Comments (2)
found the issue, will update later
from lipnet.
that sudden drop was because of the epsilon value in Adam optimizer's update function
t <- t + 1
lr_t <- learning_rate * sqrt(1 - beta2^t) / (1 - beta1^t)
m_t <- beta1 * m_{t-1} + (1 - beta1) * g
v_t <- beta2 * v_{t-1} + (1 - beta2) * g * g
variable <- variable - lr_t * m_t / (sqrt(v_t) + epsilon)
that epsilon value is 10^-8 by default, and therefore the negative part becomes very high when sqrt(v_t) is very low and epsilon is low, so to avoid that take epsilon value = [0.1, 0.01, 0.001]
from lipnet.
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from lipnet.