Borrar filtros
Borrar filtros

Custom Weighted Classification Layer: Chnage in input value size

4 visualizaciones (últimos 30 días)
I am trying to change the input vlaue size from [1 1 x] to [50 50 x] (here x =1 or 2 or 3 or so on) in weightedClassificationLayer, but its giving error. I need to know where I can do changes so taht this function accept the different input values.
classdef weightedClassificationLayer < nnet.layer.ClassificationLayer
properties
% Row vector of weights corresponding to the classes in the
% training data.
ClassWeights
end
methods
function layer = weightedClassificationLayer(classWeights, name)
% layer = weightedClassificationLayer(classWeights) creates a
% weighted cross entropy loss layer. classWeights is a row
% vector of weights corresponding to the classes in the order
% that they appear in the training data.
% layer = weightedClassificationLayer(classWeights, name)
% additionally specifies the layer name
% Set class weights.
layer.ClassWeights = classWeights;
% Set layer name.
if nargin == 2
layer.Name = name;
end
% Set layer description
layer.Description = 'Weighted cross entropy';
end
function loss = forwardLoss(layer, Y, T)
% loss = forwardLoss(layer, Y, T) returns the weighted cross
% entropy loss between the predictions Y and the training
% targets T.
N = size(Y,4);
Y = squeeze(Y);
T = squeeze(T);
W = layer.ClassWeights;
loss = -sum(W*(T.*log(Y)))/N;
end
function dLdY = backwardLoss(layer, Y, T)
% dLdX = backwardLoss(layer, Y, T) returns the derivatives of
% the weighted cross entropy loss with respect to the
% predictions Y.
[H,Wi,K,N] = size(Y);
Y = squeeze(Y);
T = squeeze(T);
W = layer.ClassWeights;
dLdY = -(W'.*T./Y)/N;
dLdY = reshape(dLdY,[H Wi K N]);
end
end
end
to check the validity of this layer
classWeights = [0 1]
Size=size(classWeights)
layer = weightedClassificationLayer(classWeights);
numClasses = numel(classWeights)
validInputSize = [1 1 numClasses]
checkLayer(layer,validInputSize, 'ObservationDimension',4);
this works on Valid input size =[1 1 numClasses] but I am trying to change it to [x x numClasses] (x any number greater than 1)

Respuesta aceptada

Divya Gaddipati
Divya Gaddipati el 22 de Oct. de 2019
As I understand, you want to change the validInputSize to “[x x numClasses]”, which implies a single prediction will be of size [x x 1]. Hence, you also need to reshape your classWeights (which is W in your code) to [x x numClasses].
Modified your code below:
classdef weightedClassificationLayer < nnet.layer.ClassificationLayer
properties
% Row vector of weights corresponding to the classes in the
% training data.
ClassWeights
end
methods
function layer = weightedClassificationLayer(classWeights, name)
% layer = weightedClassificationLayer(classWeights) creates a
% weighted cross entropy loss layer. classWeights is a row
% vector of weights corresponding to the classes in the order
% that they appear in the training data.
% layer = weightedClassificationLayer(classWeights, name)
% additionally specifies the layer name
% Set class weights.
layer.ClassWeights = classWeights;
% Set layer name.
if nargin == 2
layer.Name = name;
end
% Set layer description
layer.Description = 'Weighted cross entropy';
end
function loss = forwardLoss(layer, Y, T)
% loss = forwardLoss(layer, Y, T) returns the weighted cross
% entropy loss between the predictions Y and the training
% targets T.
N = size(Y,4);
Y = squeeze(Y);
T = squeeze(T);
W = layer.ClassWeights;
%% Modified %%
n = length(T(:))/2;
W = repelem(W, 1, [n n]);
W = reshape(W, size(T));
prod = W.*(T.*log(Y));
loss = -sum(prod(:))/N;
%% Modified %%
end
function dLdY = backwardLoss(layer, Y, T)
% dLdX = backwardLoss(layer, Y, T) returns the derivatives of
% the weighted cross entropy loss with respect to the
% predictions Y.
[H,Wi,K,N] = size(Y);
Y = squeeze(Y);
T = squeeze(T);
W = layer.ClassWeights;
%% Modified %%
n = length(T(:))/2;
W = repelem(W, 1, [n n]);
W = reshape(W, size(T));
%% Modified %%
dLdY = -(W.*T./Y)/N;
dLdY = reshape(dLdY,[H Wi K N]);
end
end
end
  5 comentarios
Divya Gaddipati
Divya Gaddipati el 22 de Oct. de 2019
Generally, such large sizes (like 256, 512) are not recommended to use for checkLayer. To speed up the tests, specify a smaller valid input size.
Raza Ali
Raza Ali el 22 de Oct. de 2019
For my case the data from the upper layer will be in this dimension (256x256x3 or 512x512x3) I cannot change the dimensions.
any other psossiblity to entertain the images of these size?

Iniciar sesión para comentar.

Más respuestas (0)

Categorías

Más información sobre Image Data Workflows en Help Center y File Exchange.

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by