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Figure 2: CNN structure of the proposed modified AlexNet neural network. SOURCE: Created using the NN-SVG diagramming tool, (www.github.com/alexlenail/NN-SVG).
value or the average value for a group
of adjacent pixels, which causes loss of
information, so it is abandoned because
it could reduce the test accuracy of the
shmoo result types Hole and Marginal.
Additionally, the batch normalization
layer is implemented instead of using
t he d ropout met hod to suppress
overfitting. With batch normalization
the data is standardized in a mini-
batch, with the mean value of the
nor malized data being 0 and the
standard deviation as 1. This process
is similar to dropout, as it “discards”
a part of the nodes where the output is
close to 0 at this layer. It also makes
the output of each layer follow the
same distribution, and so, as a result,
Figure 3: Diagram of a spatial pyramid pooling layer.
eliminates the potential of “parameter
A significant difference between point is processed as a pixel of the explosion” and “parameter attenuation”
AlexNet and this neural network is input shmoo image, so the result of the in the deep network structure training
that all pooling layers except the SPP shmoo is sensitive to every test point. process. This stabilizes and accelerates
are removed. This is because each test The pooling layer takes the maximum the model’s training [5].

Figure 4: Shmoo samples in the training dataset.

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