nsp 0.0.13

Neural Network Signal Processing

Neural Network Signal Processing

nsp is a Python package for extracting and visualizing activation patterns of PyTorch neural networks. It can

• extract the computational graph of a neural network as a directed graph.
• extract the activation pattern generated by an input of a neural network.
• compute the Fourier transform of an activation pattern (signal) in a neural network (directed graph) based on [1] and [2].
• visualize activation patterns and their Fourier coefficients.

Visualization of the activation pattern

Visualization of the Fourier coefficients

Dependencies

Installation requires pytorch, networkx, and numpy. Some functions will use scipy and/or matplotlib.

Installation

Use the package manager pip to install nsp.

pip install nsp

Usage

Get your network as torch.nn.Module and a valid input.

import nsp
import torch
import torch.nn as nn

class Network(nn.Module):
    def __init__(self):
        super(Network, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=2, kernel_size=2)
        self.conv2 = nn.Conv2d(in_channels=2, out_channels=8, kernel_size=2)
        self.fc1 = nn.Linear(1*4*8, 8)

    def forward(self, x):
        x = torch.sigmoid(self.conv1(x))
        x = torch.sigmoid(self.conv2(x))
        x = x.view(-1, 1*4*8)
        x = self.fc1(x)
        return x

network = Networks()
image = torch.tensor([[[[-6, -1, -2,  5],
                        [-3, -6,  5,  4],
                        [ 2,  5, -6,  3],
                        [ 5,  0,  1, -6]]]], dtype = torch.float)

Extract the activation pattern.

activations = nsp.Activations(network, image)

Extract the graph of your neural network. NNGraph extends networkx.DiGraph.

graph = nsp.NNGraph(activations)

Transform the activation pattern into its spectrum.

spectrum = graph.transform(activations)

Visualize the activation pattern and the spectrum. Pick your favorite cmap_style from matplotlib colormaps.

nsp.Visualizer.visualize_pattern(activations, pdf_filepath='activations.pdf', scale='layernorm', cmap_style='viridis')

nsp.Visualizer.visualize_pattern(spectrum, pdf_filepath='spectrum.pdf', scale='layernorm', cmap_style='viridis')

Visualization of the activation pattern

Visualization of the spectrum

For more details check out the tutorials and read the documentation.

License

MIT

Developed by Felipa Schwarz (c) 2021

References

[1] Markus Püschel, Bastian Seifert, and Chris Wendler. Discrete signal processing on meet/join lattices. IEEE Transactions on Signal Processing, 2021.

[2] Bastian Seifert, Chris Wendler and Markus Püschel. A Causal Shift and Fourier Transform for Directed Acyclic Graphs. Submitted for publication, 2021.