用KNN算法分类CIFAR-10图片数据

　　KNN分类CIFAR-10，并且做Cross Validation，CIDAR-10数据库数据如下：

knn.py : 主要的试验流程

from cs231n.data_utils import     load_CIFAR10
from cs231n.classifiers import KNearestNeighbor
import random
import numpy as np
import     matplotlib.pyplot as plt
# set plt params
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'

cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'
x_train,y_train,x_test,y_test = load_CIFAR10(cifar10_dir)
print'x_train : ',x_train.shape
print'y_train : ',y_train.shape
print'x_test : ',x_test.shape,'y_test : ',y_test.shape

#visual training example
classes = ['plane','car','bird','cat','deer','dog','forg','horse','ship','truck']
num_classes = len(classes)
samples_per_class = 7
for y,cls in enumerate(classes):
    #flaznonzero return indices_array of the none-zero elements
    # ten classes, y_train and y_test all in [1...10]
    idxs = np.flatnonzero(y_train == y)
    idxs = np.random.choice(idxs , samples_per_class, replace = False)
    for i,idx in enumerate(idxs):
        plt_idx = i*num_classes + y + 1
        # subplot(m,n,p)
        # m : length of subplot 
        # n : width of subplot
        # p : location of subplot
        plt.subplot(samples_per_class,num_classes,plt_idx)
        plt.imshow(x_train[idx].astype('uint8'))
        # hidden the axis info
        plt.axis('off')
        if i == 0:
            plt.title(cls)
plt.show()

# subsample data for more dfficient code execution 
num_training = 5000
#range(5)=[0,1,2,3,4]
mask = range(num_training)
x_train = x_train[mask]
y_train = y_train[mask]
num_test = 500
mask = range(num_test)
x_test = x_test[mask]
y_test = y_test[mask]
#the image data has three chanels
#the next two step shape the image size 32*32*3 to 3072*1
x_train = np.reshape(x_train,(x_train.shape[0],-1))
x_test = np.reshape(x_test,(x_test.shape[0],-1))
print 'after subsample and re shape:'
print 'x_train : ',x_train.shape," x_test : ",x_test.shape
#KNN classifier
classifier = KNearestNeighbor()
classifier.train(x_train,y_train)
# compute the distance between test_data and train_data 
dists = classifier.compute_distances_no_loops(x_test)
#each row is a single test example and its distances to training example
print 'dist shape : ',dists.shape
plt.imshow(dists , interpolation='none')
plt.show()
y_test_pred = classifier.predict_labels(dists,k = 5)
num_correct = np.sum(y_test_pred == y_test)
acc = float(num_correct)/num_test
print'k=5 ,The Accurancy is : ', acc

#Cross-Validation

#5-fold cross validation split the training data to 5 pieces
num_folds = 5
#k is params of knn
k_choice = [1,5,8,11,15,18,20,50,100]
x_train_folds = []
y_train_folds = []
x_train_folds = np.array_split(x_train,num_folds)
y_train_folds = np.array_split(y_train,num_folds)

k_to_acc={}

for k in k_choice:
    k_to_acc[k] =[]
for k in k_choice:
    print 'cross validation : k = ', k
    for j in range(num_folds):
        #vstack :stack the array to matrix
        #vertical
        x_train_cv = np.vstack(x_train_folds[0:j]+x_train_folds[j+1:])
        x_test_cv = x_train_folds[j]
        
        #>>> a = np.array((1,2,3))
        #>>> b = np.array((2,3,4))
        #>>> np.hstack((a,b))
        # horizontally    
        y_train_cv = np.hstack(y_train_folds[0:j]+y_train_folds[j+1:])
        y_test_cv = y_train_folds[j]
        
        classifier.train(x_train_cv,y_train_cv)
        dists_cv = classifier.compute_distances_no_loops(x_test_cv)
        y_test_pred = classifier.predict_labels(dists_cv,k)
        num_correct = np.sum(y_test_pred == y_test_cv)
        acc = float(num_correct)/ num_test
        k_to_acc[k].append(acc)
print k_to_acc

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