def test_backward(self):
gnn = GraphNeuralNetwork(vector_size=2)
gnn2 = copy.deepcopy(gnn)
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
step_size = 4
epsilon = 1.0e-4
params = gnn.params
gnn.backward(graphs, vertex_sizes, labels, step_size, epsilon)
for key, param in params.items():
expected = repr(
gnn2.gradient(key, graphs, vertex_sizes, labels, step_size,
epsilon))
actual = repr(gnn.grads[key])
self.assertEqual(expected, actual)
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()