test_capacity's Introduction
Anonymized code for running simulations for the paper "Capacity of Group-invariant Linear Readouts from Equivariant Representations: How many objects can be linearly classified under all possible views?", submitted to ICLR 2022. To generate Figure 1, run python manifold_plots.py This script is fairly simple and self-explanatory. To generate Figures 2 and 3, run python plot_cnn_capacity.py At the bottom of the plot_cnn_capacity.py script, the plotting function is called for different panels. Comment out lines to generate specific figures. This script searches for a match with sets of parameters defined in cnn_capacity_params.py. To modify parameters used for simulations, modify the dictionaries in cnn_capacity_params.py or define your own parameter sets. For a description of different parameter options, see the docstring for the function cnn_capacity.get_capacity. cnn_capacity_utils.py contains utility functions. The VGG model can be found in models/vgg.py. The code for generating datasets can be found in datasets.py. The code was modified and superficially refactored in preparation for submission. The simulations haven't been thoroughly tested after this refactoring so it's not 100% guaranteed that the code is correct. Fingers crossed that everything works the way it should.
test_capacity's People
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