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#### Loading Required Libraries ####
import torch
import torch.nn as nn
import torch.optim as optim
import matplotlib.pyplot as plt
# get_ipython().run_line_magic('matplotlib', 'notebook')
import imageio
from celluloid import Camera
from IPython.display import HTML
plt.rcParams['animation.ffmpeg_path'] = '/usr/local/bin/ffmpeg'
#### The Convolutional Layer ####
# First we will be creating the GCNConv class, which will serve as the Layer creation class.
# Every instance of this class will be getting Adjacency Matrix as input and will be outputing
# 'RELU(A_hat * X * W)', which the Net class will use.
class GCNConv(nn.Module):
def __init__(self, A, in_channels, out_channels):
super(GCNConv, self).__init__()
self.A_hat = A+torch.eye(A.size(0))
self.D = torch.diag(torch.sum(self.A_hat,1))
self.D = self.D.inverse().sqrt()
self.A_hat = torch.mm(torch.mm(self.D, self.A_hat), self.D)
self.W = nn.Parameter(torch.rand(in_channels,out_channels, requires_grad=True))
def forward(self, X):
out = torch.relu(torch.mm(torch.mm(self.A_hat, X), self.W))
return out
class Net(torch.nn.Module):
def __init__(self,A, nfeat, nhid, nout):
super(Net, self).__init__()
self.conv1 = GCNConv(A,nfeat, nhid)
self.conv2 = GCNConv(A,nhid, nout)
def forward(self,X):
H = self.conv1(X)
H2 = self.conv2(H)
return H2
# 'A' is the adjacency matrix, it contains 1 at a position (i,j)
# if there is a edge between the node i and node j.
A=torch.Tensor([[0,1,1,1,1,1,1,1,1,0,1,1,1,1,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0],
[1,0,1,1,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0],
[1,1,0,1,0,0,0,1,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0],
[1,1,1,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1],
[0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1],
[1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1],
[0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1],
[1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1],
[1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,1,1],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1],
[0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1],
[0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,1],
[0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1],
[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0,1,1],
[0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,1,0,1,0,1,1,0,0,0,0,0,1,1,1,0,1],
[0,0,0,0,0,0,0,0,1,1,0,0,0,1,1,1,0,0,1,1,1,0,1,1,0,0,1,1,1,1,1,1,1,0]
])
# label for admin(node 1) and instructor(node 34) so only these two contain the class label(0 and 1)
# all other are set to -1, meaning predicted value of these nodes is ignored in the loss function.
target=torch.tensor([0,-1,-1,-1, -1, -1, -1, -1,-1,-1,-1,-1, -1, -1, -1, -1,-1,-1,-1,-1, -1, -1, -1, -1,-1,-1,-1,-1, -1, -1, -1, -1,-1,1])
# X is the feature matrix.
# Using the one-hot encoding corresponding to the index of the node.
X=torch.eye(A.size(0))
# Network with 10 features in the hidden layer and 2 in output layer.
T=Net(A,X.size(0), 10, 2)
#### Training ####
criterion = torch.nn.CrossEntropyLoss(ignore_index=-1)
optimizer = optim.SGD(T.parameters(), lr=0.01, momentum=0.9)
loss=criterion(T(X),target)
#### Plot animation using celluloid ####
fig = plt.figure()
camera = Camera(fig)
for i in range(200):
optimizer.zero_grad()
loss=criterion(T(X), target)
loss.backward()
optimizer.step()
l=(T(X));
plt.scatter(l.detach().numpy()[:,0],l.detach().numpy()[:,1],c=[0, 0, 0, 0 ,0 ,0 ,0, 0, 1, 1, 0 ,0, 0, 0, 1 ,1 ,0 ,0 ,1, 0, 1, 0 ,1 ,1, 1, 1, 1 ,1 ,1, 1, 1, 1, 1, 1 ])
for i in range(l.shape[0]):
text_plot = plt.text(l[i,0], l[i,1], str(i+1))
camera.snap()
if i%20==0:
print("Cross Entropy Loss: =", loss.item())
animation = camera.animate(blit=False, interval=150)
animation.save('./train_karate_animation.mp4', writer='ffmpeg', fps=60)
HTML(animation.to_html5_video())
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