Single Perceptron Implementation [closed]

Question

Challenge

Train a single perceptron with 2 inputs and 1 output.

Step 1: Initialize the weights

Step 2: Calculate the output

For inputs: [i1, i2, ..., in] and weights: [w1, w2, ..., wn] the output is:

i1 * w1 + i2 * w2 + ... + in * wn

Step 3: Apply activation function on the output (i.e sigmoid)

Step 4: Update the weights

w(t + 1) = w(t) + r * (desired_output - actual_output)

Where r: learning rate

Step 5 Repeat steps 2, 3 and 4

Input

iterations: how many times you repeat steps 2, 3 and 4
input: a list with 2 input values i.e. [1, 0]
output: the desired output
learning_rate: the learning rate i.e.0.3

Output

It should print the last calculated output. Keep in mind this should be very close to the desired output i.e 0.96564545 for desired output 1

Example

For input (training for XOR):

1000, [1 0], 1, 0.3

The output should be:

0.9966304251639512

Note The output will never be the same even for identical test cases due to random weights initialization.

Here's some non-golfed code in Python for this test case:

Try it Online!

Rules

1. The inputs and outputs of the perceptron are fixed to: 2 and 1 respectively.
2. The output needs to be close to the desired output (see example).
3. You can use any activation function you want, just mention it.

Answer 1

JavaScript (Node.js), 121 110 bytes

Same as TFled. A "golfed" version of the example.

-11 bytes from @Arnauld

with(Math)f=(a,[b,B],c,d)=>(g=w=>a--?g(w.map(_=>_+d*(c-(y=1/(1+exp(B*w[1]-b*w[0])))))):y)([random(),random()])

Try it online!

Answer 2

Python 2, 136 bytes

from random import*
import math
def f(n,(i,I),o,r):w,W=random(),random();exec"d=1/(1+math.exp(-i*w-I*W));c=r*(o-d);w+=c;W+=c;"*n;print d

Try it online!

Basically just a golfed version of the example