3-D implementation of the DenseNet deep learning architecture.

Creates a keras model of the DenseNet deep learning architecture for image recognition based on the paper

createDenseNetModel3D(
  inputImageSize,
  numberOfClassificationLabels = 1000,
  numberOfFilters = 16,
  depth = 7,
  numberOfDenseBlocks = 1,
  growthRate = 12,
  dropoutRate = 0.2,
  weightDecay = 0.0001,
  mode = "classification"
)

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.
numberOfFilters	number of filters
depth	number of layers---must be equal to 3 * N + 4 where N is an integer (default = 7).
numberOfDenseBlocks	number of dense blocks to add to the end (default = 1).
growthRate	number of filters to add for each dense block layer (default = 12).
dropoutRate	= per drop out layer rate (default = 0.2)
weightDecay	= weight decay (default = 1e-4)
mode	'classification' or 'regression'. Default = 'classification'.

Value

an DenseNet keras model

Details

G. Huang, Z. Liu, K. Weinberger, and L. van der Maaten. Densely Connected Convolutional Networks Networks

available here:

    https://arxiv.org/abs/1608.06993

This particular implementation was influenced by the following python implementation:

    https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/DenseNet/densenet.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[1:10,,]
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 <- createDenseNetModel2D( 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 )

}