loadImage: Transformasi Gambar Beban Pembelajaran Mesin

Artikel
05/23/2023

Memuat data gambar.

Penggunaan

  loadImage(vars)

Argumen

`vars`

Daftar vektor karakter bernama dari nama variabel input dan nama variabel output. Perhatikan bahwa variabel input harus berjenis yang sama. Untuk pemetaan satu-ke-satu antara variabel input dan output, vektor karakter bernama dapat digunakan.

Detail

loadImage memuat gambar dari jalur.

Nilai

Objek maml yang menentukan transformasi.

Penulis

Microsoft Corporation Microsoft Technical Support

Contoh


 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")

Bagikan melalui