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AD in Rust
So far we have only worked on forward mode autodiff. However, to implement backprop and build some simple machine learning application we'll need to implement some reverse mode autodiff.
For now I'd like create a new module to follow with this example.
The implementation requires a Variable
and a TapeEntry
class:
classDiagram
Variable ..> TapeEntry
GradientTape ..> TapeEntry
class Variable {
+String name
+f32 value
+add(Variable) Variable
+mul(Variable) Variable
}
class TapeEntry {
+Vec[&Variable] inputs
+Vec[&Variable] outputs
+Fn propagate
}
class GradientTape {
+Vec<TapeEntry>
+add_entry(TapeEntry)
+clear()
}
Additional some functions need to be implemented:
reset_tape
: reset gradient tapegrad
: calculate gradientsNote
This is only a rough outline for now. I will refine the issue as the development proceeds.
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