3-D implementation of the spatial transformer network.

Creates a keras model of the spatial transformer network:

createSimpleClassificationWithSpatialTransformerNetworkModel3D(
  inputImageSize,
  resampledSize = c(30, 30, 30),
  numberOfClassificationLabels = 10
)

Arguments

inputImageSize	Used for specifying the input tensor shape. The shape (or dimension) of that tensor is the image dimensions followed by the number of channels (e.g., red, green, and blue). The batch size (i.e., number of training images) is not specified a priori.
resampledSize	resampled size of the transformed input images.
numberOfClassificationLabels	Number of classes.

Value

a keras model

Details

    \url{https://arxiv.org/abs/1506.02025}

based on the following python Keras model:

    \url{https://github.com/oarriaga/STN.keras/blob/master/src/models/STN.py}

Author

Tustison NJ

Examples

if (FALSE) {

library( ANTsRNet )
library( keras )

mnistData <- dataset_mnist()
numberOfLabels <- 10

# Extract a small subset for something that can run quickly

X_trainSmall <- mnistData$train$x[1:100,,]
X_trainSmall <- array( data = X_trainSmall, dim = c( dim( X_trainSmall ), 1 ) )
Y_trainSmall <- to_categorical( mnistData$train$y[1:100], numberOfLabels )

X_testSmall <- mnistData$test$x[1:10,,]
X_testSmall <- array( data = X_testSmall, dim = c( dim( X_testSmall ), 1 ) )
Y_testSmall <- to_categorical( mnistData$test$y[1:10], numberOfLabels )

# We add a dimension of 1 to specify the channel size

inputImageSize <- c( dim( X_trainSmall )[2:3], 1 )

model <- createSimpleClassificationWithSpatialTransformerNetworkModel2D(
  inputImageSize = inputImageSize,
  resampledSize = c( 30, 30 ), numberOfClassificationLabels = numberOfLabels )

}