text: # 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
  import matplotlib.pyplot as plt
  from dataset.mnist import load_mnist
  from two_layer_net import TwoLayerNet
  
  # 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 = TwoLayerNet(input_size=784, hidden_size=50, output_size=10)
  
  iters_num = 10000  # 繰り返しの回数を適宜設定する
  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)
  
  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]
      
      # 勾配の計算
      #grad = network.numerical_gradient(x_batch, t_batch)
      grad = network.gradient(x_batch, t_batch)
      
      # パラメータの更新
      for key in ('W1', 'b1', 'W2', 'b2'):
          network.params[key] -= learning_rate * grad[key]
      
      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("train acc, test acc | " + str(train_acc) + ", " + str(test_acc))
  
  # グラフの描画
  markers = {'train': 'o', 'test': 's'}
  x = np.arange(len(train_acc_list))
  plt.figure()
  plt.plot(x, train_acc_list, label='train acc')
  plt.plot(x, test_acc_list, label='test acc', linestyle='--')
  plt.xlabel("epochs")
  plt.ylabel("accuracy")
  plt.xlim(0, )
  plt.ylim(0, 1.0)
  plt.legend(loc='lower right')
  
  x = np.arange(len(train_loss_list))
  plt.figure()
  plt.plot(x, train_loss_list)
  plt.xlabel("iteration")
  plt.ylabel("loss")
  plt.xlim(0, 10000)
  plt.ylim(0, 9)
  
  x = np.arange(1000)
  plt.figure()
  plt.plot(x, train_loss_list[0:1000])
  plt.xlabel("iteration")
  plt.ylabel("loss")
  plt.xlim(0, 1000)
  plt.ylim(0, 9)
  
  plt.show()
  