Input and output size for deep learning regression

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mh.ameen - 2021-05-27T19:49:22+00:00
Question: Input and output size for deep learning regression

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.

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    • Expert Answer

    Profile picture of Prashant Kumar Prashant Kumar answered . 2025-11-20

    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.


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