def __train(weight_init_std):
bn_network = MultiLayerNetExtend(input_size=784, hidden_size_list=[100, 100, 100, 100, 100], output_size=10, weight_init_std=weight_init_std, use_batchnorm=True)
network = MultiLayerNetExtend(input_size=784, hidden_size_list=[100, 100, 100, 100, 100],output_size=10, weight_init_std=weight_init_std)
optimizer = SGD(lr=learning_rate)
train_acc_list = []
bn_train_acc_list = []
iter_per_epoch = max(train_size / batch_size, 1)
epoch_cnt = 0
for i in range(1000000000):
batch_mask = np.random.choice(train_size, batch_size)
x_batch = x_train[batch_mask]
t_batch = t_train[batch_mask]
for _network in (bn_network, network):
grads = _network.gradient(x_batch, t_batch)
optimizer.update(_network.params, grads)
if i % iter_per_epoch == 0:
train_acc = network.accuracy(x_train, t_train)
bn_train_acc = bn_network.accuracy(x_train, t_train)
train_acc_list.append(train_acc)
bn_train_acc_list.append(bn_train_acc)
print("epoch:" + str(epoch_cnt) + " | " + str(train_acc) + " - " + str(bn_train_acc))
epoch_cnt += 1
if epoch_cnt >= max_epochs:
break
return train_acc_list, bn_train_acc_list
def __train(weight_init_std):
bn_network = MultiLayerNetExtend(input_size=784, hidden_size_list=[100, 100, 100, 100, 100], output_size=10,
weight_init_std=weight_init_std, use_batchnorm=True)
network = MultiLayerNetExtend(input_size=784, hidden_size_list=[100, 100, 100, 100, 100], output_size=10,
weight_init_std=weight_init_std)
optimizer = SGD(lr=learning_rate)
train_acc_list = []
bn_train_acc_list = []
iter_per_epoch = max(train_size / batch_size, 1)
epoch_cnt = 0
for i in range(1000000000):
batch_mask = np.random.choice(train_size, batch_size)
x_batch = x_train[batch_mask]
t_batch = t_train[batch_mask]
for _network in (bn_network, network):
grads = _network.gradient(x_batch, t_batch)
optimizer.update(_network.params, grads)
if i % iter_per_epoch == 0:
train_acc = network.accuracy(x_train, t_train)
bn_train_acc = bn_network.accuracy(x_train, t_train)
train_acc_list.append(train_acc)
bn_train_acc_list.append(bn_train_acc)
print("epoch:" + str(epoch_cnt) + " | " + str(train_acc) + " - " + str(bn_train_acc))
epoch_cnt += 1
if epoch_cnt >= max_epochs:
break
return train_acc_list, bn_train_acc_list
def train(train_x, train_label, weight_init_std, learning_rate, max_epoch,
batch_size):
"""
构造带有BN层的神经网络和不带BN层的神经网络。测试BN层的效果
:param train_x:
:param train_label:
:param weight_init_std: 参数初始化方式
:param learning_rate:
:param max_epoch: 测试的epoch数
:param batch_size:
:return:
"""
bn_network = MultiLayerNetExtend(input_size=784,
hidden_size_list=[100, 100, 100, 100, 100],
output_size=10,
weight_init_std=weight_init_std,
use_batchnorm=True)
network = MultiLayerNetExtend(input_size=784,
hidden_size_list=[100, 100, 100, 100, 100],
output_size=10,
weight_init_std=weight_init_std,
use_batchnorm=False)
optimizer = SGD(learning_rate)
train_acc_list = []
bn_train_acc_list = []
train_size = train_x.shape[0]
iter_per_epoch = max(train_size / batch_size, 1)
batch_mask = np.arange(train_size)
np.random.shuffle(batch_mask)
epoch_cnt = 0
left = 0
iteration = int(iter_per_epoch * max_epoch)
for i in range(iteration):
# 获取一个batch的数据，更新left值
batch_x, batch_label, left = get_batch(train_x, train_label, batch_mask,
batch_size, left)
# 两个网络分别更新
for _network in (bn_network, network):
grads = _network.gradient(batch_x, batch_label)
optimizer.update(_network.params, grads)
# 每一个epoch记录一个在测试集上的准确率
if i % iter_per_epoch == 0:
train_acc = network.accuracy(train_x, train_label)
bn_train_acc = bn_network.accuracy(train_x, train_label)
train_acc_list.append(train_acc)
bn_train_acc_list.append(bn_train_acc)
print("epoch:" + str(epoch_cnt) + " | " + str(train_acc) + " - "
+ str(bn_train_acc))
epoch_cnt += 1
return train_acc_list, bn_train_acc_list
def main():
(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True,
one_hot_label=True)
x_train = x_train[:300]
t_train = t_train[:300]
max_epochs = 201
train_size = x_train.shape[0]
batch_size = 100
learning_rate = 0.01
use_dropout = True
dropout_ratio = 0.2
train_acc_list = []
test_acc_list = []
network = MultiLayerNetExtend(
input_size=784,
hidden_size_list=[100, 100, 100, 100, 100, 100],
output_size=10,
use_dropout=use_dropout,
dropout_ration=dropout_ratio)
optimizer = SGD(lr=learning_rate)
iter_per_epoch = max(train_size / batch_size, 1)
epoch_cnt = 0
for i in range(1000000000):
batch_mask = np.random.choice(train_size, batch_size)
x_batch = x_train[batch_mask]
t_batch = t_train[batch_mask]
grads = network.gradient(x_batch, t_batch)
optimizer.update(network.params, grads)
if i % iter_per_epoch == 0:
train_acc = network.accuracy(x_train, t_train)
test_acc = network.accuracy(x_test, t_test)
train_acc_list.append(train_acc)
test_acc_list.append(test_acc)
print("epoch:" + str(epoch_cnt) + ", train acc:" + str(train_acc) + \
", test acc:" + str(test_acc))
epoch_cnt += 1
if epoch_cnt >= max_epochs:
break
markers = {'train': 'o', 'test': 's'}
x = np.arange(max_epochs)
plt.plot(x, train_acc_list, marker='o', label='train', markevery=10)
plt.plot(x, test_acc_list, marker='s', label='test', markevery=10)
plt.xlabel("epochs")
plt.ylabel("accuracy")
plt.ylim(0, 1.0)
plt.legend(loc='lower right')
plt.show()
def __train(weight_init_std):
bn_net = MultiLayerNetExtend(input_size=784,
hidden_size_list=[100, 100, 100, 100],
output_size=10,
weight_init_std=weight_init_std,
use_batchnorm=True)
net = MultiLayerNetExtend(input_size=784,
hidden_size_list=[100, 100, 100, 100],
output_size=10,
weight_init_std=weight_init_std)
optimizer = SGD(lr=learning_rate)
train_acc_list = []
bn_train_acc_list = []
iter_per_epoch = max(train_size / batch_size, 1)
epoch_cnt = 0
for i in range(1000000000):
batch_mask = np.random.choice(train_size, batch_size)
x_batch = x_train[batch_mask]
t_batch = t_train[batch_mask]
for _net in (bn_net, net):
grads = _net.gradient(x_batch, t_batch)
optimizer.update(_net.params, grads)
if i % iter_per_epoch == 0:
train_acc = net.accuracy(x_train, t_train)
bn_train_acc = bn_net.accuracy(x_train, t_train)
train_acc_list.append(train_acc)
bn_train_acc_list.append(bn_train_acc)
print("EPOCH: {0} | NET_ACC({1}) - BN_NET_ACC({2})".format(
epoch_cnt, train_acc, bn_train_acc))
epoch_cnt += 1
if epoch_cnt >= max_epoches:
break
return train_acc_list, bn_train_acc_list
def __trainning(weight_init_std, index):
epoch_num = 0
acc_train_list = []
acc_train_BN_list = []
input_size = 784
hidden_size_list = [100] * 5
output_size = 10
activation = 'ReLu'
weight_decay_lambda = 1
network = MultiLayerNetExtend(input_size=input_size,
hidden_size_list=hidden_size_list,
output_size=output_size,
activation=activation,
weight_init_std=weight_init_std,
weight_decay_lambda=weight_decay_lambda,
use_BatchNormalization=False,
use_weight_decay=False)
network_BN = MultiLayerNetExtend(input_size=784,
hidden_size_list=hidden_size_list,
output_size=output_size,
activation=activation,
weight_init_std=weight_init_std,
weight_decay_lambda=weight_decay_lambda,
use_BatchNormalization=True,
use_weight_decay=False)
# 开始训练，训练分下面几步
# 1. 从样本中随机挑选出batch_size个样本
# 2. 前向传播、反向传播、获取梯度
# 3. 更新梯度
# 4. 重复1 ~ 2直至循环结束
for i in range(iter_num):
print('现在是W' + str(index) + '的第' + str(i) + '轮循环')
# 挑选mini_batch
batch_mask = np.random.choice(train_size, batch_size)
x_batch = x_train[batch_mask]
t_batch = t_train[batch_mask]
# 获取梯度，更新梯度
for network_ in (network, network_BN):
grads = network_.gradient(x_train, t_train, use_weight_decay=False)
# optimizer_SGD.update(params = network_.params, grads = grads)
if network_ == network:
optimizer.update(params=network_.params, grads=grads)
else:
optimizer_BN.update(params=network_.params, grads=grads)
# 每一个epoch就计算两个网络的精度然后装进数组
if i % iter_num_per_epch == 0:
network_acc = network.accuracy(x_batch, t_batch)
network_BN_acc = network_BN.accuracy(x_batch, t_batch)
acc_train_list.append(network_acc)
acc_train_BN_list.append(network_BN_acc)
epoch_num += 1
if epoch_num >= epoch_cnt_max:
break
return acc_train_list, acc_train_BN_list
optimizer = Sgd(learning_rate)
bn_optimizer = Sgd(learning_rate)
# 학습하면서 정확도의 변화를 기록
for i in range(iterations):
# 미니 배치를 랜덤하게 선택(0~999 숫자들 중 128개를 랜덤하게 선택)
mask = np.random.choice(train_size, batch_size)
x_batch = X_train[mask]
y_batch = Y_train[mask]
# 배치 정규화를 사용하지 않는 신경망에서 gradient를 계산.
gradients = neural_net.gradient(x_batch, y_batch)
# 파라미터 업데이트(갱신) - W(가중치), b(편향)을 업데이트
optimizer.update(neural_net.params, gradients)
# 업데이트된 파라미터들을 사용해서 배치 데이터의 정확도 계산
acc = neural_net.accuracy(x_batch, y_batch)
# 정확도를 기록
train_accuracies.append(acc)
# 배치 정규화를 사용하는 신경망에서 같은 작업을 수행.
bn_gradients = bn_neural_net.gradient(x_batch, y_batch) # gradient 계산
bn_optimizer.update(bn_neural_net.params, bn_gradients) # W, b 업데이트
bn_acc = bn_neural_net.accuracy(x_batch, y_batch) # 정확도 계산
bn_train_accuracies.append(bn_acc) # 정확도 기록
print(f'iteration #{i}: without={acc}, with={bn_acc}')
# 정확도 비교 그래프
x = np.arange(iterations)
plt.plot(x, train_accuracies, label='without BN')
plt.plot(x, bn_train_accuracies, label='using BN')
mini_batch_size = 100 # 1번 forward에 보낼 데이터 샘플 개수
train_size = X_train.shape[0]
iter_per_epoch = int(max(train_size / mini_batch_size, 1))
# 학습하면서 학습/테스트 데이터의 정확도를 각 에포크마다 기록
train_accuracies = []
test_accuracies = []
optimizer = Sgd(learning_rate=0.01) # optimizer
for epoch in range(epochs):
for i in range(iter_per_epoch):
x_batch = X_train[(i * mini_batch_size):((i + 1) * mini_batch_size)]
y_batch = Y_train[(i * mini_batch_size):((i + 1) * mini_batch_size)]
gradients = neural_net.gradient(x_batch, y_batch)
optimizer.update(neural_net.params, gradients)
train_acc = neural_net.accuracy(X_train, Y_train)
train_accuracies.append(train_acc)
test_acc = neural_net.accuracy(X_test, Y_test)
test_accuracies.append(test_acc)
print(f'epoch #{epoch}: train={train_acc}, test={test_acc}')
x = np.arange(epochs)
plt.plot(x, train_accuracies, label='Train')
plt.plot(x, test_accuracies, label='Test')
plt.legend()
plt.title(f'Weight Decay (lambda={wd_rate})')
plt.xlabel('epoch')
plt.ylabel('accuracy')
plt.show()
class Agent:
""" 各エージェントの動作を main からこっちに書き写す形で． """
n = int()
AdjG_init = np.zeros((n, n)) #require to be {0 or 1} to all arguments
WeiG_init = np.zeros((n, n))
maxdeg = int()
train_size = 0
batch_size = 100
# weight graph 作成規則 =====
# wtype = "maximum-degree"
wtype = "local-degree"
# ===========================
def __init__(self,
idx,
x_train,
t_train,
x_test,
t_test,
optimizer,
weight_decay_lambda=0.0):
self.idx = idx
# self.layer = MultiLayerNet(input_size=784, hidden_size_list=[100], output_size=10,
# weight_decay_lambda=weight_decay_lambda)
self.layer = MultiLayerNetExtend(
input_size=784,
hidden_size_list=[50, 50, 50],
output_size=10,
weight_decay_lambda=weight_decay_lambda,
use_dropout=False,
dropout_ration=0.0,
use_batchnorm=False)
self.optimizer = optimizer
self.rec_param = np.array([{} for i in range(self.n)])
self.x_train = x_train
self.t_train = t_train
self.x_test = x_test
self.t_test = t_test
self.z_vec = np.zeros(self.n)
self.z_vec[idx] = 1
self.rec_z = np.zeros((self.n, self.n))
self.AdjG = np.zeros(self.n) #require to be {0 or 1} to all arguments
self.WeiG = np.zeros(self.n)
if np.all(self.WeiG_init == 0):
self.makeWeiGraph(self.AdjG_init)
else:
self.WeiG = self.WeiG_init[self.idx]
self.makeAdjGraph()
self.train_loss = 0
self.train_acc = 0
self.test_acc = 0
def send(self, k, agent):
"""sending params to other nodes (return "self.layer.params"): send(agent)"""
return (self.layer.params.copy(), self.z_vec.copy())
def receive(self, agent, getparams, getz):
"""receiving other node's params: receive(agent, new_params)"""
self.rec_param[agent] = getparams.copy()
self.rec_z[agent] = getz.copy()
def selectData(self, train_size, batch_size):
batch_mask = np.random.choice(train_size, batch_size)
x_batch = self.x_train[batch_mask]
t_batch = self.t_train[batch_mask]
return x_batch, t_batch
def consensus(self):
self.weightConsensus()
self.subvalConsensus()
def weightConsensus(self):
for key in self.layer.params.keys():
self.layer.params[key] *= self.WeiG[self.idx]
for idn in np.nonzero(self.AdjG)[0]:
self.layer.params[
key] += self.WeiG[idn] * self.rec_param[idn][key]
def subvalConsensus(self):
self.rec_z[self.idx] = self.z_vec
self.z_vec = np.dot(self.WeiG, self.rec_z)
def update(self, k=1):
x_batch, t_batch = self.selectData(self.train_size, self.batch_size)
grads = self.layer.gradient(x_batch, t_batch)
self.optimizer.update(self.layer.params, grads, self.z_vec[self.idx],
k)
# self.optimizer.update(self.layer.params, grads)
def calcLoss(self):
self.train_acc = self.layer.accuracy(self.x_train, self.t_train)
self.test_acc = self.layer.accuracy(self.x_test, self.t_test)
self.train_loss = self.layer.loss(self.x_train, self.t_train)
def makeAdjGraph(self):
"""make Adjecency Graph"""
self.AdjG = self.AdjG_init[self.idx]
def makeWeiGraph(self, lAdjG):
"""make Weight matrix"""
if self.n is 1:
tmpWeiG = np.ones([1])
else:
if self.wtype == "maximum-degree":
tmpWeiG = (1 / (self.maxdeg + 1)) * lAdjG[self.idx]
tmpWeiG[self.idx] = 1 - np.sum(tmpWeiG)
elif self.wtype == "local-degree":
### count degrees ###
#degMat = np.kron(np.dot(lAdjG,np.ones([self.n,1])), np.ones([1,self.n]))
degMat = np.kron(
np.dot(lAdjG, np.ones([self.n, 1])) + 1,
np.ones([1, self.n]))
### take max() for each elements ###
degMat = np.maximum(degMat, degMat.T)
### divide for each elememts ###
tmpAllWeiG = lAdjG / degMat
selfDegMat = np.eye(self.n) - np.diag(
np.sum(tmpAllWeiG, axis=1))
tmpAllWeiG = tmpAllWeiG + selfDegMat
tmpWeiG = tmpAllWeiG[self.idx, :]
else:
try:
raise ValueError("Error: invalid weight-type")
except ValueError as e:
print(e)
self.WeiG = tmpWeiG
########
# debugging functions
########
def degub_numericalGrad(self):
return self.layer.numerical_gradient(self.x_train[:3],
self.t_train[:3])
def debug_backpropGrad(self):
return self.layer.gradient(self.x_train[:3], self.t_train[:3])
def debug_consensus(self):
params = self.layer.params.copy()
self.weightConsensus()
self.subvalConsensus()
if self.idx == 0:
ano_params = self.layer.params.copy()
for key in params.keys():
diff = np.average(np.abs(params[key] - ano_params[key]))
print(key + ":" + str(diff))
for i in range(10000):
batch_mask = np.random.choice(train_size, batch_size)
x_batch = x_train[batch_mask]
t_batch = t_train[batch_mask]
# 学習フェーズ
grads = network.gradient(x_train, t_train)
optimizer.update(network.params, grads)
if i % iter_per_epoch == 0:
#loss_train = network.loss(x_train, t_train)
#loss_test = network.loss(x_test, t_test)
#train_loss_list.append(loss_train)
#test_loss_list.append(loss_test)
train_acc = network.accuracy(x_train, t_train)
test_acc = network.accuracy(x_test, t_test)
train_acc_list.append(train_acc)
test_acc_list.append(test_acc)
epoch_cnt += 1
print("---{}/{}---".format(epoch_cnt, max_epochs))
#print("loss_train : " + str(loss_train))
#print("loss_test : " + str(loss_test))
print("acc_train : " + str(train_acc))
print("acc_test : " + str(test_acc))
if epoch_cnt >= max_epochs:
break
#plt.plot(train_loss_list, label="train_loss_list")
#plt.plot(test_loss_list, label="test_loss_list")
class Agent:
"""アルゴリズム構築に必要なAgentの機能
Function:
send : 隣接するagentの以下の状態変数を送る
Args:
layer.param (np.array) : 各層のパラメータ
receive : 隣接するagentから状態変数を受け取る
Args:
layer.param (np.arrar) : 各層のパラメータ
optimizer(SGD) : 確率勾配をバックプロパゲーションとランダムシードを用いて実装
For example:
self.optimizer = optimizer(SGD(lr))
x_batch, t_batch = self.selectData(self.train_size, self.batch_size)
grads = self.layer.gradient(x_batch, t_batch)
self.optimizer.update(self.layer.params, grads, k)
"""
""" 各エージェントの動作を main からこっちに書き写す形で． """
n = int()
AdjG_init = np.zeros((n, n)) #require to be {0 or 1} to all arguments
WeiG_init = np.zeros((n, n))
maxdeg = int()
train_size = 0
batch_size = 100
# weight graph 作成規則 =====
# wtype = "maximum-degree"
wtype = "local-degree"
# ===========================
def __init__(self,
idx,
x_train,
t_train,
x_test,
t_test,
optimizer,
weight_decay_lambda=0.0):
"""各Agentの初期状態変数
Args:
idx : Agentのインデックス
layer : Agent内のニューラルネットワークの層
optimizer : 最適化を行うアルゴリズムの選択
rec_param : 隣接するエージェントから受け取るパラメータ
z_vec : 左の固有ベクトル
rec_z : 隣接するエージェントから受け取る左固有ベクトル
AdjG : 隣接行列？？
WeiG : 重み行列？？
"""
self.idx = idx
# self.layer = MultiLayerNet(input_size=784, hidden_size_list=[100], output_size=10,
# weight_decay_lambda=weight_decay_lambda)
self.layer = MultiLayerNetExtend(
input_size=784,
hidden_size_list=[
500, 400, 300, 300, 200, 200, 100, 100, 100, 100, 100, 50, 50
],
output_size=10,
weight_decay_lambda=weight_decay_lambda,
use_dropout=True,
dropout_ration=0.3,
use_batchnorm=True)
#dropout_ratio=0.03, use_batchnorm=True, hidden_size_list=[500,400,300,300,200,200,100,100,100,50,50,50] weightdecay=0.01 → 0.9428
#hidden_size_list=[500,400,300,300,200,200,100,100,100,50,50,50], output_size=10,weight_decay_lambda=weight_decay_lambda,use_dropout=True, dropout_ration=0.05, use_batchnorm=True 一番いい
#hidden_size_list=[100,100,100,100,100] weightdecay=0.3, dropout_ration=0.3
self.optimizer = optimizer
self.rec_param = np.array([{} for i in range(self.n)])
self.send_param = np.array([{} for i in range(self.n)])
#Initialize
self.rec_param[self.idx] = self.layer.params.copy()
self.send_param[self.idx] = self.layer.params.copy()
self.x_train = x_train
self.t_train = t_train
self.x_test = x_test
self.t_test = t_test
self.AdjG = np.zeros(self.n) #require to be {0 or 1} to all arguments
self.WeiG = np.zeros(self.n)
if np.all(self.WeiG_init == 0):
self.makeWeiGraph(self.AdjG_init)
else:
self.WeiG = self.WeiG_init[self.idx]
self.makeAdjGraph()
self.train_loss = 0
self.train_acc = 0
self.test_acc = 0
#山下さんのアルゴリズム構築に必要な関数
# def send(self, k, agent):
# """sending params to other nodes (return "self.layer.params"): send(agent)"""
# return (self.layer.params.copy(), self.z_vec.copy())
# def receive(self, agent, getparams, getz):
# """receiving other node's params: receive(agent, new_params)"""
# self.rec_param[agent] = getparams.copy()
# self.rec_z[agent] = getz.copy()
def send(self, k, agent):
"""sending params to other nodes (return "self.layer.params"): send(agent)"""
self.send_param[self.idx] = self.layer.params.copy()
return self.layer.params.copy()
def receive(self, agent, getparams):
"""receiving other node's params: receive(agent, new_params)"""
for key in getparams.keys():
self.rec_param[agent][key] = getparams[key].copy()
def selectData(self, train_size, batch_size):
batch_mask = np.random.choice(train_size, batch_size)
x_batch = self.x_train[batch_mask]
t_batch = self.t_train[batch_mask]
return x_batch, t_batch
def consensus(self):
self.weightConsensus()
# self.subvalConsensus()
def weightConsensus(self):
for key in self.layer.params.keys():
self.layer.params[key] *= self.WeiG[self.idx]
for idn in np.nonzero(self.AdjG)[0]:
self.layer.params[
key] += self.WeiG[idn] * self.rec_param[idn][key]
# def subvalConsensus(self):
# self.rec_z[self.idx] = self.z_vec
# self.z_vec = np.dot(self.WeiG, self.rec_z)
def update(self, k=1):
x_batch, t_batch = self.selectData(self.train_size, self.batch_size)
grads = self.layer.gradient(x_batch, t_batch)
# self.optimizer.update(self.layer.params, grads, self.z_vec[self.idx], k)
self.send_param[self.idx], self.rec_param[
self.idx] = self.optimizer.update(self.layer.params, grads,
self.rec_param[self.idx],
self.send_param[self.idx],
self.WeiG[self.idx], k)
def calcLoss(self):
self.train_acc = self.layer.accuracy(self.x_train, self.t_train)
self.test_acc = self.layer.accuracy(self.x_test, self.t_test)
self.train_loss = self.layer.loss(self.x_train, self.t_train)
def makeAdjGraph(self):
"""make Adjecency Graph"""
self.AdjG = self.AdjG_init[self.idx]
def makeWeiGraph(self, lAdjG):
"""make Weight matrix
2020/01/28 山下さんは有効グラフを作成している．無向グラフに変更("maximum-degree"の方のみ)
Args:
tmpWeiG (np.array) : 一次的な重み行列
"""
if self.n is 1:
tmpWeiG = np.ones([1])
else:
if self.wtype == "maximum-degree":
tmpWeiG = (1 / (self.maxdeg + 1)) * lAdjG[self.idx]
tmpWeiG[self.idx] = 1 - np.sum(tmpWeiG)
elif self.wtype == "local-degree":
### count degrees ###
#degMat = np.kron(np.dot(lAdjG,np.ones([self.n,1])), np.ones([1,self.n]))
degMat = np.kron(
np.dot(lAdjG, np.ones([self.n, 1])) + 1,
np.ones([1, self.n]))
### take max() for each elements ###
degMat = np.maximum(degMat, degMat.T)
### divide for each elememts ###
tmpAllWeiG = lAdjG / degMat
selfDegMat = np.eye(self.n) - np.diag(
np.sum(tmpAllWeiG, axis=1))
tmpAllWeiG = tmpAllWeiG + selfDegMat
tmpWeiG = tmpAllWeiG[self.idx, :]
else:
try:
raise ValueError("Error: invalid weight-type")
except ValueError as e:
print(e)
self.WeiG = tmpWeiG
########
# debugging functions
########
def degub_numericalGrad(self):
return self.layer.numerical_gradient(self.x_train[:3],
self.t_train[:3])
def debug_backpropGrad(self):
return self.layer.gradient(self.x_train[:3], self.t_train[:3])
def debug_consensus(self):
params = self.layer.params.copy()
self.weightConsensus()
self.subvalConsensus()
if self.idx == 0:
ano_params = self.layer.params.copy()
for key in params.keys():
diff = np.average(np.abs(params[key] - ano_params[key]))
print(key + ":" + str(diff))
# epoch = 0
for i in range(iterations):
# 미니 배치를 랜덤하게 선택 (0~999 숫자들 중 128개를 랜덤하게 선택)
mask = np.random.choice(train_size, batch_size)
x_batch = X_train[mask]
y_batch = Y_train[mask]
# 배치 정규화
# 배치 정규화를 사용하지 않는 신경망에서 gradient 계산
gradients = neural_net.gradient(x_batch, y_batch)
# 파라미터 업데이트 -> W와 b 변경
optimizer.update(neural_net.params, gradients)
# 업데이트된 배치 데이터의 정확도를 계산
accuracy = neural_net.accuracy(x_batch, y_batch)
# 정확도를 기록
neural_accuracy.append(accuracy)
# 배치 정규화를 사용하는 신경망에서 gradient 계산
bn_gradients = bn_neural_net.gradient(x_batch, y_batch)
# 파라미터 업데이트 -> W와 b 변경
bn_optimizer.update(bn_neural_net.params, bn_gradients)
# 업데이트된 배치 데이터의 정확도를 계산
bn_accuracy = bn_neural_net.accuracy(x_batch, y_batch)
# 정확도를 기록
bn_neural_accuracy.append(bn_accuracy)
print(
f'iteration #{i}: without = {neural_accuracy[i]}, with = {bn_neural_accuracy[i]}'
)
batch_size = 128
learning_rate = 0.01
neural_optimizer = Sgd()
bn_neural_optimizer = Sgd()
neural_acc_list = []
bn_neural_acc_list = []
for i in range(iterator):
mask = np.random.choice(X_train.shape[0], batch_size)
X_batch = X_train[mask]
Y_batch = Y_train[mask]
for network in (neural_net, bn_neural_net):
gradient = network.gradient(X_batch, Y_batch)
if network == neural_net:
neural_optimizer.update(network.params, gradient)
neural_acc = neural_net.accuracy(X_batch, Y_batch)
neural_acc_list.append(neural_acc)
else:
bn_neural_optimizer.update(network.params, gradient)
bn_neural_acc = bn_neural_net.accuracy(X_batch, Y_batch)
bn_neural_acc_list.append(bn_neural_acc)
print(f'===== {i}번째 training =====')
print('Without BatchNorm', neural_acc_list[-1])
print('BatchNorm', bn_neural_acc_list[-1])
# gradients = neural_net.gradient(X_batch, Y_batch)
# neural_optimizer.update(neural_net.params, gradients)
# neural_acc = neural_net.accuracy(X_batch, Y_batch)
# neural_acc_list.append(neural_acc)
#
# bn_gradients = bn_neural_net.gradient(X_batch, Y_batch)
# 随机挑选出batch_size个样本
batch_mask = np.random.choice(train_size, batch_size)
x_batch = x_train[batch_mask]
t_batch = t_train[batch_mask]
# 前向传播，反向传播，获取梯度
for _network in (network, network_weight_decay):
grads = _network.gradient(x_batch,
t_batch,
use_weight_decay=_network.use_weight_decay)
optimizer.update(_network.params, grads)
if i % iter_num_per_epoch == 0:
epoch_num += 1
train_acc_network = network.accuracy(x_train, t_train)
train_acc_list.append(train_acc_network)
test_acc_network = network.accuracy(x_test, t_test)
test_acc_list.append(train_acc_network)
train_acc_network_weight_decay = network_weight_decay.accuracy(
x_train, t_train)
train_acc_weight_decay_list.append(train_acc_network_weight_decay)
test_acc_network_weight_decay = network_weight_decay.accuracy(
x_test, t_test)
test_acc_weight_decay_list.append(test_acc_network_weight_decay)
if epoch_num >= epoch_cnt_max:
break
