
Learning OpenCV 3 Computer Vision with Python (Update)
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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.
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_SYM
is not the only one; there is also cv2.ml.ANN_MLP_IDENTITY
and cv2.ml.ANN_MLP_GAUSSIAN
)
You could utilize different training flags (cv2.ml.ANN_MLP_UPDATE_WEIGHTS
, cv2.ml.ANN_MLP_NO_INPUT_SCALE
, cv2.ml.ANN_MLP_NO_OUTPUT_SCALE
), and training methods (back propagation or resilient back propagation)
Aside from that, bear in mind one of the mantras of software development: there is no single best technology...
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