-
Notifications
You must be signed in to change notification settings - Fork 0
/
GNE.py
195 lines (128 loc) · 5.03 KB
/
GNE.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
import torch
import torchvision
import numpy as np
import scipy as sio
import sklearn
import torch.nn as nn # All neural network modules, nn.Linear, nn.Conv2d, BatchNorm, Loss functions
import torch.optim as optim # For all Optimization algorithms, SGD, Adam, etc.
import torch.nn.functional as F # All functions that don't have any parameters
from torch.utils.data import DataLoader # Gives easier dataset managment and creates mini batches
import torchvision.datasets as datasets # Has standard datasets we can import in a nice way
from tqdm import tqdm # For nice progress bar!
from models import GCN
import time
from dataset import TUDataset, CollateFn
from MNIST import GraphTransform
torch.manual_seed(28)
torch.cuda.manual_seed(28)
# set device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Hyperparameters
num_classes = 10
learning_rate = 1e-4
batch_size = 50
num_epochs = 5
#load dataset
train_dataset = datasets.MNIST(root='dataset/', train=True, transform=GraphTransform(device), download=False)
train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True , collate_fn=CollateFn(device))
test_dataset = datasets.MNIST(root='dataset/', train=False, transform=GraphTransform(device), download=False)
test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=True,collate_fn=CollateFn(device))
# to create A+I, but please see MNIST.py, this is just a referece
def adj_head(m):
M = m**2
adj_matrix = np.zeros((M,M),dtype=int)
for i in range(m):
for j in range(m):
temp = np.zeros((m,m),dtype=int)
for yy in [-1, 0, 1]:
for xx in [-1, 0, 1]:
if 0 <= i + yy < m:
if 0<= j + xx < m:
temp[i+yy][j+xx]=1
adj_matrix[i*m+j]=temp.reshape(M)
return adj_matrix
# to create sum of A as degree matrix
def degree_(adj,m):
lenghth = adj.shape[0]
dia = 0
M = m**2
degree_matrix = np.zeros((M,M),dtype=int)
for i in range(lenghth):
degree = sum(adj[i])
degree_matrix[i][i] = degree
dia = dia + m + 1
return degree_matrix
#create graph
adj = adj_head(28)
degree = degree_(adj,28)
# normalize and symatric
# G = nx.convert_matrix.from_numpy_matrix(adj,parallel_edges=True,create_using=nx.DiGraph)
# adj = normalize(adj)
# adj = sp.csr_matrix.todense(adj)
# d_inv = np.where(degree>0, np.float_power(degree,-1/2),0)
# processed_adj = np.dot(d_inv,adj)
# processed_adj = np.dot(adj,d_inv)
# adj = sp.csr_matrix(processed_adj)
# adj = sparse_mx_to_torch_sparse_tensor(adj)
# adj = torch.from_numpy(adj).float()
def accuracy(output, labels):
preds = output.max(1)[1].type_as(labels)
correct = preds.eq(labels).double()
correct = correct.sum()
return correct / len(labels)
# model creation and loss seting
model = GCN(nfeat=1,
nhid=256,
nclass=10).to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
###Training Part
start_time = time.time()
for epoch in range(num_epochs):
for i, (adj, features, masks, batch_labels) in tqdm(enumerate(train_loader)):
# input = feature[1].view(784,1)
features = features.to(device)
batch_labels = batch_labels.to(device)
adj = adj.to(device)
t = time.time()
output = model(features, adj)
loss_train = criterion(output, batch_labels)
acc_train = accuracy(output, batch_labels)
optimizer.zero_grad()
loss_train.backward()
optimizer.step()
loss_val = F.nll_loss(output, batch_labels)
acc_val = accuracy(output, batch_labels)
model.train()
print('Epoch: {:04d}'.format(epoch+1),
'loss_train: {:.4f}'.format(loss_train.item()),
'acc_train: {:.4f}'.format(acc_train.item()),
'loss_val: {:.4f}'.format(loss_val.item()),
'acc_val: {:.4f}'.format(acc_val.item()),
'time: {:.4f}s'.format(time.time() - t))
# Train model
t_total = time.time()
print("Optimization Finished!")
print("Total time elapsed: {:.4f}s".format(time.time() - start_time))
##accuracy check
def check_accuracy(loader, model):
if loader.dataset.train:
print("Checking accuracy on training data")
else:
print("Checking accuracy on test data")
num_correct = 0
num_samples = 0
model.eval()
with torch.no_grad():
for adj, features, masks, batch_labels in loader:
adj = adj.to(device=device)
features = features.to(device=device)
batch_labels = batch_labels.to(device=device)
test_scores = model(features, adj)
_, predictions = test_scores.max(1)
num_correct += (predictions == batch_labels).sum()
num_samples += predictions.size(0)
print(f'Got {num_correct} / {num_samples} with accuracy {float(num_correct) / float(num_samples) * 100:.2f}')
return test_scores
#Get accuracy
check_accuracy(test_loader, model)