def test_graphattnet_attnlayer_selfloop(self):
gcn_graph = get_graph_test()
node_dim = gcn_graph.X.shape[1]
edge_dim = gcn_graph.E.shape[1] - 2.0
nb_class = gcn_graph.Y.shape[1]
gcn_model = GraphAttNet(node_dim,
nb_class,
num_layers=1,
learning_rate=0.01,
node_indim=8,
nb_attention=1)
gcn_model.create_model()
Wa = tf.eye(node_dim)
va = tf.ones([2, node_dim])
# elf.Ssparse, self.Tspars
alphas, nH = gcn_model.simple_graph_attention_layer(
gcn_model.node_input,
Wa,
va,
gcn_model.Ssparse,
gcn_model.Tsparse,
gcn_model.Aind,
gcn_model.Sshape,
gcn_model.nb_edge,
gcn_model.dropout_p_attn,
gcn_model.dropout_p_node,
add_self_loop=True)
alphas_shape = tf.shape(alphas)
node_indices = tf.range(gcn_model.Sshape[0])
# Sparse Idendity
# Debug
id_indices = tf.stack([node_indices, node_indices], axis=1)
val = tf.squeeze(tf.matmul(gcn_model.node_input, va, transpose_b=True))
spI = tf.SparseTensor(
indices=id_indices,
values=val,
dense_shape=[gcn_model.Sshape[0], gcn_model.Sshape[0]])
init = tf.global_variables_initializer()
#AI=tf.sparse_add(alphas,spI)
graph = gcn_graph
with tf.Session() as session:
session.run([init])
print('### Graph', graph.X.shape, graph.F.shape[0])
# print(graph.Sind)
# print(graph.Tind)
nb_node = graph.X.shape[0]
Aind = np.array(np.stack([graph.Sind[:, 0], graph.Tind[:, 1]],
axis=-1),
dtype='int64')
print("Adjacency Indices:", Aind.shape, Aind)
feed_batch = {
gcn_model.nb_node:
graph.X.shape[0],
gcn_model.nb_edge:
graph.F.shape[0],
gcn_model.node_input:
graph.X,
gcn_model.Ssparse:
np.array(graph.Sind, dtype='int64'),
gcn_model.Sshape:
np.array([graph.X.shape[0], graph.F.shape[0]], dtype='int64'),
gcn_model.Tsparse:
np.array(graph.Tind, dtype='int64'),
gcn_model.Aind:
Aind,
# self.F: graph.F,
gcn_model.y_input:
graph.Y,
# self.dropout_p_H: self.dropout_rate_H,
gcn_model.dropout_p_node:
0.0,
gcn_model.dropout_p_attn:
0.0,
}
[c_alphas, c_nH, c_alphas_shape,
spI] = session.run([alphas, nH, alphas_shape, spI],
feed_dict=feed_batch)
print('alphas', c_alphas, c_alphas_shape)
print('spI', spI)
#print('AI',AI)
sp_mat = sp.coo_matrix(
(c_alphas.values,
(c_alphas.indices[:, 0], c_alphas.indices[:, 1])),
shape=(nb_node, nb_node))
Att_dense = sp_mat.todense()
print(Att_dense)
self.assertTrue(c_alphas_shape[0] == 3)
self.assertTrue(c_alphas_shape[1] == 3)
self.assertTrue(Att_dense[0, 2] == 0)
def test_graphattnet_attnlayer(self):
gcn_graph = get_graph_test()
node_dim = gcn_graph.X.shape[1]
edge_dim = gcn_graph.E.shape[1] - 2.0
nb_class = gcn_graph.Y.shape[1]
gcn_model = GraphAttNet(node_dim,
nb_class,
num_layers=1,
learning_rate=0.01,
node_indim=8,
nb_attention=1)
gcn_model.create_model()
Wa = tf.eye(node_dim)
va = tf.ones([2, node_dim])
# elf.Ssparse, self.Tspars
alphas, nH = gcn_model.simple_graph_attention_layer(
gcn_model.node_input, Wa, va, gcn_model.Ssparse, gcn_model.Tsparse,
gcn_model.Aind, gcn_model.Sshape, gcn_model.nb_edge,
gcn_model.dropout_p_attn, gcn_model.dropout_p_node)
alphas_shape = tf.shape(alphas)
init = tf.global_variables_initializer()
graph = gcn_graph
with tf.Session() as session:
session.run([init])
print('### Graph', graph.X.shape, graph.F.shape[0])
# print(graph.Sind)
# print(graph.Tind)
nb_node = graph.X.shape[0]
Aind = np.array(np.stack([graph.Sind[:, 0], graph.Tind[:, 1]],
axis=-1),
dtype='int64')
print("Adjacency Indices:", Aind.shape, Aind)
feed_batch = {
gcn_model.nb_node:
graph.X.shape[0],
gcn_model.nb_edge:
graph.F.shape[0],
gcn_model.node_input:
graph.X,
gcn_model.Ssparse:
np.array(graph.Sind, dtype='int64'),
gcn_model.Sshape:
np.array([graph.X.shape[0], graph.F.shape[0]], dtype='int64'),
gcn_model.Tsparse:
np.array(graph.Tind, dtype='int64'),
gcn_model.Aind:
Aind,
# self.F: graph.F,
gcn_model.y_input:
graph.Y,
# self.dropout_p_H: self.dropout_rate_H,
gcn_model.dropout_p_node:
0.0,
gcn_model.dropout_p_attn:
0.0,
}
[c_alphas, c_nH,
c_alphas_shape] = session.run([alphas, nH, alphas_shape],
feed_dict=feed_batch)
print('alphas', c_alphas, c_alphas_shape)
sp_mat = sp.coo_matrix(
(c_alphas.values,
(c_alphas.indices[:, 0], c_alphas.indices[:, 1])),
shape=(nb_node, nb_node))
Att_dense = sp_mat.todense()
print(Att_dense)
self.assertTrue(c_alphas_shape[0] == 3)
self.assertTrue(c_alphas_shape[1] == 3)
self.assertTrue(Att_dense[0, 2] == 0)
self.assertAlmostEqual(Att_dense[1, 0],
np.exp(2.5) / (np.exp(2.5) + np.exp(2)))
self.assertAlmostEqual(Att_dense[0, 1], 1.0)
self.assertAlmostEqual(Att_dense[2, 1], 1.0)