Create a SpatialTransformationLayer with output size 30×30:

Create a SpatialTransformationLayer that expects an input of size 1×3×3 and returns an output of size 1×2×2:

Apply the layer to an input:

Create a SpatialTransformationLayer whose input is an image and whose output is an image:

Apply the SpatialTransformationLayer to an image with a factor-2 zoom transformation:

Apply the SpatialTransformationLayer using a sequence of zooms:

Train a digit recognizer on the MNIST database of handwritten digits using a convolutional neural network with a SpatialTransformationLayer. First obtain the training and test data:

Define a function to apply extra padding and random translations to the training and test data:

Create new training and test data using the function (this should take about a minute):

Create a network that uses the image to predict the best affine transformation to apply to the image to extract the digit:

Create a convolutional classification net to use the subimage extracted by the localization net:

Attach the classification network and the localization network to a spatial transformation layer:

Train the network:

If the classification network is removed, the effect of the spatial transformer can be visualized:

Apply the spatial transformer to some images from the validation set:

Obtain the accuracy of the network on the validation set:

Apply an AffineTransform to the coordinates of an image using ImageTransformation:

Construct an equivalent set of parameters for SpatialTransformationLayer: