createSimpleClassificationWithSpatialTransformerNetworkModel3D: 3-D implementation of the spatial transformer network.

In ANTsX/ANTsRNet: Neural Networks for Medical Image Processing

3-D implementation of the spatial transformer network.

Description

Creates a keras model of the spatial transformer network:

Usage

createSimpleClassificationWithSpatialTransformerNetworkModel3D(
  inputImageSize,
  resampledSize = c(30, 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.

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}

Value

a keras model

Author(s)

Tustison NJ

Examples

## Not run: 

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 )

model <- createSimpleClassificationWithSpatialTransformerNetworkModel2D(
  inputImageSize = inputImageSize,
  resampledSize = c( 30, 30 ), numberOfClassificationLabels = numberOfLabels )


## End(Not run)

ANTsX/ANTsRNet documentation built on April 23, 2024, 1:24 p.m.