Based on what I understood that conv2d_grad_wrt_inputs used to apply 
deconvolution graph to derive the input from the activation map so we can 
check the (attention) the most attractive features in he input that 
activated the neurons. But I can't see the idea behind the 
second conv2d_grad_wrt_weights. 

Kindly if I understood wrong or if you explain more I'll grateful. 

Thanks 


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