text: # epoch - 0 , train_acc - 0.2669 , test_acc - 0.2775
  # epoch - 1 , train_acc - 0.945066666667 , test_acc - 0.9449
  # epoch - 2 , train_acc - 0.966366666667 , test_acc - 0.9615
  # epoch - 3 , train_acc - 0.977516666667 , test_acc - 0.9698
  # epoch - 4 , train_acc - 0.982216666667 , test_acc - 0.9718
  # epoch - 5 , train_acc - 0.983283333333 , test_acc - 0.9723
  # epoch - 6 , train_acc - 0.989316666667 , test_acc - 0.9778
  # epoch - 7 , train_acc - 0.987283333333 , test_acc - 0.9751
  # epoch - 8 , train_acc - 0.99195 , test_acc - 0.9775
  # epoch - 9 , train_acc - 0.994066666667 , test_acc - 0.9779
  # epoch - 10 , train_acc - 0.992933333333 , test_acc - 0.9769
  # epoch - 11 , train_acc - 0.9953 , test_acc - 0.9797
  # epoch - 12 , train_acc - 0.9945 , test_acc - 0.9772
  # epoch - 13 , train_acc - 0.995633333333 , test_acc - 0.9787
  # epoch - 14 , train_acc - 0.996316666667 , test_acc - 0.9772
  # epoch - 15 , train_acc - 0.9979 , test_acc - 0.9803
  # epoch - 16 , train_acc - 0.996116666667 , test_acc - 0.9771
  import sys, os
  
  sys.path.append(os.pardir)
  
  import numpy as np
  from DeepLearningClass.dataset.mnist import load_mnist
  from DeepLearningClass.chapter5.two_layer_net_3_layer import TwoLayerNet
  from DeepLearningClass.common.optimizer import Adam
  
  # 데이터 읽기
  (x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, one_hot_label=True)
  
  network = TwoLayerNet(input_size=784, hidden_size1=200, hidden_size2=200, output_size=10)
  
  iters_num = 10000
  train_size = x_train.shape[0]
  batch_size = 100
  learning_rate = 0.1
  
  train_loss_list = []
  train_acc_list = []
  test_acc_list = []
  
  iter_per_epoch = max(train_size / batch_size, 1)
  
  adam = Adam()
  
  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]
  
      # 기울기 계산
      # grad = network.numerical_gradient(x_batch, t_batch) # 수치 미분 방식
      grad = network.gradient(x_batch, t_batch)  # 오차역전파법 방식(훨씬 빠르다)
  
      # 갱신
      adam.update(network.params, grad)
  
      loss = network.loss(x_batch, t_batch)
      train_loss_list.append(loss)
  
      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 -', int(i / iter_per_epoch), ', train_acc -', train_acc, ', test_acc -', test_acc)