def test_update(self):
for vector_size in range(1, 10):
adam = Adam()
gnn = GraphNeuralNetwork(vector_size)
expected = gnn.params
adam.update(gnn)
actual = gnn.params
self.assertEqual(expected, actual)
gnn.grads["W"] = np.random.rand(vector_size, vector_size)
gnn.grads["A"] = np.random.rand(vector_size)
gnn.grads["b"] = np.random.rand(1)
v = {}
m = {}
for key, grad in gnn.grads.items():
v[key] = np.zeros_like(grad)
m[key] = np.zeros_like(grad)
params = copy.deepcopy(gnn.params)
for i in range(1, 100):
gnn.grads["W"] = np.random.rand(vector_size, vector_size)
gnn.grads["A"] = np.random.rand(vector_size)
gnn.grads["b"] = np.random.rand(1)
adam.update(gnn)
for key, param in params.items():
m[key] = adam.beta1 * m[key] + (
(1 - adam.beta1) * gnn.grads[key])
v[key] = adam.beta2 * v[key] + (
(1 - adam.beta2) * gnn.grads[key]**2)
m_hat = m[key] / (1 - adam.beta1**i)
v_hat = v[key] / (1 - adam.beta2**i)
params[key] = param - adam.lr * m_hat / (np.sqrt(v_hat) +
1.0e-8)
expected1 = repr(
np.round(np.abs(params[key] - gnn.params[key]), 6))
actual1 = repr(np.zeros_like(params[key]))
self.assertEqual(expected1, actual1)
class net:
def __init__(self):
self.params = dict()
self.grads = dict()
self.params['W1'] = args.weight_init_std * np.random.randn(784, 256)
self.params['B1'] = np.zeros(256)
self.params['W2'] = args.weight_init_std * np.random.randn(256, 256)
self.params['B2'] = np.zeros(256)
self.params['W3'] = args.weight_init_std * np.random.randn(256, 10)
self.params['B3'] = np.zeros(10)
self.optimizer = Adam()
def forward(self, input, target):
z1 = np.dot(input, self.params['W1']) + self.params['B1']
x2 = self.relu(z1)
z2 = np.dot(x2, self.params['W2']) + self.params['B2']
x3 = self.relu(z2)
z3 = np.dot(x3, self.params['W3']) + self.params['B3']
output = self.softmax(z3)
self.params['X1'] = input
self.params['Z1'] = z1
self.params['X2'] = x2
self.params['Z2'] = z2
self.params['X3'] = x3
self.params['Z3'] = z3
self.params['Y'] = output
self.params['T'] = target
return output, self.loss(output, target)
def backward(self):
# Grad
# https://stats.stackexchange.com/questions/235528/backpropagation-with-softmax-cross-entropy
W3 = (self.params['Y'] - self.params['T']) / args.batch_size
self.grads['W3'] = np.dot(self.params['X3'].T, W3)
self.grads['B3'] = np.sum(W3, axis=0)
W2 = np.dot(W3, self.params['W3'].T)
W2 *= np.where(self.params['Z2'] > 0, 1, 0)
self.grads['W2'] = np.dot(self.params['X2'].T, W2)
self.grads['B2'] = np.sum(W2, axis=0)
W1 = np.dot(W2, self.params['W2'].T)
W1 *= np.where(self.params['Z1'] > 0, 1, 0)
self.grads['W1'] = np.dot(self.params['X1'].T, W1)
self.grads['B1'] = np.sum(W1, axis=0)
self.optimizer.update(self.params, self.grads)
def loss(self, output, target):
# Cross Entropy
if output.ndim == 1:
target = t.reshape(1, target.size)
output = output.reshape(1, output.size)
# 훈련 데이터가 원-핫 벡터라면 정답 레이블의 인덱스로 반환
if target.size == output.size:
target = target.argmax(axis=1)
batch_size = output.shape[0]
return -np.sum(
np.log(output[np.arange(batch_size), target] + 1e-7)) / batch_size
def accuracy(self, output, target):
return np.sum(np.argmax(output, axis=1) == np.argmax(
target, axis=1)) / output.shape[0]
def softmax(self, x):
if x.ndim == 2:
x = x.T
x = x - np.max(x, axis=0)
y = np.exp(x) / np.sum(np.exp(x), axis=0)
return y.T
x = x - np.max(x) # 오버플로 대책
return np.exp(x) / np.sum(np.exp(x))
def relu(self, x):
return np.maximum(0, x)
def relu6(self, x):
return np.minimum(6, np.maximum(0, x))
train_acc_list = []
test_acc_list = []
iters_num = 10000
batch_size = 100
train_size = x_train.shape[0]
iter_per_epoch = max(train_size / batch_size, 1)
net = TwoLayerNet(input_size=784, hidden_size=50, output_size=10)
# optim = SGD(net.params, lr=0.1, momentum=0.9)
# optim = AdaGrad(net.params)
optim = Adam(net.params)
for i in range(iters_num):
batch_mask = np.random.choice(train_size, batch_size)
x_batch = x_train[batch_mask]
t_batch = t_train[batch_mask]
grad = net.gradient(x_batch, t_batch)
net.params = optim.update(net.params, grad)
loss = net.loss(x_batch, t_batch)
train_loss_list.append(loss)
if i % iter_per_epoch == 0:
train_acc = net.accuracy(x_train, t_train)
test_acc = net.accuracy(x_test, t_test)
train_acc_list.append(train_acc)
test_acc_list.append(test_acc)
print("train acc, test acc | " + str(train_acc) + ", " + str(test_acc))
