This package includes application demos for all developed tools of MindArmour. Through these demos, you will soon master those tools of MindArmour. Let's Start!

Preparation

Most of those demos are implemented based on LeNet5 and MNIST dataset. As a preparation, we should download MNIST and train a LeNet5 model first.

1. download dataset

The MNIST database of handwritten digits has a training set of 60,000 examples, and a test set of 10,000 examples . It is a subset of a larger set available from MNIST. The digits have been size-normalized and centered in a fixed-size image.

$ cd examples/common/dataset
$ mkdir MNIST
$ cd MNIST
$ mkdir train
$ mkdir test
$ cd train
$ wget "http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz"
$ wget "http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz"
$ gzip train-images-idx3-ubyte.gz -d
$ gzip train-labels-idx1-ubyte.gz -d
$ cd ../test
$ wget "http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz"
$ wget "http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz"
$ gzip t10k-images-idx3-ubyte.gz -d
$ gzip t10k-images-idx3-ubyte.gz -d

2. trian LeNet5 model

After training the network, you will obtain a group of ckpt files. Those ckpt files save the trained model parameters of LeNet5, which can be used in 'examples/ai_fuzzer' and 'examples/model_security'.

$ cd examples/common/networks/lenet5
$ python mnist_train.py