Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
library(rnn)
library(attention)
## -----------------------------------------------------------------------------
# encoder representations of four different words
word_1 = matrix(c(1,0,0), nrow=1)
word_2 = matrix(c(0,1,0), nrow=1)
word_3 = matrix(c(1,1,0), nrow=1)
word_4 = matrix(c(0,0,1), nrow=1)
## -----------------------------------------------------------------------------
# stacking the word embeddings into a single array
words = rbind(word_1,
word_2,
word_3,
word_4)
## -----------------------------------------------------------------------------
print(words)
## -----------------------------------------------------------------------------
# initializing the weight matrices (with random values)
set.seed(0)
W_Q = matrix(floor(runif(9, min=0, max=3)),nrow=3,ncol=3)
W_K = matrix(floor(runif(9, min=0, max=3)),nrow=3,ncol=3)
W_V = matrix(floor(runif(9, min=0, max=3)),nrow=3,ncol=3)
## -----------------------------------------------------------------------------
# generating the queries, keys and values
Q = words %*% W_Q
K = words %*% W_K
V = words %*% W_V
## -----------------------------------------------------------------------------
# scoring the query vectors against all key vectors
scores = Q %*% t(K)
print(scores)
## -----------------------------------------------------------------------------
weights = attention::ComputeWeights(scores)
## -----------------------------------------------------------------------------
print(weights)
## -----------------------------------------------------------------------------
# computing the attention by a weighted sum of the value vectors
attention = weights %*% V
## -----------------------------------------------------------------------------
print(attention)
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