September 2015
Intermediate to advanced
266 pages
5h 38m
English
Content preview from Learning OpenCV 3 Computer Vision with Python (Update)Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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Possible improvements and potential applications
We have illustrated how to build an ANN, feed it training data, and use it for classification. There are a number of aspects we can improve, depending on the task at hand, and a number of potential applications of our new-found knowledge.
Improvements
There are a number of improvements that can be applied to this approach, some of which we have already discussed:
- For example, you could enlarge your dataset and iterate more times, until a performance peak is reached
- You could also experiment with the several activation functions (
cv2.ml.ANN_MLP_SIGMOID_SYMis not the only one; there is alsocv2.ml.ANN_MLP_IDENTITYandcv2.ml.ANN_MLP_GAUSSIAN) - You could utilize different training flags (
cv2.ml.ANN_MLP_UPDATE_WEIGHTS ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.