When you aren't pressing any training buttons the training page keeps trying to recognise what is in front of the camera. You can determine if your training is good by leaning one way and then the other and see if the confidence scores changes as they should. If not try adding more images.
When you are ready to test the trained system return to the Snap! tab and click on the Current image label confidences block. Then click on the confidences variable to see its value. Change what is in front of the camera and try again. Note that when you return to the training tab it starts running and you can add more training samples. When you are finished with this close the training tab.
Here is a program that launches a training tab to determine whether your finger is pointing at the camera or to the right. While training move your hand around so it is trained to ignore where the hand is and just pay attention to which way the finger is pointing. When you return to Snap! the program will repeatedly check which way your finger is pointing. If it sees a finger pointing at the camera then the turtle goes forward. If not then it turns. It always displays the latest confidence scores.
Exercise. There are many ways of enhancing this program. Add a third or fourth label and use that label to control the turtle. For example, add the label of pointing left and change the program to turn left or right depending on which way the finger is pointing. Or add a gesture to stop moving.
A full screen version of the following program can be found here. Look at the script, it isn't very complicated considering what it does. There is a similar program where the sprite moves left or right depending upon which way you are leaning. Think of modifying it in interesting ways, e.g. a face whose eyes look left and right as you lean left and right. A web page where one can type by moving one's head (perhaps useful to those with physical disabilities) uses the same ideas as this.
Exercise. Explore how to make the training more accurate. How does the performance change as more examples are presented? Does it help to simplify the images (e.g. arrange so only your pointing hand is being captured by the camera)? If you train with one person and then replace with another does the system get confused? Does it help to train with both people?
In Snap! each sprite can have any number of "costumes". Costumes can be drawings, photos, or imported images.
Using the Train with all my costumes you can send all of a sprite's costumes to the training tab. This block is defined using the Add costume <i> to training ... block. You can also ask the training tab about label confidences of a costume using Label confidences of costume number .... You can experiment with these costume-based training blocks in the following.
While in a training window you will see a button Save your training. Clicking it save a file where you want.
To load saved training you can either load the file on the local file system or host the file on the web and use its URL to access it.
In the previous chapter many blocks were introduced that do image recognition. These rely upon AI cloud services from large companies. Here we instead rely upon a trained machine learning model named MobileNet. This model runs in your browser and does not contact any cloud services. While it will choose from among 1000 labels it rarely does as well as the cloud services. For contexts where registering for API keys is a problem, or Internet access is limited or missing, or privacy is a concern then models like MobileNet are useful. It does, however, rely upon your device having a GPU to run at reasonable speeds. Note this will be slow to load but should run quickly thereafter.
Most of the things image recognition is good for and its dangers were discussed in chapter 3. But maybe the situation is different if users do the training instead of professionals in large corporations. Will the biases be different? Does the smaller training set make the system less reliable?
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Using machine learning one can make a rock paper scissors game that can be played by putting your hand into one of three configurations. If the game below doesn't work visit this help.
This game uses the train with all my costumes ... block four times. Once for rocks, paper, and scissors and a fourth time for "other". Once the training is completed the game starts and the program picks a random move and compares it to what it thinks your move was based upon the analysis by the training tab of what is in front of the camera. You can start exploring and enhancing the game most easily by clicking on the "Open this in a new tab" block in the scripting area. This example was inspired by the great set of machine learning project examples at machine learning for kids
Much of the current success of machine learning system is a result of having trained them on huge data sets. Deep learning for machine translation, for example, didn't work well until they were trained with billions of sentences. So how could the examples in this chapter work with just dozens of examples for each label? Part of the answer is that big data is necessary for high accuracy. If you try the rock paper scissors game you'll see it sometimes makes mistakes. See if you can improve its accuracy by adding more training images.
The image training command relies upon a deep learning model called MobileNet that was trained on 1000 categories in 1.2 million images. The Snap! command uses what is called transfer learning to start the training using the features that a well-trained system has already learned. The system does not know anything about, for example, hands shaped as paper, rock, or scissors but it has already learned about textures, edges, colours, and more. Each training image used in transfer learning is converted by MobileNet into a list of one thousand numbers. The confidence scores for the labels is then computed using an algorithm called k-nearest neighbors. To classify an image, its distance to each of the training images is computed and the "k" closest is found. Those closest images "vote" for their label as the classification and the sums are used to compute the confidences.
Another difference is that the image recognition services in the previous chapter can distinguish between many thousands of different kinds of images and has been trained for a long time on many millions of images. The Rock paper scissors game requires only four categories - an easier task. And AI vision services can do more than just choose a label for an image.
Computers can be trained to distinguish different sounds including spoken speech. Chapter 3 describes how to use cloud services that do speech recognition. Here you can create programs that respond to any sound. Also you can train it to recognise a few different words or phrases that works locally on your computer without sending any audio to a cloud server. This is useful in contexts without a reliable network connection or for increased privacy. Furthermore, the Audio label confidences block has an option to use a "Pre-trained model of 20 words" on your device. It is capable of recognising 'zero', 'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine', 'up', 'down', 'left', 'right', 'go', 'stop', 'yes', and 'no'. This relies upon the speech command recognizer created by Google.
The Train with audio buckets... block uses a Tensorflow.js deep learning model which without a GPU runs too slow to be useful. For running on small computers such as a Raspberry Pi there is also the similar Train (without Tensorflow) with audio buckets... block and its accompanying Audio label confidences (without Tensorflow) block.
An example using audio training is to tell a sprite to go "forward", turn "right", or "stop". Note that these labels are for us humans, you can for example make any sound or speak in another language and the program should work just fine.
Google Creative Lab recently released browser-based software called Posenet for real-time human pose estimation. This software can determine the locations of 17 different body parts: noses, eyes, ears, shoulders, elbows, wrists, hips, knees, and ankles. Posenet was created using deep machine learning and makes use ofTensorflow.js. Many are familiar with how pose detection can be embedded in video games via Microsoft's Xbox Kinect. What is so impressive about Posenet is that it works in the browser without any special software or hardware (other than a webcam).
The poses block reports a list of poses. One pose for each person in front of the camera. The system can be configured on the "poses" tab to be optimised for scenes with more than one person. Tradeoffs between speed and accuracy can also be made. The "poses" tab is also a good place to explore pose detection. There are two kinds of pose property blocks that can fetch information for a pose. They both can specify that one wants the confidence the system has that it has correctly identified a particular body part (values between 0 and 100). Or that the location of the body part is desired which is reported as a list of the x and y coordinates. Depending upon the value of the flag the location is reported either as values between 0 and 100 or as coordinates of the stage. Note that because Posenet displays the video in a mirror-like fashion left and right are from the camera's viewpoint not the person whose pose is being analysed.
Using the poses block a Simon Says game can be built. It chooses whether to preface its statement with "Simon says" and picks two body parts for the player to bring together.
Exercise. The full screen version of the game can be enhanced in many ways. Be creative.
Recently researchers have invented a way to use two machine learning programs to take an image and turn it into one that looks like a famous painting. This is called style transfer. The Create costume in style ... block takes in a Snap! sprite costume (that can be any image or captured from the camera) and a style name and produces a new costume in that style.
The available styles are based upon these paintings:
Here is a sample program that takes a photo and creates all available styles of it. It uses Snap!'s "ghost effect" to dissolve in and out the styled images.
The Create costume in style ... block relies upon the work done by students at the New York University Tisch School of the Arts who created web-based versions of style transfer models. Adapting it to work with Snap! was done by a high school student named Luie. The core idea is to use a deep learning model that can recognise images to generate a new image that matches well the low-level style features of the style image and at the same time matches the high-level content features of a content image. Another machine learning model has been trained to do this quickly.
A very good short video explaining how style transfer works is here:
Most of the project ideas listed for using image recognition services can be based upon the blocks described in this chapter. There is much that training by users enables that cloud services can't do. You can train the system to recognise yourself, distinguish between your friends, gestures, and the things that your robots may encounter in its particular environment.
Here are some project ideas that exploit the ability of users to train the system:
Many projects are possible by using the pose detection block and style transfer block:
The machine learning for kids site also has sample projects that can be adapted.
Several enhancements to this chapter are planned. These include support for other kinds of input such as text or numbers. Interfaces to other pre-trained models are planned.
In addition to the resources listed in the previous chapter there is the Learning about machine learning website. It has many "unplugged" activities and a long annotated list of resources appropriate for school students. The Royal Society has a good machine learning web site that includes two very nice interactive infographics: What is machine learning? and Machine learning in the world. Technical details on the deep learning model used here are documented here.
There is the Google blog post about style transfer that explains the ideas and introduces the idea of generating images that match multiple styles. A Neural Algorithm of Artistic Style is the paper that pioneered the idea of using deep learning models to transfer style while preserving content. Recently a painting created by a machine learning program was sold at a major art auction for the first time.
Go to the next chapter on using AI on words and sentences
Return to the previous chapter on using image recognition services.