CS231n作业笔记2.5：dropout的实现与应用

本文主要是介绍CS231n作业笔记2.5：dropout的实现与应用，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

CS231n简介

详见 CS231n课程笔记1：Introduction。
本文都是作者自己的思考，正确性未经过验证，欢迎指教。

作业笔记

dropout中唯一需要注意的就是为了平衡train与test，通过除以期望值即可。

1. 前向传播

  if mode == 'train':mask = (np.random.rand(*x.shape)<p)out = x*mask / pelif mode == 'test':out = xmask = np.ones_like(x)

2. 后向传播

  if mode == 'train':dx = dout * mask / dropout_param['p']elif mode == 'test':dx = doutreturn dx

3. 应用：带dropout的多层神经网络

在每一层ReLU后接一层dropout即可。关于多层神经网络的实现，请参考CS231n作业笔记2.4：Batchnorm的实现与使用。

    cache = {}hidden_value = Nonehidden_value,cache['fc1'] = affine_forward(X,self.params['W1'],self.params['b1'])if self.use_batchnorm:hidden_value,cache['bn1'] = batchnorm_forward(hidden_value, self.params['gamma1'], self.params['beta1'], self.bn_params[0])hidden_value,cache['relu1'] = relu_forward(hidden_value)if self.use_dropout:hidden_value, cache['drop1'] = dropout_forward(hidden_value,self.dropout_param)for index in range(2,self.num_layers):hidden_value,cache['fc'+str(index)] = affine_forward(hidden_value,self.params['W'+str(index)],self.params['b'+str(index)])if self.use_batchnorm:hidden_value,cache['bn'+str(index)] = batchnorm_forward(hidden_value,  self.params['gamma'+str(index)], self.params['beta'+str(index)], self.bn_params[index-1])hidden_value,cache['relu'+str(index)] = relu_forward(hidden_value)if self.use_dropout:hidden_value, cache['drop'+str(index)] = dropout_forward(hidden_value,self.dropout_param)scores,cache['score'] = affine_forward(hidden_value,self.params['W'+str(self.num_layers)],self.params['b'+str(self.num_layers)])# If test mode return earlyif mode == 'test':return scoresloss, grads = 0.0, {}loss,dscores = softmax_loss(scores,y)for index in range(1,self.num_layers+1):loss += 0.5*self.reg*np.sum(self.params['W'+str(index)]**2)dhidden_value,grads['W'+str(self.num_layers)],grads['b'+str(self.num_layers)] = affine_backward(dscores,cache['score'])for index in range(self.num_layers-1,1,-1):if (self.use_dropout):dhidden_value = dropout_backward(dhidden_value, cache['drop'+str(index)])dhidden_value = relu_backward(dhidden_value,cache['relu'+str(index)])if self.use_batchnorm:dhidden_value, grads['gamma'+str(index)], grads['beta'+str(index)] = batchnorm_backward(dhidden_value, cache['bn'+str(index)])dhidden_value,grads['W'+str(index)],grads['b'+str(index)] = affine_backward(dhidden_value,cache['fc'+str(index)])if (self.use_dropout):dhidden_value = dropout_backward(dhidden_value, cache['drop1'])dhidden_value = relu_backward(dhidden_value,cache['relu1'])if self.use_batchnorm:dhidden_value, grads['gamma1'], grads['beta1'] = batchnorm_backward(dhidden_value, cache['bn1'])dhidden_value,grads['W1'],grads['b1'] = affine_backward(dhidden_value,cache['fc1'])for index in range(1,self.num_layers+1):grads['W'+str(index)] += self.reg * self.params['W'+str(index)]