首先需要下载cifar-10数据集
然后将训练集的 6 个batch合并到一个数组中去，程序如下：
# cifar-10数据集的保存格式， 需要pickle解码
import pickle
import numpy as np
def unpickle(file):
with open(file, 'rb') as fo:
dict = pickle.load(fo, encoding='latin-1')
return dict
def train_data():
x = []
y = []
for i in range(1, 6):
batch_file = 'cifar-10-batches-py/data_batch_%d'%(i)
batch_dict = unpickle(batch_file)
train_batch = batch_dict['data']
train_label = batch_dict['labels']
x.append(train_batch)
y.append(train_label)
data = np.concatenate(x)
label = np.concatenate(y)
return data, label
def test_data():
batch_file = 'cifar-10-batches-py/test_batch'
batch_dict = unpickle(batch_file)
data = batch_dict['data']
test_label = batch_dict['labels']
label = np.array(test_label)
return data, label
将此程序命名为cifar_data_loader，放到同目录下
如此这般，便可以通过如下片段使用：
# 导入上述程序
import cifar_data_loader
''' 调用cifar_data_loader的train_data函数，将训练数据存为train_data,将训练标签存为train_label'''
train_data, train_label = cifar_data_loader.train_data()
''' 调用cifar_data_loader的test_data函数，将训练数据存为test_data,将训练标签存为test_label'''
test_data, test_label = cifar_data_loader.test_data()
算法部分如下：
import pickle
import numpy as np
import cifar_data_loader
train_data, train_label = cifar_data_loader.train_data()
test_data, test_label = cifar_data_loader.test_data()
class NearestNeighbor(object):
def __init__(self):
pass
def train(self, X, y):
self.Xtr = X
self.ytr = y
def predict(self, X):
num_test = X.shape[0]
Ypred = np.zeros(num_test, dtype = self.ytr.dtype)
for i in range(num_test):
distances = np.sum(np.abs(self.Xtr - X[i, :]), axis = 1)
min_index = np.argmin(distances)
Ypred[i] = self.ytr[min_index]
return Ypred
nn = NearestNeighbor()
nn.train(train_data, train_label)
Yte_predict = nn.predict(test_data)
accuracy = np.mean(Yte_predict == test_label)
print('accuracy: %f' % accuracy)
花费36分钟，得到结果如下：
> python NearestNeighbor.py
# accuracy: 0.249200