text: import sys, os
  sys.path.append(os.pardir)
  from common.functions import *
  from common.gradient import numerical_gradient
  
  class TwoLayerNet:
    def __init__(self, input_size, hidden_size, output_size, weight_init_std=0.01):
      self.params = {}
      self.params['W1'] = weight_init_std * np.random.randn(input_size, hidden_size)
      self.params['b1'] = np.zeros(hidden_size)
      self.params['W2'] = weight_init_std * np.random.randn(hidden_size, output_size)
      self.params['b2'] = np.zeros(output_size)
    
    def predict(self, x):
      W1, W2 = self.params['W1'], self.params['W2']
      b1, b2 = self.params['b1'], self.params['b2']
  
      a1 = np.dot(x, W1) + b1
      z1 = sigmoid(a1)
      a2 = np.dot(z1, W2) + b2
      y = softmax(a2)
  
      return y
    
    def loss(self, x, t):
      y = self.predict(x)
      return cross_entropy_error(y, t)
  
    def accuracy(self, x, t):
      y = self.predict(x)
      y = np.argmax(y, axis=1)
      t = np.argmax(t, axis=1)
  
      accuracy = np.sum(y == t) / float(x.shape[0])
      return accuracy
    
    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
  