# Read the Adjacency matrix
Adjacency_Matrix = pd.read_csv(DIR + 'Adjacency_Matrix.csv', header=None)
Adjacency_Matrix = np.array(Adjacency_Matrix).astype('float32')
Adjacency_Matrix = sparse.csr_matrix(Adjacency_Matrix)
# This is the coarsen levels, you can definitely change the level to observe the difference
graphs, perm = coarsening.coarsen(Adjacency_Matrix,
levels=5,
self_connections=False)
X_train = coarsening.perm_data(train_data, perm)
X_test = coarsening.perm_data(test_data, perm)
# Obtain the Graph Laplacian
L = [
graph.laplacian(Adjacency_Matrix, normalized=True)
for Adjacency_Matrix in graphs
]
# Hyper-parameters
params = dict()
params['dir_name'] = Model
params['num_epochs'] = 100
params['batch_size'] = 1024
params['eval_frequency'] = 100
# Building blocks.
params['filter'] = 'chebyshev5'
params['brelu'] = 'b2relu'
params['pool'] = 'mpool1'
os.mkdir(SAVE)
# Load the dataset, here it uses one-hot representation for labels
train_data, train_labels, test_data, test_labels = DatasetLoader(DIR=DIR)
# Read the Adjacency matrix
Adjacency_Matrix = pd.read_csv(DIR + 'Adjacency_Matrix.csv', header=None)
Adjacency_Matrix = np.array(Adjacency_Matrix).astype('float32')
Adjacency_Matrix = sparse.csr_matrix(Adjacency_Matrix)
graphs, perm = coarsening.coarsen(Adjacency_Matrix, levels=5, self_connections=False)
X_train = coarsening.perm_data(train_data, perm)
X_test = coarsening.perm_data(test_data, perm)
# Obtain the Graph Laplacian
L = [graph.laplacian(Adjacency_Matrix, normalized=True) for Adjacency_Matrix in graphs]
# Hyper-parameters
params = dict()
params['dir_name'] = Model
params['num_epochs'] = 100
params['batch_size'] = 1024
params['eval_frequency'] = 100
# Building blocks.
params['filter'] = 'chebyshev5'
params['brelu'] = 'b2relu'
params['pool'] = 'mpool1'
# Architecture.
params['F'] = [16, 32, 64, 128, 256, 512] # Number of graph convolutional filters.