quiz3_2008

# Z 1 1 e z so for the output layer of a sigmoid neural

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Unformatted text preview: z ∂wi Substituting (3) and (4) into (2): (5) ∂P ∂P ∂y i = = ( y * − y ) y (1 − y ) xi ∂wi ∂yi ∂wi Substituting (5) into the weight equation (1): wi' = wi + r( y * − y ) y (1 − y ) xi ... which can be written in terms of δ, which is the derivative of P with respect to total input, ∂P ∂y = ( y * − y ) y (1 − y ) = y (1 − y )( y * − y ) wi ∂y ∂z xi ∂P into this equation.) (Note that to change P, one only needs to substitute the new ∂y wi' = wi + rδixi , where δi = ∂P : ∂z δi yi For an inner node, we use a similar equation that takes into account the output yi for that node, and the δ and w values for the next layer, where &quot;next layer&quot; means the neighboring nodes one layer closer to the output layer: wij wi δi y = x δj y y h = xi j i j wi' = wi + rδixi , where δi = yi(1 − yi) δjwij ∑ and wij is the weight between layers i and j. ... Summary: For each layer, wi' = wi + rδixi , where for outer layer δi = y (1 − y )( y * − y ) ∑δ w inner layer δi = yi(1 − yi) j ij 11...
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## This document was uploaded on 03/17/2014 for the course EECS 6.034 at MIT.

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