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A Spiking Neural Network Architecture for MNIST Digit Recognition
This project forked from saunak1994/snnformnist
A Spiking Neural Network Architecture for MNIST Digit Recognition
Requires "mnist_train.csv" and "mnist_test.csv" . These are the original MNIST (http://yann.lecun.com/exdb/mnist/) datasets in a csv format. Available at: https://drive.google.com/open?id=1b_uP0fMelgH_RD9ODNoLw5OR44l9ovya https://drive.google.com/open?id=1gbLr38NHArKWIRG27O6tAo_-y9VLI98Y #----------------------------------------------------------------- # Parameter Generator #----------------------------------------------------------------- To run code: $ python paramGenerator.py <parameter name> <start_value> <end_value> <step_value> This will generate parameter files having default parameter values and concerned parameter ranging from start value to end value (both included) each separated by the step value and output the number of simulations required to loop over all the files. #----------------------------------------------------------------- # Spiking Neural Network Trainer #----------------------------------------------------------------- To run code: $python SNNTrainRoutine.py <start_value> <end_value> <start_mode> <operation_mode> <parameter name> <number of simulations> This will train the file over the training examples starting from <start_value> till (excluding) the <end_value>. <start_mode> is either: 1. "start": In which case, the training will start from scratch (initial weights). 2. "continue": In which case, the training script will look for a file "trainedWeights.csv" containing already trained weights upto some point and continue training from there. Allows training in laps or checkpoints. <operation_mode> is either: 1. "dse" (for Design space exploration) In which case, you have to provide the parameter name that is to be investigated and also the number of simulations, as suggested by the paramGenerator.py script. The resulting receptive fields will be saved in a monolithic pdf file, and trained weights will be saved in separate files corresponding to each parameter file previously generated. II. "standalone" (for standalone training) In which case, you should ignore the subsequent arguments. The resulting single receptive field figure will be saved in a pdf file and a single trainedWeights.csv file will be generated with the trained Weights in it. For example: to run 10 simulations on different values of the "tau_theta" parameter: $python SNNTrainRoutine.py 0 20000 start dse tau_theta 10 to run standalone training from an already trained checkpoint: $python SNNTrainRoutine.py 20000 30000 continue standalone [NB: Project still in development. Contact [email protected] for further info] 07.04.2018 © Author: Saunak Saha Iowa State University
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