How to train CNN with an image in the input and an image in the output?

76 Ansichten (letzte 30 Tage)
I have a task to train CNN with an image as input and an image as output. I have tried to do it at the beginning with Matlab tutorial, but matlab has no image as output, but a vector.
any idea how i can do this tutorial again but with image as output?
For example you could insert input as image for number one and the output is also for number one but rotated or deformed.
Please don't send me other people's question, because i have seen all answers about it.
  6 Kommentare
Osama Tabbakh
Osama Tabbakh am 15 Aug. 2020
Hello Ritesh,
Unfortunately I haven't done it in Python yet. But I wish you all the best with it, good luck.
Florent Busnoult
Florent Busnoult am 25 Aug. 2020
Bearbeitet: Florent Busnoult am 25 Aug. 2020
There is an example in the 5G toolbox that uses a CNN network with a picture as an input and a picture as an output.
The variable used to store the "labels" or (ouput images) is the following:
trainLabels(:,:,:,i) = est_grid;
It's a 4-D double variable.
And you can look at the different output images used to trian the network by using the command below:
>> imagesc(abs(trainLabels(:,:,:,2))); % output image number 2
>> imagesc(abs(trainLabels(:,:,:,3))); % output image number 3
>> imagesc(abs(trainLabels(:,:,:,25))); % output image number 25
and so on.

Melden Sie sich an, um zu kommentieren.

Akzeptierte Antwort

Vasilis Giannoglou
Vasilis Giannoglou am 16 Sep. 2020
I was also struggling with having an image as an output and I found the solution. Skip the fullyConnectedLayer(n) part, because it causes the network to have an output of either a vector or just a number. However, make sure not to include any Pooling layers if you want your output to be of the same size as your input, because they cause downsampling. Finally, keep in mind that the number of filters of your final conv network is the same number as the 3rd dimension of your output image. For example,
convolution2dLayer(3,1,'Padding','same') gives an output image of (M,N,1), where M,N are the rows and columns of the initial image. The next code runs to me.
layers = [
imageInputLayer([64 64 1]) % My initial image is 64x64
convolution2dLayer(3,8,'Padding','same')
batchNormalizationLayer
reluLayer
% averagePooling2dLayer(2,'Stride',1) % Don't want this
convolution2dLayer(3,16,'Padding','same')
batchNormalizationLayer
reluLayer
% averagePooling2dLayer(2,'Stride',1) % Don't want this
convolution2dLayer(3,32,'Padding','same')
batchNormalizationLayer
reluLayer
convolution2dLayer(3,1,'Padding','same') % Because my initial image 3rd dimension is one.
batchNormalizationLayer
reluLayer
dropoutLayer(0.2)
% fullyConnectedLayer(10) % Don't want this
regressionLayer];
  2 Kommentare
mary john
mary john am 20 Mai 2022
I tried to do this and gets an error with the regression layer. It states that the ''output size does not match response size", when I use the deep network designer.
Can you please help me to solve. Thanks in advance

Melden Sie sich an, um zu kommentieren.

Weitere Antworten (1)

Johanna Pingel
Johanna Pingel am 29 Apr. 2019
  3 Kommentare
supriya Naik
supriya Naik am 28 Aug. 2020
Did you get the solution sir?? Actually I am facing same problem now.

Melden Sie sich an, um zu kommentieren.

Produkte


Version

R2018b

Community Treasure Hunt

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

Start Hunting!

Translated by