understanding linear algebra of a forget gate
$begingroup$
This blog covers the basics of LSTMs -- http://colah.github.io/posts/2015-08-Understanding-LSTMs/.
A forget gate is defined as :
At this point the linear algebra confuses me more than it should. The syntax of W*[h,x]
is confusing in this context. I think a vector should go into the activation function since the output f
is a vector, but the syntax of the forget gate above implies that the input has 2 columns because [h,x]
will be an nx2
matrix
For the sake of example lets say ...
W = [ 0 1
2 3]
h = [ -1
2 ]
x = [ 3
0]
b = [ 1
-2 ]
Can anyone give the final vector that goes into the sigmoid function ?
I think the math is
[ 0 1 [ -1 3 [1 [ 2 0 [ 1 something
2 3] * 2 0 ] + -2] = 4 6 ] + -2] = wrong
neural-network lstm rnn
New contributor
$endgroup$
add a comment |
$begingroup$
This blog covers the basics of LSTMs -- http://colah.github.io/posts/2015-08-Understanding-LSTMs/.
A forget gate is defined as :
At this point the linear algebra confuses me more than it should. The syntax of W*[h,x]
is confusing in this context. I think a vector should go into the activation function since the output f
is a vector, but the syntax of the forget gate above implies that the input has 2 columns because [h,x]
will be an nx2
matrix
For the sake of example lets say ...
W = [ 0 1
2 3]
h = [ -1
2 ]
x = [ 3
0]
b = [ 1
-2 ]
Can anyone give the final vector that goes into the sigmoid function ?
I think the math is
[ 0 1 [ -1 3 [1 [ 2 0 [ 1 something
2 3] * 2 0 ] + -2] = 4 6 ] + -2] = wrong
neural-network lstm rnn
New contributor
$endgroup$
add a comment |
$begingroup$
This blog covers the basics of LSTMs -- http://colah.github.io/posts/2015-08-Understanding-LSTMs/.
A forget gate is defined as :
At this point the linear algebra confuses me more than it should. The syntax of W*[h,x]
is confusing in this context. I think a vector should go into the activation function since the output f
is a vector, but the syntax of the forget gate above implies that the input has 2 columns because [h,x]
will be an nx2
matrix
For the sake of example lets say ...
W = [ 0 1
2 3]
h = [ -1
2 ]
x = [ 3
0]
b = [ 1
-2 ]
Can anyone give the final vector that goes into the sigmoid function ?
I think the math is
[ 0 1 [ -1 3 [1 [ 2 0 [ 1 something
2 3] * 2 0 ] + -2] = 4 6 ] + -2] = wrong
neural-network lstm rnn
New contributor
$endgroup$
This blog covers the basics of LSTMs -- http://colah.github.io/posts/2015-08-Understanding-LSTMs/.
A forget gate is defined as :
At this point the linear algebra confuses me more than it should. The syntax of W*[h,x]
is confusing in this context. I think a vector should go into the activation function since the output f
is a vector, but the syntax of the forget gate above implies that the input has 2 columns because [h,x]
will be an nx2
matrix
For the sake of example lets say ...
W = [ 0 1
2 3]
h = [ -1
2 ]
x = [ 3
0]
b = [ 1
-2 ]
Can anyone give the final vector that goes into the sigmoid function ?
I think the math is
[ 0 1 [ -1 3 [1 [ 2 0 [ 1 something
2 3] * 2 0 ] + -2] = 4 6 ] + -2] = wrong
neural-network lstm rnn
neural-network lstm rnn
New contributor
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edited 2 mins ago
sam
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asked 11 mins ago
samsam
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sam is a new contributor. Be nice, and check out our Code of Conduct.
sam is a new contributor. Be nice, and check out our Code of Conduct.
sam is a new contributor. Be nice, and check out our Code of Conduct.
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