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Input and output size for deep learning regression

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SA Yoganathan
SA Yoganathan on 17 Mar 2020
Answered: Uttiya Ghosh on 18 Jun 2020
Hi everyone,
I have the following input and target matrix
Input: 110 samples of 273x262
Target: 110 samples of 273x262
I have to work on deep learning regression problem with a simple layers as shown below
Layer: [imageInputLayer()
convolution2dLayer(5,16,'Padding','same')
batchNormalizationLayer
reluLayer
fullyConnectedLayer()
regressionLayer]
What is the matrix size I have to use for the inputlayer and fullyconnectedlayer?
I am thinking of 4D matrix of size [273, 262, 1, 110] for inputlayer and a 2D matrix of size [273*263, 110] for output layer.
Is this correct? Will this exceed the matrix array size preference? Any other suggestions. Thank you

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Answers (1)

Uttiya Ghosh
Uttiya Ghosh on 18 Jun 2020
Hi SA,
From my understanding, you are working with grayscale images on a deep learning regression model. You are expecting a output in the form of a matrix for each image and not a single valued scalar output.
For imageInputLayer, size of the input data is specified as a row vector of integers [h w c], where h, w, and c correspond to the height, width, and number of channels respectively. You do not need to specify the number of samples. Hence, as per my understanding, the inputSize should be a row vector [273, 262, 1].
For fullyConnectedLayer, output size must be a positive integer. You shall not specify the sample size here as well. Hence as per my understanding, the outputSize should be 273*262.
For more information, refer to the following links.

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