How to retain dependency between variables in PyTorch?
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I am modeling k-dimensional positions over time t = 0...T using a set of initial positions Z0 with requires_grad=True and storing the results in Z with requires_grad=False for the remaining T-1 time steps.
A simple model is
Zt = Zt-1 + e
where e is some constant noise.
Which is optimized in PyTorch using gradient descent, by moving the initial positions accordingly.
The problem is, when using Z to compute subsequent time steps for t > 1, the relation between Zt and Z0 is lost, such that the model converges significantly slower opposed to simply modeling
Zt = Z0 + t * e, where the dependency between initial positions and Zt is retained.
Note: This model is for illustrative purposes only, such that the models in question are too complex to be defined in terms of Z0, requiring the intermediary results of Z.
Accumulating gradients or retaining gradient graph does not help.
machine-learning python gradient-descent pytorch
asked yesterday
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add a comment |
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I am modeling k-dimensional positions over time t = 0...T using a set of initial positions Z0 with requires_grad=True and storing the results in Z with requires_grad=False for the remaining T-1 time steps.
A simple model is
Zt = Zt-1 + e
where e is some constant noise.
Which is optimized in PyTorch using gradient descent, by moving the initial positions accordingly.
The problem is, when using Z to compute subsequent time steps for t > 1, the relation between Zt and Z0 is lost, such that the model converges significantly slower opposed to simply modeling
Zt = Z0 + t * e, where the dependency between initial positions and Zt is retained.
Note: This model is for illustrative purposes only, such that the models in question are too complex to be defined in terms of Z0, requiring the intermediary results of Z.
Accumulating gradients or retaining gradient graph does not help.
machine-learning python gradient-descent pytorch
asked yesterday
HelgeHelge
1
New contributor
Helge 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 |
$begingroup$
I am modeling k-dimensional positions over time t = 0...T using a set of initial positions Z0 with requires_grad=True and storing the results in Z with requires_grad=False for the remaining T-1 time steps.
A simple model is
Zt = Zt-1 + e
where e is some constant noise.
Which is optimized in PyTorch using gradient descent, by moving the initial positions accordingly.
The problem is, when using Z to compute subsequent time steps for t > 1, the relation between Zt and Z0 is lost, such that the model converges significantly slower opposed to simply modeling
Zt = Z0 + t * e, where the dependency between initial positions and Zt is retained.
Note: This model is for illustrative purposes only, such that the models in question are too complex to be defined in terms of Z0, requiring the intermediary results of Z.
Accumulating gradients or retaining gradient graph does not help.
machine-learning python gradient-descent pytorch
asked yesterday
HelgeHelge
1
New contributor
Helge is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I am modeling k-dimensional positions over time t = 0...T using a set of initial positions Z0 with requires_grad=True and storing the results in Z with requires_grad=False for the remaining T-1 time steps.
A simple model is
Zt = Zt-1 + e
where e is some constant noise.
Which is optimized in PyTorch using gradient descent, by moving the initial positions accordingly.
The problem is, when using Z to compute subsequent time steps for t > 1, the relation between Zt and Z0 is lost, such that the model converges significantly slower opposed to simply modeling
Zt = Z0 + t * e, where the dependency between initial positions and Zt is retained.
Note: This model is for illustrative purposes only, such that the models in question are too complex to be defined in terms of Z0, requiring the intermediary results of Z.
Accumulating gradients or retaining gradient graph does not help.
machine-learning python gradient-descent pytorch
machine-learning python gradient-descent pytorch
asked yesterday
HelgeHelge
1
New contributor
Helge is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
HelgeHelge
1
New contributor
Helge is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
HelgeHelge
1
New contributor
Helge is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
HelgeHelge
1
asked yesterday
HelgeHelge
1
1
New contributor
Helge is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Helge is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Helge is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
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