CS231n全连接神经网络作业答案参考

# -*- coding: utf-8 -*- """ Created on Sun Nov 4 14:48:40 2018 @author: 12890 """ import numpy as np import layer from matplotlib import pylab as plt #定义神经网络 class NN(object): def __init__(self, num_layers, layer_size, data, labels, learning_rate, p, batch_size, iterations, k, beta1, beta2): """ 初始化神经网络 num_layers ：网络的层数 layer_size ：网络每一层的大小 data ：网络需要处理的数据，即网络的输入 labels : 网络输入数据的标签 learning_rate ：学习率 p ：dropout的概率 batch_size ：训练过程中btach的大小 iterations : 训练的迭代次数 k : 学习率的降低参数 beta1 : Adam法中动量项的系数 beta2 ：Adam法中梯度项的系数 """ self.num_layers = num_layers self.layer_size = layer_size self.data = data self.labels = labels self.num_layers = num_layers self.learning_rate = learning_rate self.p = p self.batch_size = batch_size self.iterations = iterations self.k = k self.beta1 = beta1 self.beta2 = beta2 self.layers = [] self.socre = [] self.loss = 0 self.gradient = [] """构建神经网络的每一层""" def add_layer(self): for i in range(self.num_layers): if i == 0: layer_temp=layer.LAYER(self.layer_size[i][0], self.layer_size[i][1], self.data) self.layers.append(layer_temp) else: self.layers[i-1].forward(False, self.p) input_data=self.layers[i-1].output layer_temp=layer.LAYER(self.layer_size[i][0], self.layer_size[i][1], input_data) self.layers.append(layer_temp) """构建神经网络的softmax层""" def add_softmax_layer(self): self.socre=self.layers[-1].output self.socre=np.exp(self.socre) self.socre=self.socre/np.tile(np.sum(self.socre,axis=1), (self.layers[-1].output_dim,1)).T """构建用于训练的batch""" def sample_training_data(self, data, labels): batch_index= np.random.randint(0, data.shape[0], self.batch_size) batch=data[batch_index] batch_labels=labels[batch_index] return batch, batch_labels """前向传播过程""" def forward(self, training_mode): for i in range(self.num_layers): if i == 0: self.layers[i].input = self.data.copy() else : self.layers[i].input = self.layers[i-1].output.copy() #以上一层的输出作为这一层的输入 if i == self.num_layers-1: self.layers[i].forward(False , self.p) #最后一层不进行dropout操作 else: self.layers[i].forward(training_mode , self.p) self.add_softmax_layer() """计算损失""" def compute_loss(self): loss=np.zeros(self.data.shape[0]) for i in range(self.data.shape[0]): loss[i] = -np.log(self.socre[i, self.labels[i]]) self.loss = np.mean(loss) """反向传播过程""" def bp(self): for i in range(self.num_layers, 0, -1): if i == self.num_layers: dsdo = self.socre.copy() for j in range(self.data.shape[0]): dsdo[j, self.labels[j]]-=1 else: dsdo = np.dot(self.layers[i].dsdwh, self.layers[i].w.T) if i != self.num_layers: dsdo *= self.layers[i-1].u1 dodwh = np.dot(self.layers[i-1].input, self.layers[i-1].w) dodwh[np.where(dodwh<=0)] = 0 dodwh[np.where(dodwh>0) ] = 1 self.layers[i-1].dsdwh = dsdo * dodwh self.layers[i-1].db = np.mean(self.layers[i-1].dsdwh, axis = 0) self.layers[i-1].dw = np.dot(self.layers[i-1].input.T, self.layers[i-1].dsdwh)/self.data.shape[0] """使用Adam进行网络权重更新""" def Adam(self, t): for i in range(self.num_layers): #更新w self.layers[i].dw_m = self.beta1*self.layers[i].dw_m +(1-self.beta1)*self.layers[i].dw mt = self.layers[i].dw_m/(1-self.beta1**(t+1)) self.layers[i].dw_v = self.beta2*self.layers[i].dw_v +(1-self.beta2)*(self.layers[i].dw**2) vt = self.layers[i].dw_v / (1-self.beta2**(t+1)) alpha = self.learning_rate*np.exp(-(i+1)*self.k) self.layers[i].w += - alpha * mt/(np.sqrt(vt)+1e-8) #更新b self.layers[i].db_m = self.beta1*self.layers[i].db_m +(1-self.beta1)*self.layers[i].db mt = self.layers[i].db_m/(1-self.beta1**(t+1)) self.layers[i].db_v = self.beta2*self.layers[i].db_v +(1-self.beta2)*(self.layers[i].db**2) vt = self.layers[i].db_v / (1-self.beta2**(t+1)) self.layers[i].b += - alpha * mt/(np.sqrt(vt)+1e-8) """训练过程""" def training(self, training_data, training_labels): total_loss=[] for i in range(self.iterations): batch, batch_labels = self.sample_training_data(training_data, training_labels) self.data = batch self.labels = batch_labels self.forward(True) self.compute_loss() self.bp() self.Adam(i) if np.mod(i,10)==0: total_loss.append(self.loss) if np.mod(i, 1000)==0: print('Steps ',i,' finished. Loss is ', self.loss,' \n') plt.figure(0) plt.plot(range(0, self.iterations, 10), total_loss) plt.xlabel('iteration') plt.ylabel('loss') plt.savefig('Loss') """测试过程""" def testing(self, testing_data, testing_labels): self.data= testing_data self.labels=testing_labels self.forward(False) result=np.argmax(self.socre,axis=1) correct=np.where(result==testing_labels) correct_num=np.size(correct[0]) accuracy=correct_num/testing_data.shape[0] return accuracy