cs231n链接：http://cs231n.github.io/linear-classify/，
训练集链接：https://download.csdn.net/download/fanzonghao/10592049
KNN缺点：每个测试样本都要循环一遍训练样本。
该数据集由5个data_batch和一个test_batch构成，测试代码
import pickle
import numpy as np
fo=open('./datasets/cifar-10-batches-py/data_batch_1','rb')
dict=pickle.load(fo,encoding='bytes')
print(dict)
print(dict[b'data'].shape)print(dict[b'labels'])
print(len(dict[b'labels']))print(dict[b'filenames'])
print(len(dict[b'filenames']))
fo.close()
可看出，一个data_batch由10000个，32×32×3大小的图片组成，5个就是50000个，test_batch也是10000张，故有50000张训练样本，10000张测试样本。
将5个训练集合成一个代码如下：
import pickle
import numpy as np"""
解压数据集
"""
def unpickle(file):fo=open(file,'rb')dict=pickle.load(fo,encoding='bytes')fo.close()return dict
"""
5个data_batch和1个test_batch合成一个
"""
def load_cifar10(file):data_train = []label_train=[]#融合训练集for i in range(1,6):dic=unpickle(file+'data_batch_'+str(i))for i_data in dic[b'data']:data_train.append(i_data)for i_label in dic[b'labels']:label_train.append(i_label)# print(np.array(data_train).shape)# print(np.array(label_train).shape)# 融合测试集data_test=[]label_test=[]dic = unpickle(file + 'test_batch')for i_data in dic[b'data']:data_test.append(i_data)for i_label in dic[b'labels']:label_test.append(i_label)# print(np.array(data_test).shape)# print(np.array(label_test).shape)return (np.array(data_train),np.array(label_train),np.array(data_test),np.array(label_test))
path='./datasets/cifar-10-batches-py/'
# #(50000,3072) (50000,) (10000,3072) (10000,)
(data_train,label_train,data_test,label_test)=load_cifar10(path)
print(data_train.shape)
print(label_train.shape)
print(label_train[:10])
print(data_test.shape)
print(label_test.shape)
KNN代码：
import numpy as np
import pickle
"""
程序功能：k近邻实现cifar10上的样本分类 精度低 测试时间长
"""
#输入训练集和测试集
#解压数据集
def unpickle(file):fo=open(file,'rb')dict=pickle.load(fo,encoding='bytes')print(dict)fo.close()return dict
#融合训练集和测试集作为输出总样本
def load_cifar10(file):data_train = []label_train=[]#融合训练集for i in range(1,6):dic=unpickle(file+'data_batch_'+str(i))for i_data in dic[b'data']:data_train.append(i_data)for i_label in dic[b'labels']:label_train.append(i_label)# print(np.array(data_train).shape)# print(np.array(label_train).shape)# 融合测试集data_test=[]label_test=[]dic = unpickle(file + 'test_batch')for i_data in dic[b'data']:data_test.append(i_data)for i_label in dic[b'labels']:label_test.append(i_label)# print(np.array(data_test).shape)# print(np.array(label_test).shape)return (np.array(data_train),np.array(label_train),np.array(data_test),np.array(label_test))
path='./datasets/cifar-10-batches-py/'
#(50000,3072) (50000,) (10000,3072) (10000,)
(data_train,label_train,data_test,label_test)=load_cifar10(path)
#print(label_train)
print(data_train.shape,label_train.shape,data_test.shape,label_test.shape)
#print(data_test.shape[0])"""
实现最近邻的预测
"""
class NearestNeighbor:def __init__(self):passdef train(self,X,y):self.Xtr=Xself.ytr=ydef predict(self,X):num_test=X.shape[0]self.X=XY_pred=np.zeros(num_test,dtype=self.ytr.dtype)for i in range(num_test):distances=np.sum(np.abs(self.Xtr-self.X[i,:]),axis=1)#distances=np.sqrt(np.sum(np.square(self.Xtr-self.X[i,:]),axis=1))min_index=np.argmin(distances)Y_pred[i]=self.ytr[min_index]if i%100==0:print('运行到{}步'.format(i))return Y_pred
nn=NearestNeighbor()
nn.train(data_train,label_train)
Y_pred=nn.predict(data_test)
accuarcy=np.mean(label_test==Y_pred)
print('accuarcy={}'.format(accuarcy))打印结果：精度不高，后面引入神经网络
SVM损失函数：
loss.py
import numpy as np
"""
程序功能：利用SVM代价函数实现损失值的积累
"""
def L(X,y,W):#X [3073,50000]#y 一维（50000,）#W [10,3073]delta=1.0scores=np.dot(W,X)#print(y)#对应训练样本的输出y#print(scores[y, np.arange(scores.shape[1])])#(10,50000)#SVM函数margins=np.maximum(0,scores-scores[y, np.arange(scores.shape[1])]+delta)#print('margins.shape={}'.format(margins.shape))margins[y,np.arange(scores.shape[1])]=0loss=np.mean(margins)return lossoptimizer_grand.py
import numpy as np
import pickle
import loss
"""
函数功能：利用随机搜索和局部随机搜索来获取W和b采用SVM损失函数 获取最佳的W和b
"""
#输入训练集和测试集
#解压数据集
def unpickle(file):fo=open(file,'rb')dict=pickle.load(fo,encoding='bytes')fo.close()return dict
#融合训练集和测试集作为输出总样本
def load_cifar10(file):data_train = []label_train=[]#融合训练集for i in range(1,6):dic=unpickle(file+'data_batch_'+str(i))for i_data in dic[b'data']:data_train.append(i_data)for i_label in dic[b'labels']:label_train.append(i_label)# print(np.array(data_train).shape)# print(np.array(label_train).shape)# 融合测试集data_test=[]label_test=[]dic = unpickle(file + 'test_batch')for i_data in dic[b'data']:data_test.append(i_data)for i_label in dic[b'labels']:label_test.append(i_label)# print(np.array(data_test).shape)# print(np.array(label_test).shape)return (np.array(data_train),np.array(label_train),np.array(data_test),np.array(label_test))
path='./datasets/cifar-10-batches-py/'
#(50000,3072) (50000,) (10000,3072) (10000,)
(data_train,label_train,data_test,label_test)=load_cifar10(path)
#print(label_train)
print(data_train.shape,label_train.shape,data_test.shape,label_test.shape)
#(3072,50000)
train_data=np.transpose(data_train)
#增加一行 处理偏置值
bias=np.ones((1,train_data.shape[1]))
#(3073,50000)
train_data=np.vstack((train_data,bias))
print(train_data.shape)
#随机选择最佳的权值 输出最佳的W
def random_search():bestloss=float('inf')for number in range(1000):# 随机搜索 权值随机更新 选出比较好的W = np.random.randn(10, 3073) * 0.0001# 计算损失值lost = loss.L(train_data, label_train, W)if lost<bestloss:bestloss=lostbestW=Wif number%100==0:print('number={},the lost={},bestloss={}'.format(number,lost,bestloss))return bestW
#调用随机产生的最佳权值产生预测值与标签值算精确度
def random_search_accu():bestW=random_search()#(10,50000)scores=np.dot(bestW,train_data)#找出每列分数最大值的索引Y_predict=np.argmax(scores,axis=0)accurarcy=np.mean(Y_predict==label_train)print('accurarcy={}'.format(accurarcy))
def random_local_search():W = np.random.randn(10, 3073) * 0.001bestloss=float('inf')for number in range(1000):# 随机搜索 权值随机更新 选出比较好的step_size=0.0001W_try=W+np.random.randn(10, 3073) * step_size# 计算损失值lost = loss.L(train_data, label_train, W_try)if lost<bestloss:bestloss=lostbestW=W_tryif number%100==0:print('number={},the lost={},bestloss={}'.format(number,lost,bestloss))return bestW
#调用随机产生的最佳权值产生预测值与标签值算精确度
def random_local_search_accu():bestW=random_local_search()#(10,50000)scores=np.dot(bestW,train_data)#找出每列分数最大值的索引Y_predict=np.argmax(scores,axis=0)accurarcy=np.mean(Y_predict==label_train)print('accurarcy={}'.format(accurarcy))
if __name__ == '__main__':#随机搜索# random_search_accu()#局部随机搜索random_local_search_accu()#梯度跟随
随机最佳权重的打印结果：
在迭代过程中，权重还变化的结果
