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.
ANSWER
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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].
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Matlabsolutions.com provide latest MatLab Homework Help,MatLab Assignment Help for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil with 100% output.Matlab Code for B.E, B.Tech,M.E,M.Tech, Ph.D. Scholars with 100% privacy guaranteed. Get MATLAB projects with source code for your learning and research.
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].
SEE COMPLETE ANSWER CLICK THE LINK
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