3-D implementation of the ResNet deep learning architecture.

Creates a keras model of the ResNet deep learning architecture for image classification. The paper is available here:

createResNetModel3D(
  inputImageSize,
  inputScalarsSize = 0,
  numberOfClassificationLabels = 1000,
  layers = 1:4,
  residualBlockSchedule = c(3, 4, 6, 3),
  lowestResolution = 64,
  cardinality = 1,
  squeezeAndExcite = FALSE,
  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.
inputScalarsSize	Optional integer specifying the size of the input vector for scalars that get concatenated to the fully connected layer at the end of the network.
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' )
squeezeAndExcite	boolean to add the squeeze-and-excite block variant.
mode	'classification', 'sigmoid' or 'regression'.

Value

an ResNet keras model

Details

    https://arxiv.org/abs/1512.03385

This particular implementation was influenced by the following python implementation:

    https://gist.github.com/mjdietzx/0cb95922aac14d446a6530f87b3a04ce

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 <- createResNetModel2D( inputImageSize = inputImageSize,
  numberOfClassificationLabels = numberOfLabels )

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

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 )

}