README.md

On the Robustness of Convolutional Neural Networks to Internal Architecture and Weight Perturbations

On the Robustness of Convolutional Neural Networks to Internal Architecture and Weight Perturbations

Nicholas Cheney*, Martin Schrimpf*, Gabriel Kreiman - arXiv:1703.08245, 2017.

https://arxiv.org/abs/1703.08245, https://cbmm.mit.edu/sites/default/files/publications/CBMM-Memo-065_0.pdf

Setup

This project requires Python version >= 3.2.

First, install library requirements by running pip install -r requirements.txt.

Then download model weights and datasets by running python download_data.py. This will:

• download the following weights, each pre-trained on an ILSVRC training set
  • Alexnet: ILSVRC2012, convnetskeras' model files
  • VGG16 and VGG19: ILSVRC2014, keras uses VGG's weights (16, 19)
  • ResNet50: ILSVRC2015, keras uses Kaiming He's weights (model files)
  • Inceptionv3: keras' trained weights
• download the following datasets
  • VOC2012 (training and validation data)
  • ILSVRC2012 (training, validation and test data - note that the labels for the test data are all zero)

Running

Use python run.py to re-train models and predict datasets, python perturb_weights.py to perturb the weights, python analyze.py to analyze weights and results.

Re-Training

Run python run.py with the --weights argument not set to re-train the model on the dataset(s), for instance:

python run.py --model alexnet --datasets VOC2012/val

will re-train the model alexnet on the dataset VOC2012/val. Note that the model will not be trained from scratch but instead start with its associated weights, in the example weights/alexnet.h5.

Prediction

Run python run.py with the --weights argument set to predict the dataset(s) with the model, for instance:

python run.py --model alexnet --weights alexnet_retrained_on_VOC2012 --datasets VOC2012/val

will predict the dataset VOC2012/val with the model alexnet using the weights weights/alexnet_retrained_on_VOC2012.h5.

Weight Perturbation

Run python perturb_weights.py to perturb the weights of a model. For instance:

python perturb_weights.py --weights alexnet --layer conv_1 conv_2 conv_3 conv_4 conv_5 dense_1 dense_2 dense_3 --ratio 0.1 0.2 0.3 0.4 0.5 --num_perturbations 5

will perturb all 8 layers of weights/alexnet.h5 using the ratios {0.1, 0.2, ..., 0.5} and 5 different random variations for each layer.

Analyze Predictions

Run python analyze.py to analyze the results. For instance:

python analyze.py --weights perturbations/alexnet-conv_1-draw0.10 --datasets ILSVRC2012/val --metrics top5error

will analyze the predictions from the weights weights/perturbations/alexnet-conv_1-draw0.10.h5 on ILSVRC2012/val using the top5error metric.

Plotting

Run python plot.py <task> to plot weights or the results produced by a previous analysis where <task> is one of num_weights, weight_diffs, performances.

Weight Inference

If a results file for some weights itself does not exist, but variations of it with appended -num[0-9]+ etc. do exist, then those will be used and the results averaged across these variations. For instance, if --weights alexnet-conv1-draw0.5 is provided and the results for these weights do not exist, the script wil search for alexnet-conv1-draw0.5-num1.p, alexnet-conv1-draw0.5-num2.p etc. instead.

Running with LSF

To run any of the Python progams on a cluster with LSF,
use the run_lsf.sh script, for instance:

./run_lsf.sh run.py --model alexnet

Note that this script is tailored to our own cluster, specifically a queue named gpu is assumed to exist.