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import os, sys | |
sys.path.append(os.path.join(os.getcwd(), "keras-deep-graph-learning")) # Adding the submodule to the module search path | |
sys.path.append(os.path.join(os.getcwd(), "keras-deep-graph-learning/examples")) # Adding the submodule to the module search path | |
import numpy as np | |
from examples import utils | |
from keras.layers import Dense, Activation, Dropout | |
from keras.models import Model, Sequential | |
from keras.regularizers import l2 | |
from keras.optimizers import Adam | |
from keras_dgl.layers import GraphCNN | |
import keras.backend as K | |
X, A, Y = utils.load_data(dataset='cora') | |
print("Just to check that this is indeed sparse, but not zero, check the column sums: ", sum(A.A)) | |
y_train, y_val, y_test, idx_train, idx_val, idx_test, train_mask = utils.get_splits(Y) | |
A_norm = utils.preprocess_adj_numpy(A, True) | |
# for reference, what do we do with preprocessing? | |
# | |
# adj = adj + np.eye(adj.shape[0]) | |
# d = np.diag(np.power(np.array(adj.sum(1)), -0.5).flatten(), 0) | |
# a_norm = adj.dot(d).transpose().dot(d) | |
# return a_norm |
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