Hi,
You can create a custom layer in MATLAB to achieve this. Here's an example implementation:
classdef ReshapeLayer < nnet.layer.Layer
properties
InputDimLabels = {'CBT'} % input dimension labels
OutputDimLabels = {'SCBT'} % output dimension labels
end
methods
function layer = ReshapeLayer(name)
layer.Name = name;
layer.Description = "Reshape Layer";
end
function Z = predict(layer, X)
% Reshape input data to output shape
Z = reshape(X, [], size(X,3));
end
function [dLdX] = backward(layer, X, Z, dLdZ, ~)
% Reshape gradients back to input shape
dLdX = reshape(dLdZ, size(X));
end
function outputSize = forwardPropagateSize(layer, inputSize)
% Output size calculation based on input size and output dimension labels
outputSize = [prod(inputSize(1:end-1)), length(layer.OutputDimLabels)];
end
function inputSize = backwardPropagateSize(layer, outputSize)
% Input size calculation based on output size and input dimension labels
inputSize = [outputSize(1:end-1), length(layer.InputDimLabels)];
end
function outputNames = getOutputArguments(layer)
% Output argument names based on output dimension labels
outputNames = layer.OutputDimLabels;
end
function inputNames = getInputArguments(layer)
% Input argument names based on input dimension labels
inputNames = layer.InputDimLabels;
end
function outputSize = getOutputSize(layer, ~)
% Output size based on output dimension labels
outputSize = [NaN, length(layer.OutputDimLabels)];
end
function inputSize = getInputSize(layer)
% Input size based on input dimension labels
inputSize = [NaN, length(layer.InputDimLabels)];
end
function tf = isValidInputSize(layer, inputSize)
% Check if input size is compatible with input dimension labels
tf = isequal(inputSize(end), length(layer.InputDimLabels));
end
function tf = isValidOutputSize(layer, outputSize)
% Check if output size is compatible with output dimension labels
tf = isequal(outputSize(end), length(layer.OutputDimLabels));
end
end
end
You can use this layer in your neural network by creating an instance of the layer and adding it to the network:
reshapeLayer = ReshapeLayer('reshape_layer');
layers = [
imageInputLayer([32 32 3], 'Name', 'input', 'Normalization', 'none', 'DataAugmentation', 'none', 'DimensionLabels', {'Height', 'Width', 'Channels'})
reshapeLayer
fullyConnectedLayer(10, 'Name', 'fc')
softmaxLayer('Name', 'softmax')
classificationLayer('Name', 'output')
];
lgraph = layerGraph(layers);
