Deep Learning Algorithm Implementations 1.0.0
C++ implementations of fundamental deep learning algorithms
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Function node in the computational graph. More...
#include <autograd.hpp>
Public Member Functions | |
virtual | ~Function ()=default |
virtual Matrix< T > | forward (const std::vector< Variable< T > > &inputs)=0 |
Forward pass computation. | |
virtual std::vector< Matrix< T > > | backward (const Matrix< T > &grad_output)=0 |
Backward pass computation. | |
virtual void | save_for_backward (const std::vector< Matrix< T > > &tensors) |
Set saved tensors for backward pass. | |
Protected Attributes | |
std::vector< Matrix< T > > | saved_tensors_ |
Function node in the computational graph.
Definition at line 25 of file autograd.hpp.
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virtualdefault |
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pure virtual |
Backward pass computation.
grad_output | Gradient from the output |
Implemented in utils::AddFunction< T >, utils::SubFunction< T >, utils::MulFunction< T >, utils::DotFunction< T >, utils::TransposeFunction< T >, utils::SigmoidFunction< T >, and utils::SumFunction< T >.
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pure virtual |
Forward pass computation.
inputs | Input variables |
Implemented in utils::AddFunction< T >, utils::SubFunction< T >, utils::MulFunction< T >, utils::DotFunction< T >, utils::TransposeFunction< T >, utils::SigmoidFunction< T >, and utils::SumFunction< T >.
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inlinevirtual |
Set saved tensors for backward pass.
Definition at line 46 of file autograd.hpp.
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protected |
Definition at line 51 of file autograd.hpp.