2-D implementation of the DenseNet deep learning architecture.

Source: R/createDenseNetModel.R

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

createDenseNetModel2D(
  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


library( ANTsRNet )
library( keras )

mnistData <- dataset_mnist()

#> Error in py_discover_config(required_module, use_environment): Python specified in RETICULATE_PYTHON (/Users/ntustison/anaconda3/envs/antsx/bin/python3) does not exist
numberOfLabels <- 10

# Extract a small subset for something that can run quickly

X_trainSmall <- mnistData$train$x[1:100,,]

#> Error in eval(expr, envir, enclos): object 'mnistData' not found
X_trainSmall <- array( data = X_trainSmall, dim = c( dim( X_trainSmall ), 1 ) )

#> Error in array(data = X_trainSmall, dim = c(dim(X_trainSmall), 1)): object 'X_trainSmall' not found
Y_trainSmall <- to_categorical( mnistData$train$y[1:100], numberOfLabels )

#> Error in to_categorical(mnistData$train$y[1:100], numberOfLabels): object 'mnistData' not found

X_testSmall <- mnistData$test$x[1:10,,]

#> Error in eval(expr, envir, enclos): object 'mnistData' not found
X_testSmall <- array( data = X_testSmall, dim = c( dim( X_testSmall ), 1 ) )

#> Error in array(data = X_testSmall, dim = c(dim(X_testSmall), 1)): object 'X_testSmall' not found
Y_testSmall <- to_categorical( mnistData$test$y[1:10], numberOfLabels )

#> Error in to_categorical(mnistData$test$y[1:10], numberOfLabels): object 'mnistData' not found

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

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

#> Error in eval(expr, envir, enclos): object 'X_trainSmall' not found

model <- createDenseNetModel2D( inputImageSize = inputImageSize,
  numberOfClassificationLabels = numberOfLabels )

#> Error in py_discover_config(required_module, use_environment): Python specified in RETICULATE_PYTHON (/Users/ntustison/anaconda3/envs/antsx/bin/python3) does not exist

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

#> Error in compile(., loss = "categorical_crossentropy", optimizer = optimizer_adam(lr = 1e-04),     metrics = c("categorical_crossentropy", "accuracy")): object 'model' not found

# 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 )