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#Actualysg

Posted 02 May 2013 - 08:27 AM

Hello, I'm reading a book by Jeff Heaton where he talks about artificial neural networks in C#, the 2nd edition.

I got to this part:

-snip-

We must now compare those weights with the input pattern of 0101:

0 1 0 1

0 -1 1 -1

We will sum only the weights corresponding to the positions that contain a 1 in the input pattern. Therefore, the activation of the first neuron is –1 + –1, or –2. The results of the activation of each neuron are shown below.

N1 = -1 + -1 = -2
N2 = 0 + 1 = 1
N3 = -1 + -1 = -2
N4 = 1 + 0 = 1

Therefore, the output neurons, which are also the input neurons, will report the above activation results. The final output vector will then be –2, 1, –2, 1. These val- ues are meaningless without an activation function. We said earlier that a threshold establishes when a neuron will fire. A threshold is a type of activation function. An activation function determines the range of values that will cause the neuron, in this case the output neuron, to fire. A threshold is a simple activation function that fires when the input is above a certain value.

-snip-

What I don't understand is how and why does this addition happen? How does the logic flow in this case? Why do you add together only the negative numbers? Having a hard time visualizing this logic.

If anyone could provide some input, I'd greatly appreciate it.

The part that's causing me to scratch my head is on page 88 of the book, if that helps.

#1ysg

Posted 02 May 2013 - 08:24 AM

Hello, I'm reading a book by Jeff Heaton where he talks about artificial neural networks in C#, the 2nd edition.

I got to this part:

-snip-

We must now compare those weights with the input pattern of 0101:

0 1 0 1

0 -1 1 -1

We will sum only the weights corresponding to the positions that contain a 1 in the input pattern. Therefore, the activation of the first neuron is –1 + –1, or –2. The results of the activation of each neuron are shown below.

N1 = -1 + -1 = -2
N2 = 0 + 1 = 1
N3 = -1 + -1 = -2
N4 = 1 + 0 = 1

Therefore, the output neurons, which are also the input neurons, will report the above activation results. The final output vector will then be –2, 1, –2, 1. These val- ues are meaningless without an activation function. We said earlier that a threshold establishes when a neuron will fire. A threshold is a type of activation function. An activation function determines the range of values that will cause the neuron, in this case the output neuron, to fire. A threshold is a simple activation function that fires when the input is above a certain value.

-snip-

What I don't understand is how and why does this addition happen? How does the logic flow in this case? Why do you add together only the negative numbers?

If anyone could provide some input, I'd greatly appreciate it.

The part that's causing me to scratch my head is on page 88 of the book, if that helps.

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