loadImage: Machine Learning Load Image Transform
Loads image data.
A named list of character vectors of input variable names and the name of the output variable. Note that the input variables must be of the same type. For one-to-one mappings between input and output variables, a named character vector can be used.
loadImage loads images from paths.
maml object defining the transform.
Microsoft Technical Support
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")