README created online with Bitbucket

README

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+https://bitbucket.org/rshegde/deepmie-deep-learning-electromagnetic-scattering
+
+Abstract
+===============================================================
+We propose the use of Deep Convolutional Neural Networks (DCNN)
+  in concert with Differential Evolution for solving inverse problems in
+  electromagnetics.  We show that networks built using innovations like
+  convolutional layers, batch normalization, parametric REctified Linear Unit
+  (ReLU) and residual blocks achieve comparable performance to previously
+  reported dense fully-connected networks but achieve drastic reduction in
+  parameter weights and training epoch lengths. The proposed lightweight CNNs
+  are used in concert with Differential Evolution global optimization to achieve
+  8x speedup in convergence time. Systematic hyperparameter grid searches are
+  reported to permit faster model discovery for application to other problems.
+
+See the following arxiv paper for details
+
+
+Requirements
+================================================================
+1) CUDA capable GPU 
+2) Python 3.5
+3) Numpy, Matplotlib, Pandas, Scipy, Scikitlearn
+4) Cudatooklit 9.0, CuDNN 7.1
+5) Mxnet-GPU, Mxnet-cumkl
+6) Keras, Keras-mxnet
+7) Scattnlay -- needed if you want to generate other datasets
+8) Jupyter notebook 
+9) ipyparallel for calling multiple runs at the same time
+
+Getting started
+==================================
+Most of the code is placed in Jupyter notebooks for interactivity. 
+PyMIEtest.ipynb   --  familiarizes you with generating datasets
+Scatternets.ipynb -- allows building and visualizing models
+hypoptim.ipynb -- hyperparameter grid search using scikit CV grid search
+scipydiffe -- differential evolution using compiled models. 
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