February 2019
Intermediate to advanced
260 pages
6h 3m
English
Content preview from Hands-On Java Deep Learning for Computer Vision
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Object localization prediction layer
Once the data has been labeled, we need to adopt the prediction layer. Let's look at the following flow chart:
If we consider the image-classifier example used in the previous section, we label the image and depict different images as belonging to different classes.
All of this information is fed to the neural network, which passes through the prediction layer using softmax, and this outputs the probabilities for each class.
The neural network will map the output as 65% chances if it being class 0, 15% chance of the image being class 1, and so on.
During the process of training, we calculate the difference ...
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