Data Representation for sequential input NN
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The essence of the problem I want to model is to go from input sequences(length n) to a "distance matrix"(nxn). Although for this distance matrix we are not actually predicting $n^2$ values because it is symmetric, so only $frac{n(n-1)}{2}$ values.
I am trying to train a NN on this, but the problem I am running into is these sequences and matrices are of variable length. I can prepad the sequence so that they are all of the same length, but then I do not know what to do with the output... padding the matrix in multiple dimensions doesnt seem like it'd make for a great model.
Any ideas would be appreciated.
deep-learning data
asked 19 hours ago
gtogto
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The essence of the problem I want to model is to go from input sequences(length n) to a "distance matrix"(nxn). Although for this distance matrix we are not actually predicting $n^2$ values because it is symmetric, so only $frac{n(n-1)}{2}$ values.
I am trying to train a NN on this, but the problem I am running into is these sequences and matrices are of variable length. I can prepad the sequence so that they are all of the same length, but then I do not know what to do with the output... padding the matrix in multiple dimensions doesnt seem like it'd make for a great model.
Any ideas would be appreciated.
deep-learning data
asked 19 hours ago
gtogto
1
New contributor
gto is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
The essence of the problem I want to model is to go from input sequences(length n) to a "distance matrix"(nxn). Although for this distance matrix we are not actually predicting $n^2$ values because it is symmetric, so only $frac{n(n-1)}{2}$ values.
I am trying to train a NN on this, but the problem I am running into is these sequences and matrices are of variable length. I can prepad the sequence so that they are all of the same length, but then I do not know what to do with the output... padding the matrix in multiple dimensions doesnt seem like it'd make for a great model.
Any ideas would be appreciated.
deep-learning data
asked 19 hours ago
gtogto
1
New contributor
gto is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
The essence of the problem I want to model is to go from input sequences(length n) to a "distance matrix"(nxn). Although for this distance matrix we are not actually predicting $n^2$ values because it is symmetric, so only $frac{n(n-1)}{2}$ values.
I am trying to train a NN on this, but the problem I am running into is these sequences and matrices are of variable length. I can prepad the sequence so that they are all of the same length, but then I do not know what to do with the output... padding the matrix in multiple dimensions doesnt seem like it'd make for a great model.
Any ideas would be appreciated.
deep-learning data
deep-learning data
asked 19 hours ago
gtogto
1
New contributor
gto is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 19 hours ago
gtogto
1
New contributor
gto is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 19 hours ago
gtogto
1
New contributor
gto is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 19 hours ago
gtogto
1
asked 19 hours ago
gtogto
1
1
New contributor
gto is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
gto is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
gto is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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