Gradient descent self code: loss increasing gradually while optimizing -python

Question

I'm trying to estimate the two parameter of the following exponential decay.
but the error(loss) is gradually increasing.

I tried smaller learning_rate, from 10 ^-2 to 10 ^-10
calculate differential again,
tried with different data set.

The parameters didn't bounce. Just steadily changing i.e. increasing or decreasing only.

code and the data is here: https://github.com/psmuler/temp.git

What is wrong with the code? If I change the minus of
tau - dif_tau/len(data), b - dif_b/len(data)
in the line 35 into plus(+), it worked. But surely this is not the solution.
Maybe wrong with the partial differentiation.

or do I just misunderstand the very basis?

Thank you.

If I change the minus of
tau - dif_tau/len(data), b - dif_b/len(data)
in the line 35 into plus(+), it worked. But surely this is not the solution.

I've got tau = 1291.352909 b = 0.14934105 on the data set 1_7, which correspond quite well.

Answer 1

You forgot to put - before time.

def dif_f0_b(time, tau, b):
    return (-1)*math.exp(time/tau) + 1

must be

def dif_f0_b(time, tau, b):
    return (-1)*math.exp(-time/tau) + 1