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### #ActualTournicoti

Posted 02 May 2013 - 10:37 AM

Hello

The final output vector will then be –2, 1, –2, 1

You have to pass this to the activation function which is, typically in Hopfield Network :

f(x)=1 if x>0, -1 otherwise

A vector (input/output)  of Hopfield network can only consists in -1 and 1 (At least in discrete model)

Good luck

In the stochastic version, you choose randomly an unit, compute its ouput (integrate inputs + activation function), until you get a stable network state (vector)

### #1Tournicoti

Posted 02 May 2013 - 09:46 AM

Hello

The final output vector will then be –2, 1, –2, 1

You have to pass this to the activation function which is, typically in Hopfield Network :

f(x)=1 if x>0, -1 otherwise

A vector (input/output)  of Hopfield network can only consists in -1 and 1 (At least in discrete model)

Good luck

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