text: # coding: utf-8
  import numpy as np
  from demo_code.functions import *
  from demo_code.util import im2col, col2im
  
  class Relu:
      def __init__(self):
          self.mask = None
  
      def forward(self, x):
          self.mask = (x <= 0)
          out = x.copy()
          out[self.mask] = 0
  
          return out
  
      def backward(self, dout):
          dout[self.mask] = 0
          dx = dout
  
          return dx
  
  class Sigmoid:
      def __init__(self):
          self.out = None
  
      def forward(self, x):
          out = sigmoid(x)
          self.out = out
          return out
  
      def backward(self, dout):
          dx = dout * (1.0 - self.out) * self.out
  
          return dx
  
  class Affine:
      def __init__(self, W, b):
          self.W =W
          self.b = b
          
          self.x = None
          self.original_x_shape = None
          # 权重和偏置参数的导数
          self.dW = None
          self.db = None
  
      def forward(self, x):
          # 对应张量
          self.original_x_shape = x.shape
          x = x.reshape(x.shape[0], -1)
          self.x = x
  
          out = np.dot(self.x, self.W) + self.b
  
          return out
  
      def backward(self, dout):
          dx = np.dot(dout, self.W.T)
          self.dW = np.dot(self.x.T, dout)
          self.db = np.sum(dout, axis=0)
          
          dx = dx.reshape(*self.original_x_shape)  # 还原输入数据的形状（对应张量）
          return dx
  
  class SoftmaxWithLoss:
      def __init__(self):
          self.loss = None
          self.y = None # softmax的输出
          self.t = None # 监督数据
  
      def forward(self, x, t):
          self.t = t
          self.y = softmax(x)
          self.loss = cross_entropy_error(self.y, self.t)
          
          return self.loss
  
      def backward(self, dout=1):
          batch_size = self.t.shape[0]
          if self.t.size == self.y.size: # 监督数据是one-hot-vector的情况
              dx = (self.y - self.t) / batch_size
          else:
              dx = self.y.copy()
              dx[np.arange(batch_size), self.t] -= 1
              dx = dx / batch_size
          
          return dx
  
  class Dropout:
      """
      http://arxiv.org/abs/1207.0580
      """
      def __init__(self, dropout_ratio=0.5):
          self.dropout_ratio = dropout_ratio
          self.mask = None
  
      def forward(self, x, train_flg=True):
          if train_flg:
              self.mask = np.random.rand(*x.shape) > self.dropout_ratio
              return x * self.mask
          else:
              return x * (1.0 - self.dropout_ratio)
  
      def backward(self, dout):
          return dout * self.mask
  
  class BatchNormalization:
      """
      http://arxiv.org/abs/1502.03167
      """
      def __init__(self, gamma, beta, momentum=0.9, running_mean=None, running_var=None):
          self.gamma = gamma
          self.beta = beta
          self.momentum = momentum
          self.input_shape = None # Conv层的情况下为4维，全连接层的情况下为2维  
  
          # 测试时使用的平均值和方差
          self.running_mean = running_mean
          self.running_var = running_var  
          
          # backward时使用的中间数据
          self.batch_size = None
          self.xc = None
          self.std = None
          self.dgamma = None
          self.dbeta = None
  
      def forward(self, x, train_flg=True):
          self.input_shape = x.shape
          if x.ndim != 2:
              N, C, H, W = x.shape
              x = x.reshape(N, -1)
  
          out = self.__forward(x, train_flg)
          
          return out.reshape(*self.input_shape)
              
      def __forward(self, x, train_flg):
          if self.running_mean is None:
              N, D = x.shape
              self.running_mean = np.zeros(D)
              self.running_var = np.zeros(D)
                          
          if train_flg:
              mu = x.mean(axis=0)
              xc = x - mu
              var = np.mean(xc**2, axis=0)
              std = np.sqrt(var + 10e-7)
              xn = xc / std
              
              self.batch_size = x.shape[0]
              self.xc = xc
              self.xn = xn
              self.std = std
              self.running_mean = self.momentum * self.running_mean + (1-self.momentum) * mu
              self.running_var = self.momentum * self.running_var + (1-self.momentum) * var            
          else:
              xc = x - self.running_mean
              xn = xc / ((np.sqrt(self.running_var + 10e-7)))
              
          out = self.gamma * xn + self.beta 
          return out
  
      def backward(self, dout):
          if dout.ndim != 2:
              N, C, H, W = dout.shape
              dout = dout.reshape(N, -1)
  
          dx = self.__backward(dout)
  
          dx = dx.reshape(*self.input_shape)
          return dx
  
      def __backward(self, dout):
          dbeta = dout.sum(axis=0)
          dgamma = np.sum(self.xn * dout, axis=0)
          dxn = self.gamma * dout
          dxc = dxn / self.std
          dstd = -np.sum((dxn * self.xc) / (self.std * self.std), axis=0)
          dvar = 0.5 * dstd / self.std
          dxc += (2.0 / self.batch_size) * self.xc * dvar
          dmu = np.sum(dxc, axis=0)
          dx = dxc - dmu / self.batch_size
          
          self.dgamma = dgamma
          self.dbeta = dbeta
          
          return dx
  
  class Convolution:
      def __init__(self, W, b, stride=1, pad=0):
          self.W = W
          self.b = b
          self.stride = stride
          self.pad = pad
          
          # 中间数据（backward时使用）
          self.x = None   
          self.col = None
          self.col_W = None
          
          # 权重和偏置参数的梯度
          self.dW = None
          self.db = None
  
      def forward(self, x):
          FN, C, FH, FW = self.W.shape
          N, C, H, W = x.shape
          out_h = (H + 2*self.pad - FH) // self.stride + 1
          out_w = (W + 2*self.pad - FW) // self.stride + 1
  
          col = im2col(x, FH, FW, self.stride, self.pad)
          col_W = self.W.reshape(FN, -1).T
  
          out = np.dot(col, col_W) + self.b
          out = out.reshape(N, out_h, out_w, -1).transpose(0, 3, 1, 2)
  
          self.x = x
          self.col = col
          self.col_W = col_W
  
          return out
  
      def backward(self, dout):
          FN, C, FH, FW = self.W.shape
          dout = dout.transpose(0,2,3,1).reshape(-1, FN)
  
          self.db = np.sum(dout, axis=0)
          self.dW = np.dot(self.col.T, dout)
          self.dW = self.dW.transpose(1, 0).reshape(FN, C, FH, FW)
  
          dcol = np.dot(dout, self.col_W.T)
          dx = col2im(dcol, self.x.shape, FH, FW, self.stride, self.pad)
  
          return dx
  
  class Pooling:
      def __init__(self, pool_h, pool_w, stride=1, pad=0):
          self.pool_h = pool_h
          self.pool_w = pool_w
          self.stride = stride
          self.pad = pad
          
          self.x = None
          self.arg_max = None
  
      def forward(self, x):
          N, C, H, W = x.shape
          out_h = (H - self.pool_h) // self.stride + 1
          out_w = (W - self.pool_w) // self.stride + 1
  
          col = im2col(x, self.pool_h, self.pool_w, self.stride, self.pad)
          col = col.reshape(-1, self.pool_h*self.pool_w)
  
          arg_max = np.argmax(col, axis=1)
          out = np.max(col, axis=1)
          out = out.reshape(N, out_h, out_w, C).transpose(0, 3, 1, 2)
  
          self.x = x
          self.arg_max = arg_max
  
          return out
  
      def backward(self, dout):
          dout = dout.transpose(0, 2, 3, 1)
          
          pool_size = self.pool_h * self.pool_w
          dmax = np.zeros((dout.size, pool_size))
          dmax[np.arange(self.arg_max.size), self.arg_max.flatten()] = dout.flatten()
          dmax = dmax.reshape(dout.shape + (pool_size,)) 
          
          dcol = dmax.reshape(dmax.shape[0] * dmax.shape[1] * dmax.shape[2], -1)
          dx = col2im(dcol, self.x.shape, self.pool_h, self.pool_w, self.stride, self.pad)
          
          return dx
  