2-D implementation of the ResNet deep learning architecture with a preceding spatial transformer network layer.

Creates a keras model of the ResNet deep learning architecture for image classification with a spatial transformer network (STN) layer. The paper is available here:

createResNetWithSpatialTransformerNetworkModel2D(
  inputImageSize,
  numberOfClassificationLabels = 1000,
  layers = 1:4,
  residualBlockSchedule = c(3, 4, 6, 3),
  lowestResolution = 64,
  cardinality = 1,
  numberOfSpatialTransformerUnits = 50,
  resampledSize = c(64, 64),
  mode = c("classification", "regression")
)

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.
numberOfClassificationLabels	Number of segmentation labels.
layers	a vector determining the number of 'filters' defined at for each layer.
residualBlockSchedule	vector defining the how many residual blocks repeats.
lowestResolution	number of filters at the initial layer.
cardinality	perform ResNet (cardinality = 1) or ResNeXt (cardinality != 1 but powers of 2---try '32' )
numberOfSpatialTransformerUnits	number of units in the dense layer.
resampledSize	output image size of the spatial transformer network.
mode	'classification' or 'regression'.

Value

an STN + ResNet keras model

Details

    https://arxiv.org/abs/1512.03385

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[1:10,,]
X_trainSmall <- array( data = X_trainSmall, dim = c( dim( X_trainSmall ), 1 ) )
Y_trainSmall <- to_categorical( mnistData$train$y[1:10], 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 <- createResNetWithSpatialTransformerNetworkModel2D(
  inputImageSize = inputImageSize,
  numberOfClassificationLabels = numberOfLabels )

model %>% compile( loss = 'categorical_crossentropy',
  optimizer = optimizer_adam( lr = 0.0001 ),
  metrics = c( 'categorical_crossentropy', 'accuracy' ) )

# Comment out the rest due to travis build constraints

# track <- model %>% fit( X_trainSmall, Y_trainSmall, verbose = 1,
#   epochs = 1, batch_size = 2, shuffle = TRUE, validation_split = 0.5 )

# Now test the model

# testingMetrics <- model %>% evaluate( X_testSmall, Y_testSmall )
# predictedData <- model %>% predict( X_testSmall, verbose = 1 )
rm(model); gc()
}