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
  net = TwoLayerNet(input_size = 784, hidden_size = 100, output_size = 10)
  print(net.params['W1'].shape)
  print(net.params['b1'].shape)
  print(net.params['W2'].shape)
  print(net.params['b2'].shape)