# This file generates the results of the tropical pruning discussed in the paper.
The arguments needs to be passed to the Runme.py are
1) Network archeticture: 'vgg16', 'alexnet', 'lenet'
2) dataset : 'mnist', 'mnistfashion', 'cifar10', 'cifar100', 'svhn'
3) method  : 'classblind', 'classuniform', 'classdistribution', 'compresstg'
4) retrain : 'True', 'False'
5) category: 'fcbias', 'allbias', 'all'
Where we mean by retrain as after each iteration of compression, you either retrain non-prunned parameters or not. 
For the category: which parameters you want to retrain after each iteration of compression. either the biases of the FC layers,
or the biases of the whole netwrok, or all of the parameters of the network except of the parameters of the classifier.
