featurizeimage.md

title	description	author	ms.author	ms.date	ms.service	ms.subservice	ms.topic	keywords	monikerRange
featurizeImage function (MicrosoftML)	Featurizes an image using a pre-trained deep neural network model (MicrosoftML).	rothja	jroth	07/15/2019	sql	machine-learning	reference	(MicrosoftML) featurizeImage alexnet cnn dnn featurize image resnet transform	>=sql-server-2016||>=sql-server-linux-ver15

featurizeImage: Machine Learning Image Featurization Transform

Featurizes an image using a pre-trained deep neural network model.

Usage

  featurizeImage(var, outVar = NULL, dnnModel = "Resnet18")

Arguments

var

Input variable containing extracted pixel values.

outVar

The prefix of the output variables containing the image features. If null, the input variable name will be used. The default value is NULL.

dnnModel

The pre-trained deep neural network. The possible options are:

• "resnet18"
• "resnet50"
• "resnet101"
• "alexnet"
  The default value is "resnet18". See Deep Residual Learning for Image Recognition for details about ResNet.

Details

featurizeImage featurizes an image using the specified pre-trained deep neural network model. The input variables to this transform must be extracted pixel values.

Value

A maml object defining the transform.

Author(s)

Microsoft Corporation Microsoft Technical Support

Examples

 train <- data.frame(Path = c(system.file("help/figures/RevolutionAnalyticslogo.png", package = "MicrosoftML")), Label = c(TRUE), stringsAsFactors = FALSE)

 # Loads the images from variable Path, resizes the images to 1x1 pixels and trains a neural net.
 model <- rxNeuralNet(
     Label ~ Features,
     data = train,
     mlTransforms = list(
         loadImage(vars = list(Features = "Path")),
         resizeImage(vars = "Features", width = 1, height = 1, resizing = "Aniso"),
         extractPixels(vars = "Features")
         ),
     mlTransformVars = "Path",
     numHiddenNodes = 1,
     numIterations = 1)

 # Featurizes the images from variable Path using the default model, and trains a linear model on the result.
 model <- rxFastLinear(
     Label ~ Features,
     data = train,
     mlTransforms = list(
         loadImage(vars = list(Features = "Path")),
         resizeImage(vars = "Features", width = 224, height = 224), # If dnnModel == "AlexNet", the image has to be resized to 227x227.
         extractPixels(vars = "Features"),
         featurizeImage(var = "Features")
         ),
     mlTransformVars = "Path")