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License: Other
Deep Learning Framework with a specialisation aimed for Binarized Neural Networks.
License: Other
This issue aims at specifying the workflow and discussing the design of the framework for BNN.
If you have anything to suggest about the modules and their organization then please comment below.
This issue aims to discuss and result in a stable API for Tensors. Taking references from already exisiting Tensor
in tensorflow
and torch
is required.
This issue aims at discussing design of automatic differentiation(AD).
Basically, AD has two modes, Forward Mode and Reverse Mode. We plan to implement both of them as they have their own pros and cons.
In the context of this project, the function to be differentiated will be loss function whose variables will be the weights of the network. All of the quantities i.e., weights, inputs and output will be tensors.
Forward Mode
This mode computes the derivative alongside computing the value of the loss function. For each variable under consideration a derivative component is associated with it. Then chain rule is applied in each step of the computation.
Reverse Mode
This is similar to back propagation. Here the computation graph is formed in the forward pass and the derivative of the loss function w.r.t all the variables is calculated in reverse pass.
With multiple variables, when the gradient is overwritten for the next variable the previous gradient pointer should be cleared from heap memory. Currently, it leads to a memory leak.
BNN/bnn/autodiff/forward_impl.cpp
Lines 50 to 56 in 3d752fe
APIs for models(train, test) need to be discussed in the issue. A simple use case for starting,
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