Constructs a step rate policy.
the rate at first iteration
The rate at the first iteration.
Learning rate retention per step; - 0 < gamma < 1 - large gamma CAN cause networks to never converge, low gamma CAN cause networks to converge too quickly
Calculates the current training rate.
count
the current training rate
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Exponential Learning Rate
The learning rate will exponentially decrease.
The rate at
iterationis calculated as:rate = base_rate * Math.pow(gamma, iteration)