View source: R/createSimpleClassificationWithSpatialTransformerNetworkModel.R
createSimpleClassificationWithSpatialTransformerNetworkModel2D | R Documentation |
Creates a keras model of the spatial transformer network:
createSimpleClassificationWithSpatialTransformerNetworkModel2D(
inputImageSize,
resampledSize = c(30, 30),
numberOfClassificationLabels = 10
)
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. |
\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}
a keras model
Tustison NJ
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 )
## Not run:
model <- createSimpleClassificationWithSpatialTransformerNetworkModel2D(
inputImageSize = inputImageSize,
resampledSize = c( 30, 30 ), numberOfClassificationLabels = numberOfLabels )
## End(Not run)
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