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\subsection{Tools Used}  We explored the usage of several deep learning packages - Caffe, Tensor Flow(Keras) and Theano (Keras). We initially decided to use Caffe given the extensive availability of popular training Deep-learning models on the platform. However, we had difficulty setting up Caffe to work on local and cloud machines. We were finally able to setup the VGG model on Keras[] with Theano\cite{Bastien-Theano-2012} \cite{Bergstra-Theano-2010}  as the backend. We have been successful in converting the VGG Net model on Caffe to work with Keras and were successful in validating the model with various random test images. \subsection{Approach} 
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We have been able to construct simple gradients of a neuron with respect to the input image. Gradients of neurons at different layers of VGG net are shown below in Fig[].  \label{fig:g1} Gradients of activation of Neuron at Layer 1 of VGG Net  \label{fig:g5} Gradients of activation of Neuron at Layer 5 of VGG Net  \label{fig:g10} Gradients of activation of Neuron at Layer 10 of VGG Net