createDenseNetModel2D: 2-D implementation of the DenseNet deep learning...

In ANTsX/ANTsRNet: Neural Networks for Medical Image Processing

2-D implementation of the DenseNet deep learning architecture.

Description

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

Usage

createDenseNetModel2D(
  inputImageSize,
  numberOfOutputs = 1000,
  numberOfFilters = 16,
  depth = 7,
  numberOfDenseBlocks = 1,
  growthRate = 12,
  dropoutRate = 0.2,
  weightDecay = 1e-04,
  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.
numberOfOutputs	Specifies number of units in final layer
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'.

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

Value

an DenseNet keras model

Author(s)

Tustison NJ

Examples

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 <- createDenseNetModel2D( inputImageSize = inputImageSize,
  numberOfOutputs = 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 )

ANTsX/ANTsRNet documentation built on Nov. 21, 2024, 4:07 a.m.