22 | Machine Learning |
So far in this book, when we’ve wanted the Wolfram Language to do something, we’ve written code to tell it exactly what to do. But the Wolfram Language is also set up to be able to learn what to do just by looking at examples, using the idea of machine learning.
We’ll talk about how to train the language yourself. But first let’s look at some built-in functions that have already been trained on huge numbers of examples.
LanguageIdentify takes pieces of text, and identifies what human language they’re in.
Identify the language each phrase is in:
LanguageIdentify[{ "thank you", "merci", "dar las gracias", "感謝",
"благодарить"}]
The Wolfram Language can also do the considerably more difficult “artificial intelligence” task of identifying what an image is of.
Identify what an image is of:
ImageIdentify[\!\(\*
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There’s a general function Classify, which has been taught various kinds of classification. One example is classifying the “sentiment” of text.
Upbeat text is classified as having positive sentiment:
Classify["Sentiment", "I'm so excited to be programming"]
Downbeat text is classified as having negative sentiment:
Classify["Sentiment", "math can be really hard"]
You can also train Classify yourself. Here’s a simple example of classifying handwritten digits as 0 or 1. You give Classify a collection of training examples, followed by a particular handwritten digit. Then it’ll tell you whether the digit you give is a 0 or 1.
With training examples, Classify correctly identifies a handwritten 0:
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ImageSizeRaw->{20, 20},
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ImageSizeRaw->{20, 20},
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To get some sense of how this works—and because it’s useful in its own right—let’s talk about the function Nearest, that finds what element in a list is nearest to what you supply.
Find what number in the list is nearest to 22:
Nearest[{10, 20, 30, 40, 50, 60, 70, 80}, 22]
Find the nearest three numbers:
Nearest[{10, 20, 30, 40, 50, 60, 70, 80}, 22, 3]
Nearest can find nearest colors as well.
Find the 3 colors in the list that are nearest to the color you give:
Nearest[{Hue[0.], Hue[0.05], Hue[0.1], Hue[0.15000000000000002`], Hue[
0.2], Hue[0.25], Hue[0.30000000000000004`], Hue[
0.35000000000000003`], Hue[0.4], Hue[0.45], Hue[0.5], Hue[0.55],
Hue[0.6000000000000001], Hue[0.65], Hue[0.7000000000000001], Hue[
0.75], Hue[0.8]}, RGBColor[
0.8727094319714808, 0.9066617343162084, 0.09569669807641734], 3]
It also works on words.
Find the 10 words nearest to “good” in the list of words:
Nearest[WordList[ ], "good", 10]
There’s a notion of nearness for images too. And though it’s far from the whole story, this is effectively part of what ImageIdentify is using.
Something that’s again related is recognizing text (optical character recognition or OCR). Let’s make a piece of text that’s blurred.
Create an image of the word “hello”, then blur it:
Blur[Rasterize[Style["hello", 30]], 3]
TextRecognize can still recognize the original text string in this.
Recognize text in the image:
TextRecognize[\!\(\*
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"], {{0, 38}, {63, 0}}, {0, 255},
ColorFunction->RGBColor],
BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> False],
Selectable->False],
DefaultBaseStyle->"ImageGraphics",
ImageSize->Automatic,
ImageSizeRaw->{63, 38},
PlotRange->{{0, 63}, {0, 38}}]\)]
If the text gets too blurred TextRecognize can’t tell what it says—and you probably can’t either.
Generate a sequence of progressively more blurred pieces of text:
Table[Blur[Rasterize["hello"], r], {r, 0, 10}]
As the text gets more blurred, TextRecognize makes a mistake, then gives up altogether:
Table[TextRecognize[Blur[Rasterize["hello"], r]], {r, 0, 10}]
Something similar happens if we progressively blur the picture of a cheetah. When the picture is still fairly sharp, ImageIdentify will correctly identify it as a cheetah. But when it gets too blurred ImageIdentify starts thinking it’s more likely to be a lion, and eventually the best guess is that it’s a picture of a person.
Progressively blur a picture of a cheetah:
Table[Blur[\!\(\*
GraphicsBox[
TagBox[RasterBox[CompressedData["
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ColorFunction->RGBColor],
BoxForm`ImageTag[
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MetaInformation -> {"Exif" -> {"Make" -> "Canon",
"Model" -> "Canon EOS 700D",
"ExposureTime" -> Rational[1, 320],
"FNumber" -> Rational[28, 5], "ISOSpeedRatings" -> 2000,
"DateTimeOriginal" -> "2014:08:18 16:34:53", "Flash" -> 16,
"FocalLength" -> 250,
System`Convert`ExifDump`ReadFIDData[
InputStream[String, 4], {136, 0, 0, 0, 0}, True],
"GPSLatitudeRef" -> "N", "GPSLatitude" -> {51, 54,
Rational[147, 100]}, "GPSLongitudeRef" -> "W",
"GPSLongitude" -> {8, 17,
Rational[667, 20]}}}],
Selectable->False],
BaseStyle->"ImageGraphics",
ImageSize->{83.03125, Automatic},
ImageSizeRaw->{320, 213},
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When the picture gets too blurred, ImageIdentify no longer thinks it’s a cheetah:
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ImageIdentify normally just gives what it thinks is the most likely identification. You can tell it, though, to give a list of possible identifications, starting from the most likely. Here are the top 10 possible identifications, in all categories.
ImageIdentify thinks this might be a cheetah, but it’s more likely to be a wildcat, or it could be a baboon:
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