createVggModel2D: 2-D implementation of the VGG deep learning architecture.

In ANTsX/ANTsRNet: Neural Networks for Medical Image Processing

2-D implementation of the VGG deep learning architecture.

Description

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

Usage

createVggModel2D(
  inputImageSize,
  numberOfOutputs = 1000,
  layers = c(1, 2, 3, 4, 4),
  lowestResolution = 64,
  convolutionKernelSize = c(3, 3),
  poolSize = c(2, 2),
  strides = c(2, 2),
  numberOfDenseUnits = 4096,
  dropoutRate = 0,
  style = 19,
  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.
numberOfOutputs	Number of outputs in the final layer
layers	a vector determining the number of filters defined at for each layer.
lowestResolution	number of filters at the beginning.
convolutionKernelSize	2-d vector definining the kernel size during the encoding path
poolSize	2-d vector defining the region for each pooling layer.
strides	2-d vector describing the stride length in each direction.
numberOfDenseUnits	integer for the number of units in the last layers.
dropoutRate	float between 0 and 1 to use between dense layers.
style	⁠'16'⁠ or ⁠'19'⁠ for VGG16 or VGG19, respectively.
mode	'classification' or 'regression'.

Details

K. Simonyan and A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition

available here:

    \url{https://arxiv.org/abs/1409.1556}

This particular implementation was influenced by the following python implementation:

    \url{https://gist.github.com/baraldilorenzo/8d096f48a1be4a2d660d}

Value

a VGG keras model

Author(s)

Tustison NJ

Examples

library( ANTsRNet )
library( keras )
library( ANTsR )

mnistData <- dataset_mnist()
numberOfLabels <- 10

# Extract a small subset for something that can run quickly.
# We also need to resample since the native mnist data size does
# not fit with GoogLeNet parameters.

resampledImageSize <- c( 100, 100 )
numberOfTrainingData <- 10
numberOfTestingData <- 5

X_trainSmall <- as.array(
  resampleImage( as.antsImage( mnistData$train$x[1:numberOfTrainingData,,] ),
    c( numberOfTrainingData, resampledImageSize ), TRUE ) )
X_trainSmall <- array( data = X_trainSmall, dim = c( dim( X_trainSmall ), 1 ) )
Y_trainSmall <- to_categorical( mnistData$train$y[1:numberOfTrainingData], numberOfLabels )

X_testSmall <- as.array(
  resampleImage( as.antsImage( mnistData$test$x[1:numberOfTestingData,,] ),
    c( numberOfTestingData, resampledImageSize ), TRUE ) )
X_testSmall <- array( data = X_testSmall, dim = c( dim( X_testSmall ), 1 ) )
Y_testSmall <- to_categorical( mnistData$test$y[1:numberOfTestingData], numberOfLabels )

# We add a dimension of 1 to specify the channel size

inputImageSize <- c( dim( X_trainSmall )[2:3], 1 )

model <- createVggModel2D( inputImageSize = c( resampledImageSize, 1 ),
  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 April 18, 2024, 8 a.m.