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createGoogLeNetModel.R
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createGoogLeNetModel.R
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#' 2-D implementation of the GoogLeNet deep learning architecture.
#'
#' Creates a keras model of the GoogLeNet deep learning architecture for image
#' recognition based on the paper
#'
#' C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke,
#' A. Rabinovich, Going Deeper with Convolutions
#' C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna. Rethinking the Inception
#' Architecture for Computer Vision
#'
#' available here:
#'
#' https://arxiv.org/abs/1409.4842
#' https://arxiv.org/abs/1512.00567
#'
#' This particular implementation was influenced by the following python
#' implementation:
#'
#' https://github.com/fchollet/deep-learning-models/blob/master/inception_v3.py
#'
#' @param 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.
#' @param numberOfOutputs Specifies number of units in final layer
#' @param mode 'classification' or 'regression'.
#'
#' @return a GoogLeNet keras model
#' @author 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 <- createGoogLeNetModel2D( 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 )
#' rm(model); gc()
#' model <- createGoogLeNetModel2D( inputImageSize = c( resampledImageSize, 1 ),
#' numberOfOutputs = 2 )
#' rm(model); gc()
#'
#' model <- createGoogLeNetModel2D( inputImageSize = c( resampledImageSize, 1 ),
#' mode = "regression" )
#' rm(model); gc()
#' @import keras
#' @export
createGoogLeNetModel2D <- function( inputImageSize,
numberOfOutputs = 1000,
mode = c( "classification", "regression" )
){
K <- keras::backend()
mode <- match.arg( mode )
convolutionAndBatchNormalization2D <- function(
model,
numberOfFilters,
kernelSize,
padding = 'same',
strides = c( 1, 1 ) )
{
K <- keras::backend()
channelAxis <- 1
if( K$image_data_format() == 'channels_last' )
{
channelAxis <- 3
}
model <- model %>% layer_conv_2d(
numberOfFilters,
kernel_size = kernelSize, padding = padding, strides = strides,
use_bias = TRUE )
model <- model %>% layer_batch_normalization( axis = channelAxis,
scale = FALSE )
model <- model %>% layer_activation( activation = 'relu' )
return( model )
}
channelAxis <- 1
if( K$image_data_format() == 'channels_last' )
{
channelAxis <- 3
}
inputs <- layer_input( shape = inputImageSize )
outputs <- convolutionAndBatchNormalization2D( inputs, numberOfFilters = 32,
kernelSize = c( 3, 3 ), strides = c( 2, 2 ), padding = 'valid' )
outputs <- convolutionAndBatchNormalization2D( outputs, numberOfFilters = 32,
kernelSize = c( 3, 3 ), padding = 'valid' )
outputs <- convolutionAndBatchNormalization2D( outputs, numberOfFilters = 64,
kernelSize = c( 3, 3 ) )
outputs <- outputs %>% layer_max_pooling_2d( pool_size = c( 3, 3 ),
strides = c( 2, 2 ) )
outputs <- convolutionAndBatchNormalization2D( outputs, numberOfFilters = 80,
kernelSize = c( 1, 1 ), padding = 'valid' )
outputs <- convolutionAndBatchNormalization2D( outputs, numberOfFilters = 192,
kernelSize = c( 3, 3 ) )
outputs <- outputs %>% layer_max_pooling_2d( pool_size = c( 3, 3 ),
strides = c( 2, 2 ) )
# mixed 0, 1, 2: 35x35x256
branchLayers <- list()
branchLayers[[1]] <- convolutionAndBatchNormalization2D(
outputs, numberOfFilters = 64,
kernelSize = c( 1, 1 ) )
branchLayers[[2]] <- convolutionAndBatchNormalization2D(
outputs, numberOfFilters = 48,
kernelSize = c( 1, 1 ) )
branchLayers[[2]] <- convolutionAndBatchNormalization2D(
branchLayers[[2]],
numberOfFilters = 64, kernelSize = c( 5, 5 ) )
branchLayers[[3]] <- convolutionAndBatchNormalization2D(
outputs, numberOfFilters = 64,
kernelSize = c( 1, 1 ) )
branchLayers[[3]] <- convolutionAndBatchNormalization2D(
branchLayers[[3]],
numberOfFilters = 96, kernelSize = c( 3, 3 ) )
branchLayers[[3]] <- convolutionAndBatchNormalization2D(
branchLayers[[3]],
numberOfFilters = 96, kernelSize = c( 3, 3 ) )
branchLayers[[4]] <- outputs %>% layer_average_pooling_2d(
pool_size = c( 3, 3 ),
strides = c( 1, 1 ), padding = 'same' )
branchLayers[[4]] <- convolutionAndBatchNormalization2D(
branchLayers[[4]],
numberOfFilters = 32, kernelSize = c( 1, 1 ) )
outputs <- layer_concatenate( branchLayers, axis = channelAxis, trainable = TRUE )
# mixed 1: 35x35x256
branchLayers <- list()
branchLayers[[1]] <- convolutionAndBatchNormalization2D(
outputs, numberOfFilters = 64,
kernelSize = c( 1, 1 ) )
branchLayers[[2]] <- convolutionAndBatchNormalization2D(
outputs, numberOfFilters = 48,
kernelSize = c( 1, 1 ) )
branchLayers[[2]] <- convolutionAndBatchNormalization2D(
branchLayers[[2]],
numberOfFilters = 64, kernelSize = c( 5, 5 ) )
branchLayers[[3]] <- convolutionAndBatchNormalization2D(
outputs, numberOfFilters = 64,
kernelSize = c( 1, 1 ) )
branchLayers[[3]] <- convolutionAndBatchNormalization2D(
branchLayers[[3]],
numberOfFilters = 96, kernelSize = c( 3, 3 ) )
branchLayers[[3]] <- convolutionAndBatchNormalization2D(
branchLayers[[3]],
numberOfFilters = 96, kernelSize = c( 3, 3 ) )
branchLayers[[4]] <- outputs %>% layer_average_pooling_2d(
pool_size = c( 3, 3 ),
strides = c( 1, 1 ), padding = 'same' )
branchLayers[[4]] <- convolutionAndBatchNormalization2D(
branchLayers[[4]],
numberOfFilters = 32, kernelSize = c( 1, 1 ) )
outputs <- layer_concatenate( branchLayers, axis = channelAxis, trainable = TRUE )
# mixed 2: 35x35x256
branchLayers <- list()
branchLayers[[1]] <- convolutionAndBatchNormalization2D(
outputs, numberOfFilters = 64,
kernelSize = c( 1, 1 ) )
branchLayers[[2]] <- convolutionAndBatchNormalization2D(
outputs, numberOfFilters = 48,
kernelSize = c( 1, 1 ) )
branchLayers[[2]] <- convolutionAndBatchNormalization2D(
branchLayers[[2]],
numberOfFilters = 64, kernelSize = c( 5, 5 ) )
branchLayers[[3]] <- convolutionAndBatchNormalization2D(
outputs, numberOfFilters = 64,
kernelSize = c( 1, 1 ) )
branchLayers[[3]] <- convolutionAndBatchNormalization2D(
branchLayers[[3]],
numberOfFilters = 96, kernelSize = c( 3, 3 ) )
branchLayers[[3]] <- convolutionAndBatchNormalization2D(
branchLayers[[3]],
numberOfFilters = 96, kernelSize = c( 3, 3 ) )
branchLayers[[4]] <- outputs %>% layer_average_pooling_2d(
pool_size = c( 3, 3 ),
strides = c( 1, 1 ), padding = 'same' )
branchLayers[[4]] <- convolutionAndBatchNormalization2D(
branchLayers[[4]],
numberOfFilters = 32, kernelSize = c( 1, 1 ) )
outputs <- layer_concatenate( branchLayers, axis = channelAxis, trainable = TRUE )
# mixed 3: 17x17x768
branchLayers <- list()
branchLayers[[1]] <- convolutionAndBatchNormalization2D( outputs, numberOfFilters = 384,
kernelSize = c( 3, 3 ), strides = c( 2, 2 ), padding = 'valid' )
branchLayers[[2]] <- convolutionAndBatchNormalization2D( outputs, numberOfFilters = 64,
kernelSize = c( 1, 1 ) )
branchLayers[[2]] <- convolutionAndBatchNormalization2D( branchLayers[[2]],
numberOfFilters = 96, kernelSize = c( 3, 3 ) )
branchLayers[[2]] <- convolutionAndBatchNormalization2D( branchLayers[[2]],
numberOfFilters = 96, kernelSize = c( 3, 3 ), strides = c( 2, 2 ), padding = 'valid' )
branchLayers[[3]] <- outputs %>% layer_max_pooling_2d( pool_size = c( 3, 3 ),
strides = c( 2, 2 ) )
outputs <- layer_concatenate( branchLayers, axis = channelAxis, trainable = TRUE )
# mixed 4: 17x17x768
branchLayers <- list()
branchLayers[[1]] <- convolutionAndBatchNormalization2D( outputs, numberOfFilters = 192,
kernelSize = c( 1, 1 ) )
branchLayers[[2]] <- convolutionAndBatchNormalization2D( outputs, numberOfFilters = 128,
kernelSize = c( 1, 1 ) )
branchLayers[[2]] <- convolutionAndBatchNormalization2D( branchLayers[[2]],
numberOfFilters = 128, kernelSize = c( 1, 7 ) )
branchLayers[[2]] <- convolutionAndBatchNormalization2D( branchLayers[[2]],
numberOfFilters = 192, kernelSize = c( 7, 1 ) )
branchLayers[[3]] <- convolutionAndBatchNormalization2D( outputs, numberOfFilters = 128,
kernelSize = c( 1, 1 ) )
branchLayers[[3]] <- convolutionAndBatchNormalization2D( branchLayers[[3]],
numberOfFilters = 128, kernelSize = c( 7, 1 ) )
branchLayers[[3]] <- convolutionAndBatchNormalization2D( branchLayers[[3]],
numberOfFilters = 128, kernelSize = c( 1, 7 ) )
branchLayers[[3]] <- convolutionAndBatchNormalization2D( branchLayers[[3]],
numberOfFilters = 128, kernelSize = c( 7, 1 ) )
branchLayers[[3]] <- convolutionAndBatchNormalization2D( branchLayers[[3]],
numberOfFilters = 192, kernelSize = c( 1, 7 ) )
branchLayers[[4]] <- outputs %>% layer_average_pooling_2d( pool_size = c( 3, 3 ),
strides = c( 1, 1 ), padding = 'same' )
branchLayers[[4]] <- convolutionAndBatchNormalization2D( branchLayers[[4]],
numberOfFilters = 192, kernelSize = c( 1, 1 ) )
outputs <- layer_concatenate( branchLayers, axis = channelAxis, trainable = TRUE )
# mixed 4: 17x17x768
for( i in 1:2 )
{
branchLayers <- list()
branchLayers[[1]] <- convolutionAndBatchNormalization2D( outputs, numberOfFilters = 192,
kernelSize = c( 1, 1 ) )
branchLayers[[2]] <- convolutionAndBatchNormalization2D( outputs, numberOfFilters = 160,
kernelSize = c( 1, 1 ) )
branchLayers[[2]] <- convolutionAndBatchNormalization2D( branchLayers[[2]],
numberOfFilters = 160, kernelSize = c( 1, 7 ) )
branchLayers[[2]] <- convolutionAndBatchNormalization2D( branchLayers[[2]],
numberOfFilters = 192, kernelSize = c( 7, 1 ) )
branchLayers[[3]] <- convolutionAndBatchNormalization2D( outputs, numberOfFilters = 160,
kernelSize = c( 1, 1 ) )
branchLayers[[3]] <- convolutionAndBatchNormalization2D( branchLayers[[3]],
numberOfFilters = 160, kernelSize = c( 7, 1 ) )
branchLayers[[3]] <- convolutionAndBatchNormalization2D( branchLayers[[3]],
numberOfFilters = 160, kernelSize = c( 1, 7 ) )
branchLayers[[3]] <- convolutionAndBatchNormalization2D( branchLayers[[3]],
numberOfFilters = 160, kernelSize = c( 7, 1 ) )
branchLayers[[3]] <- convolutionAndBatchNormalization2D( branchLayers[[3]],
numberOfFilters = 192, kernelSize = c( 1, 7 ) )
branchLayers[[4]] <- outputs %>% layer_average_pooling_2d( pool_size = c( 3, 3 ),
strides = c( 1, 1 ), padding = 'same' )
branchLayers[[4]] <- convolutionAndBatchNormalization2D( branchLayers[[4]],
numberOfFilters = 192, kernelSize = c( 1, 1 ) )
outputs <- layer_concatenate( branchLayers, axis = channelAxis, trainable = TRUE )
}
# mixed 7: 17x17x768
branchLayers <- list()
branchLayers[[1]] <- convolutionAndBatchNormalization2D( outputs, numberOfFilters = 192,
kernelSize = c( 1, 1 ) )
branchLayers[[2]] <- convolutionAndBatchNormalization2D( outputs, numberOfFilters = 192,
kernelSize = c( 1, 1 ) )
branchLayers[[2]] <- convolutionAndBatchNormalization2D( branchLayers[[2]],
numberOfFilters = 192, kernelSize = c( 1, 7 ) )
branchLayers[[2]] <- convolutionAndBatchNormalization2D( branchLayers[[2]],
numberOfFilters = 192, kernelSize = c( 7, 1 ) )
branchLayers[[3]] <- convolutionAndBatchNormalization2D( outputs, numberOfFilters = 192,
kernelSize = c( 1, 1 ) )
branchLayers[[3]] <- convolutionAndBatchNormalization2D( branchLayers[[3]],
numberOfFilters = 192, kernelSize = c( 7, 1 ) )
branchLayers[[3]] <- convolutionAndBatchNormalization2D( branchLayers[[3]],
numberOfFilters = 192, kernelSize = c( 1, 7 ) )
branchLayers[[3]] <- convolutionAndBatchNormalization2D( branchLayers[[3]],
numberOfFilters = 192, kernelSize = c( 7, 1 ) )
branchLayers[[3]] <- convolutionAndBatchNormalization2D( branchLayers[[3]],
numberOfFilters = 192, kernelSize = c( 1, 7 ) )
branchLayers[[4]] <- outputs %>% layer_average_pooling_2d( pool_size = c( 3, 3 ),
strides = c( 1, 1 ), padding = 'same' )
branchLayers[[4]] <- convolutionAndBatchNormalization2D( branchLayers[[4]],
numberOfFilters = 192, kernelSize = c( 1, 1 ) )
outputs <- layer_concatenate( branchLayers, axis = channelAxis, trainable = TRUE)
# mixed 8: 8x8x1280
branchLayers <- list()
branchLayers[[1]] <- convolutionAndBatchNormalization2D( outputs, numberOfFilters = 192,
kernelSize = c( 1, 1 ) )
branchLayers[[1]] <- convolutionAndBatchNormalization2D( branchLayers[[1]],
numberOfFilters = 320, kernelSize = c( 3, 3 ), strides = c( 2, 2 ),
padding = 'valid' )
branchLayers[[2]] <- convolutionAndBatchNormalization2D( outputs, numberOfFilters = 192,
kernelSize = c( 1, 1 ) )
branchLayers[[2]] <- convolutionAndBatchNormalization2D( branchLayers[[2]],
numberOfFilters = 192, kernelSize = c( 1, 7 ) )
branchLayers[[2]] <- convolutionAndBatchNormalization2D( branchLayers[[2]],
numberOfFilters = 192, kernelSize = c( 7, 1 ) )
branchLayers[[2]] <- convolutionAndBatchNormalization2D( branchLayers[[2]],
numberOfFilters = 192, kernelSize = c( 3, 3 ), strides = c( 2, 2 ),
padding = 'valid' )
branchLayers[[3]] <- outputs %>% layer_max_pooling_2d( pool_size = c( 3, 3 ),
strides = c( 2, 2 ) )
outputs <- layer_concatenate( branchLayers, axis = channelAxis, trainable = TRUE)
# mixed 9: 8x8x2048
for( i in 1:2 )
{
branchLayers <- list()
branchLayer <- convolutionAndBatchNormalization2D( outputs, numberOfFilters = 320,
kernelSize = c( 1, 1 ) )
branchLayers[[1]] <- branchLayer
branchLayer <- convolutionAndBatchNormalization2D( outputs, numberOfFilters = 384,
kernelSize = c( 1, 1 ) )
branchLayer1 <- convolutionAndBatchNormalization2D( branchLayer,
numberOfFilters = 384, kernelSize = c( 1, 3 ) )
branchLayer2 <- convolutionAndBatchNormalization2D( branchLayer,
numberOfFilters = 384, kernelSize = c( 3, 1 ) )
branchLayers[[2]] <- layer_concatenate( list( branchLayer1, branchLayer2 ),
axis = channelAxis, trainable = TRUE )
branchLayer <- convolutionAndBatchNormalization2D( outputs, numberOfFilters = 448,
kernelSize = c( 1, 1 ) )
branchLayer <- convolutionAndBatchNormalization2D( branchLayer,
numberOfFilters = 384, kernelSize = c( 3, 3 ) )
branchLayer1 <- convolutionAndBatchNormalization2D( branchLayer,
numberOfFilters = 384, kernelSize = c( 1, 3 ) )
branchLayer2 <- convolutionAndBatchNormalization2D( branchLayer,
numberOfFilters = 384, kernelSize = c( 3, 1 ) )
branchLayers[[3]] <- layer_concatenate( list( branchLayer1, branchLayer2 ),
axis = channelAxis, trainable = TRUE )
branchLayers[[4]] <- outputs %>% layer_average_pooling_2d( pool_size = c( 3, 3 ),
strides = c( 1, 1 ), padding = 'same' )
branchLayers[[4]] <- convolutionAndBatchNormalization2D( branchLayers[[4]],
numberOfFilters = 192, kernelSize = c( 1, 1 ) )
outputs <- layer_concatenate( branchLayers, axis = channelAxis, trainable = TRUE )
}
outputs <- outputs %>% layer_global_average_pooling_2d()
layerActivation <- ''
if( mode == 'classification' ) {
layerActivation <- 'softmax'
} else if( mode == 'regression' ) {
layerActivation <- 'linear'
} else {
stop( 'Error: unrecognized mode.' )
}
outputs <- outputs %>%
layer_dense( units = numberOfOutputs, activation = layerActivation )
googLeNetModel <- keras_model( inputs = inputs, outputs = outputs )
return( googLeNetModel )
}