2-D implementation of the spatial transformer network.

Creates a keras model of the spatial transformer network:

createSimpleClassificationWithSpatialTransformerNetworkModel2D(
  inputImageSize,
  resampledSize = c(30, 30),
  numberOfClassificationLabels = 10
)

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.
resampledSize	resampled size of the transformed input images.
numberOfClassificationLabels	Number of classes.

Value

a keras model

Details

    \url{https://arxiv.org/abs/1506.02025}

based on the following python Keras model:

    \url{https://github.com/oarriaga/STN.keras/blob/master/src/models/STN.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

if (FALSE) {
model <- createSimpleClassificationWithSpatialTransformerNetworkModel2D(
  inputImageSize = inputImageSize,
  resampledSize = c( 30, 30 ), numberOfClassificationLabels = numberOfLabels )
}