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 __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 main():
(train_x, train_label), (test_x, test_label) = load_mnist()
# 为了再现过拟合，减少学习数据
train_x = train_x[: 300]
train_label = train_label[: 300]
# 设定是否使用Dropuout，以及比例 ========================
use_dropout = False
# use_dropout = True
dropout_ratio = 0.2
network = MultiLayerNetExtend(
input_size=784, hidden_size_list=[100, 100, 100, 100, 100, 100],
output_size=10, use_dropout=use_dropout, dropout_ratio=dropout_ratio)
trainer = Trainer(network, train_x, train_label, test_x, test_label,
epochs=301, mini_batch_size=100, optimizer='sgd',
optimizer_param={'lr': 0.01}, verbose=True)
trainer.train()
train_acc_list = trainer.train_acc_list
test_acc_list = trainer.test_acc_list
draw(train_acc_list, test_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 __train(lr, weight_decay, epocs=50):
network = MultiLayerNetExtend(
input_size=784,
hidden_size_list=[100, 100, 100, 100, 100, 100],
output_size=10,
weight_decay_lambda=weight_decay)
trainer = Trainer(network,
x_train,
t_train,
x_val,
t_val,
epochs=epocs,
mini_batch_size=100,
optimizer='sgd',
optimizer_param={'lr': lr},
verbose=False)
trainer.train()
return trainer.test_acc_list, trainer.train_acc_list
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 __train(lr, weight_decay_lambda, epoch_num=120):
network = MultiLayerNetExtend(input_size=784,
hidden_size_list=[100] * 5,
output_size=10,
activation='ReLu',
weight_init_std='ReLu',
weight_decay_lambda=weight_decay_lambda,
use_BatchNormalization=False,
use_weight_decay=True)
# 注意下面传入的不是测试集而是验证集
trainer = Trainer(network=network,
x_train=x_train,
t_train=t_train,
x_test=x_val,
t_test=t_val,
epochs=epoch_num,
mini_batch_num=100,
optimizer='SGD',
optimizer_params={'lr': lr})
trainer.train()
return trainer.train_acc_list, trainer.test_acc_list # 返回一次实验测试集和验证集的精度
def main():
# 读入数据
(train_x, train_label), _ = load_mnist(one_hot_label=True)
# 构造神经网络
network = MultiLayerNetExtend(input_size=784,
hidden_size_list=[100, 100],
output_size=10,
use_batchnorm=True)
# 仅用一个训练样本来测试
batch_x = train_x[:1]
batch_label = train_label[:1]
# 用反向传播和数值方法分别计算梯度
grad_backprop = network.gradient(batch_x, batch_label)
grad_numerical = network.numerical_gradient(batch_x, batch_label)
# 比较两种方法的计算结果
for key in grad_numerical.keys():
diff = np.average(np.abs(grad_backprop[key] - grad_numerical[key]))
print(key + ":" + str(diff))
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
# p.213 그림 6-18을 그리세요.
# Batch Normalization을 사용하는 신경망과 사용하지 않는 신경망의 학습 속도 비교
import numpy as np
import matplotlib.pyplot as plt
from ch06.e02_sgd import Sgd
from common.multi_layer_net_extend import MultiLayerNetExtend
from common.optimizer import Momentum, AdaGrad, Adam
from dataset.mnist import load_mnist
# 배치 정규화를 사용하는 신경망
bn_neural_net = MultiLayerNetExtend(input_size=784,
hidden_size_list=[100, 100, 100, 100, 100],
output_size=10,
weight_init_std=0.01,
use_batchnorm=True)
# 배치 정규화를 사용하지 않는 신경망
neural_net = MultiLayerNetExtend(input_size=784,
hidden_size_list=[100, 100, 100, 100, 100],
output_size=10,
weight_init_std=0.01,
use_batchnorm=False)
# 미니 배치를 20번 학습시키면서, 두 신경망에서 정확도(accuracy)를 기록
# -> 그래프
(X_train, Y_train), (X_test, Y_test) = load_mnist(one_hot_label=True)
# 학습 시간을 줄이기 위해서 학습 데이터의 개수를 줄임.
X_train = X_train[:1000] # 데이터 1000개만 사용
from ch06.ex02_sgd import Sgd
from common.multi_layer_net import MultiLayerNet
from common.multi_layer_net_extend import MultiLayerNetExtend
from dataset.mnist import load_mnist
np.random.seed(110)
# 데이터 준비
(X_train, Y_train), (X_test, Y_test) = load_mnist(one_hot_label=True)
# 신경망 생성
wd_rate = 0
neural_net = MultiLayerNetExtend(input_size=784,
hidden_size_list=[100, 100, 100, 100, 100],
output_size=10,
weight_decay_lambda=wd_rate,
use_dropout=True,
dropout_ration=0.15)
# weight_decay_lambda: 가중치 감소에 사용할 상수 값
# 학습 데이터 개수를 300개로 제한 -> overfitting 만들기 위해서
X_train = X_train[:300]
Y_train = Y_train[:300]
X_test = X_test[:300] # 실험 시간 줄이기 위해서
Y_test = Y_test[:300]
epochs = 200 # 1 에포크: 모든 학습 데이터가 1번씩 학습된 경우
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
print(x)
mask = x > 0.5
print(mask)
print(x *
mask) # false = 0, false * x = 0; true = 1, returns its original value
np.random.seed(110)
# 데이터 준비
(X_train, Y_train), (X_test, Y_test) = load_mnist(one_hot_label=True)
# 신경망 생성
dropout_ratio = 0.1
neural_net = MultiLayerNetExtend(input_size=784,
hidden_size_list=[100, 100, 100, 100, 100],
output_size=10,
use_dropout=True,
dropout_ration=dropout_ratio)
X_train = X_train[:500]
Y_train = Y_train[:500]
X_test = X_test[:500]
Y_test = Y_test[:500]
# Initial Idea
# # drop out 구현 (Affine > Batch > ReLU > Dropout 이니까 Relu값을 가지고 dropout을 구현한다)
#
train_size = X_test.shape[0]
epochs = 200
mini_batch_size = 100
mask_train = np.random.choice(x_train.shape[0], 1000)
mask_test = np.random.choice(x_test.shape[0], 1000)
x_train = x_train[mask_train]
t_train = t_train[mask_train]
x_test = x_test[mask_test]
t_test = t_test[mask_test]
train_size = x_train.shape[0]
batch_size = 128
optimizer = SGD(lr=0.01)
network_st = MultiLayerNet(input_size=784,
hidden_size_list=[100, 100, 100],
output_size=10)
network_bn = MultiLayerNetExtend(input_size=784,
hidden_size_list=[100, 100, 100],
output_size=10,
use_batchnorm=True)
loss_list = {
"network_st": [],
"network_bn": [],
"network_st_test": [],
"network_bn_test": []
}
for i in range(iter_num):
batch_mask = np.random.choice(train_size, batch_size)
x_batch = x_train[batch_mask]
t_batch = t_train[batch_mask]
x_test_batch = x_test[batch_mask]
t_test_batch = t_test[batch_mask]
#念のため、データの次元数の確認
print("x_train,t_train dimension_number")
print(x_train.shape,t_train.shape)
print("x_test,t_test dimension_number")
print(x_test.shape,t_test.shape)
# 高速化のため訓練データの削減
#x_train = x_train[:56678]
#t_train = t_train[:56678]
#optimizerを選択
optimizers = Adam()
network = MultiLayerNetExtend(input_size=122, hidden_size_list=[1000,1000], output_size=2,
activation='relu', weight_init_std=0.01,
weight_decay_lambda=0, use_dropout=True, dropout_ration=0.5,
use_batchnorm=False)
#ハイパーパラメータ
iters_num = 73554 #イテレーションの回数
batch_size = 512
learning_rate = 0.00001 #学習率
train_size = x_train.shape[0] #x_trainの行数を当てはめる
#格納するリストの作成
train_loss_list = []
train_acc_list = []
test_acc_list = []
iter_per_epoch_float = max(train_size / batch_size, 1) #1エポック回すのに何イテレーションかかるか
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
from ch06.ex02_sgd import Sgd
from common.multi_layer_net_extend import MultiLayerNetExtend
from common.optimizer import Momentum
from dataset.mnist import load_mnist
import numpy as np
import matplotlib.pyplot as plt
#p.213의 그림 그려보기
# Batch Normalization을 사용하는 신경망과 사용하지 않는 신경망의 학습 속도 비교
np.random.seed(110)
# 배치 정규화를 사용하는 신경망
bn_neural_net = MultiLayerNetExtend(
input_size=784,
hidden_size_list=[100, 100, 100, 100, 100], #뉴런 100개짜리 계층 5개 생성
output_size=10,
weight_init_std=0.3, # [W1, W2, W3, W4, W5]과 ~~~~ 했을 때, std = 0.01
use_batchnorm=True)
# 배치 정규화를 사용하지 않는 신경망
neural_net = MultiLayerNetExtend(
input_size=784,
hidden_size_list=[100, 100, 100, 100, 100], #뉴런 100개짜리 계층 5개 생성
output_size=10,
weight_init_std=0.3, # [W1, W2, W3, W4, W5]과 ~~~~ 했을 때, std = 0.01
use_batchnorm=False)
# MNIST 데이터를 불러온다
(X_train, Y_train), (X_test, Y_test) = load_mnist(one_hot_label=True)
# 학습시간을 줄이기 위해서 학습 데이터의 개수를 줄임
use_dropout = True
dropout_ratio = np.linspace(0, 0.2, 5)
weight_decay = np.geomspace(0.001, 0.2, num=5)
learning_rate = np.geomspace(0.001, 0.2, num=5)
best_hp = []
# Training begins
with progressbar.ProgressBar(max_value=125) as bar:
for i in range(0, len(dropout_ratio)):
for j in range(0, len(weight_decay)):
for k in range(0, len(learning_rate)):
network = MultiLayerNetExtend(
input_size=784,
hidden_size_list=[100, 100, 100, 100],
output_size=10,
activation='sigmoid',
weight_init_std='xavier',
weight_decay_lambda=weight_decay[j],
use_dropout=use_dropout,
dropout_ration=dropout_ratio[i])
trainer = Trainer(network,
x_train,
t_train,
x_test,
t_test,
epochs=5,
mini_batch_size=500,
optimizer='adam',
optimizer_param={'lr': learning_rate[k]},
verbose=False)
trainer.train()
from common.trainer import Trainer
(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True)
# 为了再现过拟合，减少学习数据
x_train = x_train[:300]
t_train = t_train[:300]
# 设定是否使用Dropuout，以及比例 ========================
use_dropout = True # 不使用Dropout的情况下为False
dropout_ratio = 0.2
# ====================================================
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)
trainer = Trainer(network,
x_train,
t_train,
x_test,
t_test,
epochs=301,
mini_batch_size=100,
optimizer='sgd',
optimizer_param={'lr': 0.01},
verbose=True)
trainer.train()
train_acc_list, test_acc_list = trainer.train_acc_list, trainer.test_acc_list
output_size = 10
activation = 'ReLu' # 每一层之间的激活函数
weight_init_std = 'ReLu' # 根据激活函数以初始化权值分布
weight_decay_lambda = 1
epoch_num = 0
train_acc_list = []
test_acc_list = []
train_acc_weight_decay_list = []
test_acc_weight_decay_list = []
# 构造网络对象
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_weight_decay = 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=True)
# 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
