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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 were successfully able to convert 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}  Our Approach to the Stochastic Deconvolution problem (described in Section 1) to recreate realistic images backwards from a class label, can be broken down into 3 main steps-  1. \subsubsection{1.  Calcualte Gradients & Deconv for a Neuron`s Activations Activations}  As of now, we have been able to calculate gradients of a downstream neuron`s activations with respect to the input image to visualize the output sensitivity of the neuron with respect to the input image pixels. We are exploring the Keras & Theano package to modify the standard gradient, using approaches similar to guided backprop, in order to better represent effects of different image areas on the activations of neurons in a given layer.  2. Statistical Sampling