text: # !usr/bin/env python
  # -*- coding: utf-8 -*-
  
  import sys, os
  sys.path.append(os.pardir)
  import numpy as np
  from dataset.mnist import load_mnist
  from two_layer_net import TwoLayerNet
  
  (x_train, t_train), (x_test, t_test) =\
      load_mnist(normalize=True, one_hot_label=True)
  
  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)
  
  network = TwoLayerNet(input_size=784, hidden_size=50, output_size=10)
  
  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]
  
      grad = network.gradient(x_batch, t_batch)
  
      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. {0:.5f} : test acc. {1:.5f}".format(train_acc, test_acc))
  