def test_loss(self):
gnn = GraphNeuralNetwork(vector_size=3)
gnn.params["W"] = -np.arange(1, 10).reshape(3, 3)
gnn.params["b"] = np.array([-100])
graphs = [[[0, 0, 1, 0], [0, 0, 1, 1], [1, 1, 0, 1], [0, 1, 1, 0]]
] * 10
vertex_sizes = [4] * 10
labels = [1] * 10
expected = "array([100.])"
actual = repr(gnn.loss(graphs, vertex_sizes, labels))
self.assertEqual(expected, actual)
def test_loss(self):
gnn = GraphNeuralNetwork(vector_size=2)
gnn.params["W"] = -np.arange(1, 5).reshape(2, 2)
gnn.params["b"] = np.array([0])
vertex_size = 4
graph = [[0, 0, 1, 0],
[0, 0, 1, 1],
[1, 1, 0, 1],
[0, 1, 1, 0]]
label = [0, 1]
params = [[0], [-100], [100]]
expecteds = [[0.6931471805599453]*2,
[0., 100.],
[100., 0.]]
for param, expected in zip(params, expecteds):
gnn.params["b"] = np.array(param)
actual1 = gnn.loss(graph, vertex_size, label[0])
self.assertEqual(expected[0], actual1)
actual2 = gnn.loss(graph, vertex_size, label[1])
self.assertEqual(expected[1], actual2)
from gnn import GraphNeuralNetwork
from sgd import SGD
if __name__ == "__main__":
gnn = GraphNeuralNetwork(8, 0)
sgd = SGD()
graph = [[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[1, 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, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 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, 1],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1],
[1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0]]
vertex_size = 11
label = 1
loss = []
print("A")
for i in range(0, 50):
loss.append(gnn.loss(graph, vertex_size, label))
gnn.backward(graph, vertex_size, label)
sgd.update(gnn)
loss.append(gnn.loss(graph, vertex_size, label))
print(f"最初の損失:{loss[0]}, 最後の損失:{loss[-1]}")
plt.plot(np.arange(len(loss)), loss)
plt.show()
import sys
sys.path.append("src/")
from gnn import GraphNeuralNetwork
from optimizer import SGD
from train import read_graph, read_label
if __name__ == "__main__":
graph = read_graph(0)
label = read_label(0)
print("label: {}".format(label))
gnn = GraphNeuralNetwork(optimizer=SGD())
for i in range(1000):
print("[{}th iteration] loss: {}, p: {}".format(
i,
gnn.loss(graph, label)[0],
gnn._get_p(graph)[0],
))
gnn.gradient_descent([(graph, label)])