old_contents: # coding: utf-8
  import numpy as np
  
  
  def identity_function(x):
      return x
  
  
  def step_function(x):
      return np.array(x > 0, dtype=np.int)
  
  
  def sigmoid(x):
      return 1 / (1 + np.exp(-x))    
  
  
  def sigmoid_grad(x):
      return (1.0 - sigmoid(x)) * sigmoid(x)
      
  
  def relu(x):
      return np.maximum(0, x)
  
  
  def relu_grad(x):
      grad = np.zeros(x)
      grad[x>=0] = 1
      return grad
      
  
  def softmax(x):
      if x.ndim == 2:
          x = x.T
          x = x - np.max(x, axis=0)
          y = np.exp(x) / np.sum(np.exp(x), axis=0)
          return y.T 
  
      x = x - np.max(x) # オーバーフロー対策
      return np.exp(x) / np.sum(np.exp(x))
  
  
  def mean_squared_error(y, t):
      return 0.5 * np.sum((y-t)**2)
  
  
  def cross_entropy_error(y, t):
      if y.ndim == 1:
          t = t.reshape(1, t.size)
          y = y.reshape(1, y.size)
          
      # 教師データがone-hot-vectorの場合、正解ラベルのインデックスに変換
      if t.size == y.size:
          t = t.argmax(axis=1)
               
      batch_size = y.shape[0]
      return -np.sum(np.log(y[np.arange(batch_size), t])) / batch_size
  
  
  def softmax_loss(X, t):
      y = softmax(X)
      return cross_entropy_error(y, t)
  
new_contents: # coding: utf-8
  import numpy as np
  
  
  def identity_function(x):
      return x
  
  
  def step_function(x):
      return np.array(x > 0, dtype=np.int)
  
  
  def sigmoid(x):
      return 1 / (1 + np.exp(-x))    
  
  
  def sigmoid_grad(x):
      return (1.0 - sigmoid(x)) * sigmoid(x)
      
  
  def relu(x):
      return np.maximum(0, x)
  
  
  def relu_grad(x):
      grad = np.zeros(x)
      grad[x>=0] = 1
      return grad
      
  
  def softmax(x):
      if x.ndim == 2:
          x = x.T
          x = x - np.max(x, axis=0)
          y = np.exp(x) / np.sum(np.exp(x), axis=0)
          return y.T 
  
      x = x - np.max(x) # オーバーフロー対策
      return np.exp(x) / np.sum(np.exp(x))
  
  
  def mean_squared_error(y, t):
      return 0.5 * np.sum((y-t)**2)
  
  
  def cross_entropy_error(y, t):
      if y.ndim == 1:
          t = t.reshape(1, t.size)
          y = y.reshape(1, y.size)
          
      # 教師データがone-hot-vectorの場合、正解ラベルのインデックスに変換
      if t.size == y.size:
          t = t.argmax(axis=1)
               
      batch_size = y.shape[0]
      return -np.sum(np.log(y[np.arange(batch_size), t] + 1e-7)) / batch_size
  
  
  def softmax_loss(X, t):
      y = softmax(X)
      return cross_entropy_error(y, t)
  
subject: Add delta(1e-7) for cross_entropy_error