Can I use “softmaxLayer” for regression network?
Can I use “softmaxLayer” for regression network?
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A softmax layer normalizes the input to the layer such that its elements sum up to 1. Therefore, it is useful when you want the network to compute a probability of a classification problem, since it ensures that the sum of the scores over the classes is 1, as requested for a probability measure.
However, it can be used also in combination with other layers as long as it makes sense to normalize the inputs to sum up to 1.
Thus, you can use “softmaxLayer” in MATLAB for your regression problem (although assessing its effectiveness would depend on the problem you are trying to solve at hand).
For example, you can create the following deep learning network:
>> layers = [ ...
>> imageInputLayer([28 28 1]), ...
>> convolution2dLayer(12,25), ...
>> softmaxLayer, ...
>> fullyConnectedLayer(1), ...
>> regressionLayer]Currently, a”softmaxLayer” cannot directly precede a “regressionLayer”, which is why a “fullyConnectedLayer” is between the two in the above example. However, another workaround would be to define a custom regression output layer and a custom softmax layer. If you use a custom layers instead of the layers provided in the Deep Learning Toolbox, you can have a softmax layer immediately before a regression layer.
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Can I use “softmaxLayer” for regression network?
NOTE:-
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.
A softmax layer normalizes the input to the layer such that its elements sum up to 1. Therefore, it is useful when you want the network to compute a probability of a classification problem, since it ensures that the sum of the scores over the classes is 1, as requested for a probability measure.
However, it can be used also in combination with other layers as long as it makes sense to normalize the inputs to sum up to 1.
Thus, you can use “softmaxLayer” in MATLAB for your regression problem (although assessing its effectiveness would depend on the problem you are trying to solve at hand).
For example, you can create the following deep learning network:
>> layers = [ ...
>> imageInputLayer([28 28 1]), ...
>> convolution2dLayer(12,25), ...
>> softmaxLayer, ...
>> fullyConnectedLayer(1), ...
>> regressionLayer]Currently, a”softmaxLayer” cannot directly precede a “regressionLayer”, which is why a “fullyConnectedLayer” is between the two in the above example. However, another workaround would be to define a custom regression output layer and a custom softmax layer. If you use a custom layers instead of the layers provided in the Deep Learning Toolbox, you can have a softmax layer immediately before a regression layer.
SEE COMPLETE ANSWER CLICK THE LINK
