How to visualize the predicted values of segmentation from softmax output?

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Raza Ali
Raza Ali 2020 年 9 月 11 日
コメント済み: Mahesh Taparia 2020 年 9 月 16 日
I want to know (visualize) what value, network has predicted during training.
i used Dice pixel classfication layer to observe this but instead of predicted value it shows the original image. Its written that T is target which means T is ground truth and Y is predicted which means predicted pixels.
Code:
classdef dicePixelClassificationLayer < nnet.layer.ClassificationLayer
% This layer implements the generalized Dice loss function for training
% semantic segmentation networks.
properties(Constant)
% Small constant to prevent division by zero.
Epsilon = 1e-8;
end
methods
function layer = dicePixelClassificationLayer(name)
% layer = dicePixelClassificationLayer(name) creates a Dice
% pixel classification layer with the specified name.
% Set layer name.
layer.Name = name;
% Set layer description.
layer.Description = 'Dice loss';
end
function loss = forwardLoss(layer, Y, T)
% loss = forwardLoss(layer, Y, T) returns the Dice loss between
% the predictions Y and the training targets T.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
code used to visualize the predictions Y and the training targets T
T1=T(:,:,1);
Y1=Y(:,:,1);
subplot(1,2,1)
imshow(T1)
subplot(1,2,2)
imshow(Y1)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Weights by inverse of region size.
W = 1 ./ sum(sum(T,1),2).^2;
intersection = sum(sum(Y.*T,1),2);
union = sum(sum(Y.^2 + T.^2, 1),2);
numer = 2*sum(W.*intersection,3) + layer.Epsilon;
denom = sum(W.*union,3) + layer.Epsilon;
% Compute Dice score.
dice = numer./denom;
% Return average Dice loss.
N = size(Y,4);
loss = sum((1-dice))/N;
end
function dLdY = backwardLoss(layer, Y, T)
% dLdY = backwardLoss(layer, Y, T) returns the derivatives of
% the Dice loss with respect to the predictions Y.
% Weights by inverse of region size.
W = 1 ./ sum(sum(T,1),2).^2;
intersection = sum(sum(Y.*T,1),2);
union = sum(sum(Y.^2 + T.^2, 1),2);
numer = 2*sum(W.*intersection,3) + layer.Epsilon;
denom = sum(W.*union,3) + layer.Epsilon;
N = size(Y,4);
dLdY = (2*W.*Y.*numer./denom.^2 - 2*W.*T./denom)./N;
end
end
end
left is groundtruth and right side is origianl image, which supoosed to be predicted one. now how can i see the predicted one?

採用された回答

Mahesh Taparia
Mahesh Taparia 2020 年 9 月 15 日
Hi
The softmax layer gives the probability of the predicted class. To get the segmented result from that, you can put the probabilistic threshold of 0.5. For example:
P=T1(:,:,1)>0.5;
Else, in order to evaluate the segmented result, you can use the 'semanticseg' function. For more information, you can refer this documentation. Hope it wil help!
  2 件のコメント
Raza Ali
Raza Ali 2020 年 9 月 16 日
Thank you for the answer.
What is Y and T?
defined in loss fucntion? Are they ground truth (T) and predicted values (Y)?
Mahesh Taparia
Mahesh Taparia 2020 年 9 月 16 日
Yes, they are ground truth (T) and predicted values (Y).

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