new_contents: 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
  from common.layers import *
  from common.gradient import numerical_gradient
  from collections import OrderedDict
  
  class TwoLayerNet:
      
      def __init__(self, input_size, hidden_size, output_size, weight_init_std=0.01):
          # 重みの初期化
          self.params = {}
          self.params['W1'] = weight_init_std * np.random.randn(input_size, hidden_size)
          self.params['W2'] = weight_init_std * np.random.randn(hidden_size, output_size)
          self.params['b1'] = np.zeros(hidden_size)
          self.params['b2'] = np.zeros(output_size)
          
          # レイヤの生成
          self.layers = OrderedDict()
          self.layers['Affine1'] = Affine(self.params['W1'], self.params['b1'])
          self.layers['Relu1'] = Relu()
          self.layers['Affine2'] = Affine(self.params['W2'], self.params['b2'])
  
          self.lastLayer = SoftmaxWithLoss()
      
      def predict(self, x):
          for layer in self.layers.values():
              x = layer.forward(x)
          
          return x
      
      # x: 入力データ
      # t: 教師データ
      def loss(self, x, t):
          y = self.predict(x)
          return self.lastLayer.forward(y, t)
          
      # 認識制度を求める
      def accuracy(self, x, t):
          y = self.predict(x)
          y = np.argmax(y, axis=1)
          if t.ndim != 1 : t = np.argmax(t, axis=1)
          
          accuracy = np.sum(y == t) / float(x.shape[0])
          return accuracy
          
      # 重みパラメータに対する勾配を求める
      def numerical_gradient(self, x, t):
          loss_W = lambda W: self.loss(x, t)
          
          grads = {}
          grads['W1'] = numerical_gradient(loss_W, self.params['W1'])
          grads['b1'] = numerical_gradient(loss_W, self.params['b1'])
          grads['W2'] = numerical_gradient(loss_W, self.params['W2'])
          grads['b2'] = numerical_gradient(loss_W, self.params['b2'])
          
          return grads
      
      # 重みパラメータに対する勾配を求める(誤差逆伝播法)
      def gradient(self, x, t):
          # forward
          self.loss(x, t)
  
          # backward
          dout = 1
          dout = self.lastLayer.backward(dout)
          
          layers = list(self.layers.values())
          layers.reverse()
          for layer in layers:
              dout = layer.backward(dout)
  
          # 設定
          grads = {}
          grads['W1'], grads['b1'] = self.layers['Affine1'].dW, self.layers['Affine1'].db
          grads['W2'], grads['b2'] = self.layers['Affine2'].dW, self.layers['Affine2'].db
  
          return grads
  
subject: Add backpropagation code.