def test_update(self):
sgd = SGD()
gnn = GraphNeuralNetwork(vector_size=2)
expected = gnn.params
sgd.update(gnn)
actual = gnn.params
self.assertEqual(expected, actual)
params = copy.deepcopy(gnn.params)
for _ in range(100):
gnn.grads["W"] = np.random.rand()
gnn.grads["A"] = np.random.rand()
gnn.grads["b"] = np.random.rand()
sgd.update(gnn)
for key, param in params.items():
params[key] = param - gnn.grads[key] * sgd.lr
expected = repr(params[key])
actual = repr(gnn.params[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()
offsets = [i * jump for i in range(batch_size)]
for epoch in range(max_epoch):
for iter in range(max_iters):
# 2. Get mini bathes
batch_x = np.empty((batch_size, time_size), dtype='i')
batch_t = np.empty((batch_size, time_size), dtype='i')
for t in range(time_size):
for i, offset in enumerate(offsets):
batch_x[i, t] = xs[(offset + time_index) % data_size]
batch_t[i, t] = ts[(offset + time_index) % data_size]
time_index += 1
# Calculate gradients and update parameters
loss = model.forward(batch_x, batch_t)
model.backward()
optimiser.update(model.params, model.grads)
total_loss += loss
loss_count += 1
# 3. Eveluate perplexity in each epoch
ppl = np.exp(total_loss / loss_count)
print('Epoch %d | Perplexity %.2f' % (epoch + 1, ppl))
ppl_list.append(float(ppl))
total_loss = 0
loss_count = 0
x = np.arange(len(ppl_list))
plt.figure()
plt.plot(x, ppl_list, label='train')
plt.xlabel('Epochs')
plt.ylabel('Perplexity')