How do I check if the weights of my perceptron/step activation function are correct I am new to stack overflow and deep learning so I hope I am doing this the right way. I tried to find the solution myself but it has not been successful so I am seeking some help. This is the situation:
You train a perception with a step activation function to learn the function in this table




input1
input2
output




0
0
0


0
1
1


1
0
0


1
1
0




The following weights approximate the function:
Weight input1 = -1
Weight input2 = 1.3
Bias = -1.0
My question is how do I calculate that this is true? I don't really understand how I can know that these are the correct weights for the numbers in the table. Thank you in advance!

You calculate the forward propagation for each input. The equation is $\sigma(w_1x_1+w_2x_2+b)$, where $\sigma$ is step activation.
$$(x_1,x_2)=(0,0)\rightarrow \sigma(-1\times 0+1.3\times 0-1)= 0$$
$$(x_1,x_2)=(0,1)\rightarrow \sigma(-1\times 0+1.3\times 1-1)= 1$$
$$(x_1,x_2)=(1,0)\rightarrow \sigma(-1\times 1+1.3\times 0-1)= 0$$
$$(x_1,x_2)=(1,1)\rightarrow \sigma(-1\times 1+1.3\times 1-1)= 0$$
As seen above, the weights are correct.
