3-D implementation of the AlexNet deep learning architecture.

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

createAlexNetModel3D(
  inputImageSize,
  numberOfClassificationLabels = 1000,
  numberOfDenseUnits = 4096,
  dropoutRate = 0,
  mode = c("classification", "regression"),
  batch_size = NULL
)

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.
numberOfDenseUnits	number of dense units.
dropoutRate	optional regularization parameter between [0, 1]. Default = 0.0.
mode	'classification' or 'regression'. Default = 'classification'.
batch_size	batch size to pass to first layer

Value

an AlexNet keras model

Details

A. Krizhevsky, and I. Sutskever, and G. Hinton. ImageNet Classification with Deep Convolutional Neural Networks.

available here:

    http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf

This particular implementation was influenced by the following python implementation:

    https://github.com/duggalrahul/AlexNet-Experiments-Keras/
    https://github.com/lunardog/convnets-keras/

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
rm(mnistData); gc()
inputImageSize <- c( dim( X_trainSmall )[2:3], 1 )

model <- createAlexNetModel2D( inputImageSize = inputImageSize,
  numberOfClassificationLabels = numberOfLabels )

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

track <- model %>% fit( X_trainSmall, Y_trainSmall, verbose = 1,
  epochs = 2, batch_size = 20, shuffle = TRUE, validation_split = 0.25 )

# Now test the model

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