3D ML. Partie 5: convolutions sur graphes

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IT- “VR/AR & AI” — PHYGITALISM.

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, “Representation Learning on Graphs: Methods and Applications” [1]. , Stanford CS224W: Machine Learning with Graphs. Sberloga, , , . ODS, (.. ).

Medium:

1. How to do Deep Learning on Graphs with Graph Convolutional Networks Tobias Skovgaard Jepsen;
2. - A Gentle Introduction to Graph Neural Networks (Basics, DeepWalk, and GraphSage) - Hands-on Graph Neural Networks with PyTorch & PyTorch Geometric Kung-Hsiang, Huang (Steeve);
3. Jan H. Jensen ( );
4. Medium — .

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import networkx as nx

G = nx.grid_graph([4, 4])
nx.draw(G)
      
      

        
        
        
      

    
        
        
        
      
      

        
        
        
      

    
    

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$$X_1 W_1 + X_2 W_2 \ne X_2 W_1 + X_1 W_2$$

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$$X_1 = (X_1 + X_2 + X_3 + X_4) W_1.$$

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$$X^{(l+1)} = X^{(l)} W^{(l)}.$$

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, $$$28 \times 28$ MNIST. numpy:

import numpy as np
from scipy.spatial.distance import cdist
img_size = 28  # MNIST image width and height
col, row = np.meshgrid(np.arange(img_size), np.arange(img_size))
coord = np.stack((col, row), axis=2).reshape(-1, 2) / img_size
dist = cdist(coord, coord)  # . 6 ( )
sigma = 0.2 * np.pi  # width of a Gaussian
A = np.exp(-dist ** 2 / sigma ** 2) # .6 (  )
      
      

        
        
        
      

    
        
        
        
      
      

        
        
        
      

    
    

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1. $$$\mathcal{A} = A$ $$$\mathcal{A} X^{(l)}$ [7];
2. $$$\mathcal{A} = \frac{A}{\|A\|}$ $$$\mathcal{A} X^{(l)}$ [6].

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$$X^{(l+1)} = \mathcal{A} X^{(l)} W^{(l)}.$$

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PyTorch, :

import torch
import torch.nn as nn

C = 2  # Input feature dimensionality
F = 8  # Output feature dimensionality
W = nn.Linear(in_features=C, out_features=F)  # Trainable weights

# Fully connected layer
X = torch.randn(1, C)  # Input features
Z = W(X)  # Output features : torch.Size([1, 8])

#Graph Neural Network layer
N = 6  # Number of nodes in a graph
X = torch.randn(N, C)  # Input feature
A = torch.rand(N, N)  # Adjacency matrix (edges of a graph)
Z = W(torch.mm(A, X))  # Output features: torch.Size([6, 8])
      
      

        
        
        
      

    
        
        
        
      
      

        
        
        
      

    
    

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import torch.nn as nn           # using PyTorch
nn.Sequential(nn.Linear(4, 64), # map coordinates to a hidden layer
             nn.ReLU(),         # nonlinearity
             nn.Linear(64, 1),  # map hidden representation to edge
             nn.Tanh())         # squash edge values to [-1, 1]
      
      

        
        
        
      

    
        
        
        
      
      

        
        
        
      

    
    

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, Medium.