13 lines
812 B
Plaintext
13 lines
812 B
Plaintext
Sometimes the training phase of the network breaks in the middle. It happens
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because the synaptical weights are initialized with random values, and
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sometimes updating them causes those values to become >= 1. This makes the
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output values of the network diverge instead of converging to the desired,
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expected values. The library recognizes this behaviour, and when a weight
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become >= 1 throws an InvalidSynapticalWeightException. So far there's no
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way to prevent this odd, random behaviour. The network implements the usage
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of an inertial momentum coefficient to avoid strong oscillations in the
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training phase, in order to make this phenomenon rarer, but also using this
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mechanism there's a possibility ~ 10% of getting a diverging network, and
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so a training phase broken by an InvalidSynapticalWeightException.
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