This directory contains the implementation of the continual binary ImageNet classification problem.
The first step to replicate the results is to download the data. The data can be downloaded here.
Create a directory named data and extract the downloaded file in data
cd lop/imagenet/
mkdir dataThe next step is to test a deep convolution network that uses backprop to learn.
The network is specified in ../nets/conv_net.py
This command produces 30 temporary cfg files in temp_cfg.
python3.8 multi_param_expr.py -c cfg/bp.json Each of the new temporary cfg files can then be used to do one run of backprop. Each run takes about 12 hours on an A-100 GPU.
python3.8 expr.py -c temp_cfg/0.json Finally, after completing all the runs, the command below can be used to generate
the plot for the network specified in cfg/sgd/bp.json.
python3.8 bp_plot.py -c cfg/bp.json The command above will generate the plot on the right below. The results below are averaged for 30 runs, and accuracy is binned into bins of size 50.
Similarly, you can use the cfg files for l2 regularization, shrink-and-perturb, and continual backpropagation to evaluate the peformance of these methods. The results of these methods can be plotted using the following command.
python3.8 all_plot.py -c cfg/bp.json The results for all the methods are presented below.
