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 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))
# coding: utf-8
import sys, os
sys.path.append(os.pardir) # 为了导入父目录的文件而进行的设定
import numpy as np
from dataset.mnist import load_mnist
from common.multi_layer_net_extend import MultiLayerNetExtend
# 读入数据
(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, one_hot_label=True)
network = MultiLayerNetExtend(input_size=784, hidden_size_list=[100, 100], output_size=10,
use_batchnorm=True)
x_batch = x_train[:1]
t_batch = t_train[:1]
grad_backprop = network.gradient(x_batch, t_batch)
grad_numerical = network.numerical_gradient(x_batch, t_batch)
for key in grad_numerical.keys():
diff = np.average( np.abs(grad_backprop[key] - grad_numerical[key]) )
print(key + ":" + str(diff))
bn_train_accuracies = [] # 배치 정규화를 사용하는 신경망의 정확도를 기록
# optimizer = Sgd(learning_rate)
# 파라미터 최적화 알고리즘이 SGD가 아닌 경우에는 신경망 개수만큼 optimizer를 생성.
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}')
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
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')
# coding: utf-8
import sys, os
sys.path.append(os.pardir) # 親ディレクトリのファイルをインポートするための設定
import numpy as np
from dataset.mnist import load_mnist
from common.multi_layer_net_extend import MultiLayerNetExtend
# データの読み込み
(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, one_hot_label=True)
network = MultiLayerNetExtend(input_size=784, hidden_size_list=[100, 100], output_size=10,
use_batchnorm=True)
x_batch = x_train[:1]
t_batch = t_train[:1]
grad_backprop = network.gradient(x_batch, t_batch)
grad_numerical = network.numerical_gradient(x_batch, t_batch)
for key in grad_numerical.keys():
diff = np.average( np.abs(grad_backprop[key] - grad_numerical[key]) )
print(key + ":" + str(diff))
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))
train_loss_list = []
test_loss_list = []
train_acc_list = []
test_acc_list = []
iter_per_epoch = max(train_size / batch_size, 1)
epoch_cnt = 0
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))
#平均計算をするために使用されるfloat型
count = 0.0
train_sum = 0.0
test_sum = 0.0
#処理前の時刻
t1 = time.time()
for i in range(iters_num): #イテレーションの回数分for文を回している
batch_mask = np.random.choice(train_size, batch_size) #batch_size分のbatch_size行列の乱数を作成
x_batch = x_train[batch_mask] #x_subsetの作成
t_batch = t_train[batch_mask] #t_subsetの作成
# 勾配
#grad = network.numerical_gradient(x_batch, t_batch)
grad = network.gradient(x_batch, t_batch)
optimizers.update(network.params, grad)
# 更新
#for key in ('W1', 'b1', 'W2', 'b2'):
#network.params[key] -= learning_rate * grad[key]
loss = network.loss(x_batch, t_batch) #損失を計算
train_loss_list.append(loss) #損失リストに格納する
if i % iter_per_epoch == 0: #イテレーションの回数分回している中で、iがiter_per_epochの倍数の時に以下を実行
#それぞれの認識精度を計算
train_acc = network.accuracy(x_train, t_train)
test_acc = network.accuracy(x_test, t_test)
#認識精度のリストに格納する
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))
