```
import math

inp = 0.1
target = 0.3
weight = 0.04
learning_rate = 1
bias = 0

def sigmoid(x):
    return (1/1+(math.e**(-x)))

for count in range(10):
    net = (weight*inp)+(bias*1)
    out = sigmoid(net)
    error_total = 0.5*((target - out)**2)
    print('error',error_total,'|| output',out,'|| weight',weight)
    adjustment = (out - target)*(out)*(1 - out)*(inp)
    weight = weight - (learning_rate*(adjustment))

```

output

```
error 1.4382215499593243 || output 1.9960079893439915 || weight 0.04
error 1.3827597601324302 || output 1.9629851232842885 || weight 0.3771731560625728
error 1.3336445885853887 || output 1.9331837548698485 || weight 0.6915314696982848
error 1.2897634204261337 || output 1.9060905456580794 || weight 0.9861603791287348
error 1.2502583453938265 || output 1.8813022136162503 || weight 1.2635467711722324
error 1.2144557693222424 || output 1.8584965635651831 || weight 1.5257260148874747
error 1.1818184468701014 || output 1.8374124019729394 || weight 1.7743861541249517
error 1.1519119478941984 || output 1.8178352663541577 || weight 2.0109434853536103
error 1.1243806861957226 || output 1.7995870672926748 || weight 2.2365985045783425
error 1.0989304444985601 || output 1.7825184278777517 || weight 2.4523780684160923

```

in my neural network i want to predict a single output on single input
i tryed by setting bias and learning rate to different values but of no use

and the weights goes on increasing and error rate decreasing but the network was unable to reach target output