text: # coding: utf-8
  import numpy as np
  
  
  def smooth_curve(x):
      """用于使损失函数的图形变圆滑
  
      参考：http://glowingpython.blogspot.jp/2012/02/convolution-with-numpy.html
      """
      window_len = 11
      s = np.r_[x[window_len-1:0:-1], x, x[-1:-window_len:-1]]
      w = np.kaiser(window_len, 2)
      y = np.convolve(w/w.sum(), s, mode='valid')
      return y[5:len(y)-5]
  
  
  def shuffle_dataset(x, t):
      """打乱数据集
  
      Parameters
      ----------
      x : 训练数据
      t : 监督数据
  
      Returns
      -------
      x, t : 打乱的训练数据和监督数据
      """
      permutation = np.random.permutation(x.shape[0])
      x = x[permutation,:] if x.ndim == 2 else x[permutation,:,:,:]
      t = t[permutation]
  
      return x, t
  
  def conv_output_size(input_size, filter_size, stride=1, pad=0):
      return (input_size + 2*pad - filter_size) / stride + 1
  
  
  def im2col(input_data, filter_h, filter_w, stride=1, pad=0):
      """
  
      Parameters
      ----------
      input_data : 由(数据量, 通道, 高, 长)的4维数组构成的输入数据
      filter_h : 滤波器的高
      filter_w : 滤波器的长
      stride : 步幅
      pad : 填充
  
      Returns
      -------
      col : 2维数组
      """
      N, C, H, W = input_data.shape
      out_h = (H + 2*pad - filter_h)//stride + 1
      out_w = (W + 2*pad - filter_w)//stride + 1
  
      img = np.pad(input_data, [(0,0), (0,0), (pad, pad), (pad, pad)], 'constant')
      col = np.zeros((N, C, filter_h, filter_w, out_h, out_w))
  
      for y in range(filter_h):
          y_max = y + stride*out_h
          for x in range(filter_w):
              x_max = x + stride*out_w
              col[:, :, y, x, :, :] = img[:, :, y:y_max:stride, x:x_max:stride]
  
      col = col.transpose(0, 4, 5, 1, 2, 3).reshape(N*out_h*out_w, -1)
      return col
  
  
  def col2im(col, input_shape, filter_h, filter_w, stride=1, pad=0):
      """
  
      Parameters
      ----------
      col :
      input_shape : 输入数据的形状（例：(10, 1, 28, 28)）
      filter_h :
      filter_w
      stride
      pad
  
      Returns
      -------
  
      """
      N, C, H, W = input_shape
      out_h = (H + 2*pad - filter_h)//stride + 1
      out_w = (W + 2*pad - filter_w)//stride + 1
      col = col.reshape(N, out_h, out_w, C, filter_h, filter_w).transpose(0, 3, 4, 5, 1, 2)
  
      img = np.zeros((N, C, H + 2*pad + stride - 1, W + 2*pad + stride - 1))
      for y in range(filter_h):
          y_max = y + stride*out_h
          for x in range(filter_w):
              x_max = x + stride*out_w
              img[:, :, y:y_max:stride, x:x_max:stride] += col[:, :, y, x, :, :]
  
      return img[:, :, pad:H + pad, pad:W + pad]