14.5 How shares become a mixture

Content and key concepts

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The share of attention multiplys each of the coordinates of the transmitted value. Then the deposits of all authorized items are added.
The request is compared to the keys; the fractions are weighted by the values transferred. The future is not available when predicting continuation. Several heads make independent comparisons.
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Each member multiplys the whole value vector.
After A, we compute O=AV. Shapes: [n,n]×[n,d_v]=[n.d_V]. For output coordinates:
O
i
r
=
∑
j
A
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j
V
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O_{ir}=\sum_j A_{ij}V_{jr}.
O
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r
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=
j
∑
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A
ij
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V
j
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The same co-officent Aij is involved in all co-ordinates value_j. This is not the choice of a single maximum element of each co-location.
In default, the third row A has a species of[a,a,b] where a=1/(2+exp(1/G2)),b=1-2a.Values[1.0],[0.1], [1.1], therefore, output[a+b,a+B]. Each contribution can be checked separately: at the first co-ordinate of a×1+a×0+bx1.
Experiment.
Select query and change only W_V. The table co-officents should be retained and outputs should change. With equal valutes, all their normalised mixtures are consistent regardless of spores.
Boundary.
Until the exit of the projection, the mixture lies in the bulge of values at these non-negative normalized coefficients. After W_O and residual, this geometric restriction is no longer directly relevant to the final hidden state.
from math import exp,sqrt
a=1/(2+exp(1/sqrt(2))); b=1-2*a
v=[[1,0],[0,1],[1,1]]; w=[a,a,b]
out=[sum(w[j]*v[j][r] for j in range(3)) for r in range(2)]
assert abs(out[0]-(a+b))<1e-12 and out[0]==out[1]
The code repeats the operations just taken care of.
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Then, the deposits in each coordinate are summarized by j.
Several separate ways to gather information. How does this question continue?
Python examples of Chapter 14
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