真味是淡至如常。
KNN图像分类
链接
摘自大佬的笔记，拿来细细品味,别是一番滋味。
代码语言：javascript复制import numpy as np
import os
import pickle
import matplotlib.pyplot as plt
import h5py
import scipy
from PIL import Image
from scipy import ndimage
def distance(X_test, X_train):
"""
输入:
X_test -- 由numpy数组表示的测试集，大小为(图片长度 * 图片高度 * 3 , 测试样本数)
X_train -- 由numpy数组表示的训练集，大小为(图片长度 * 图片高度 * 3 , 训练样本数)
输出:
distances -- 测试数据与各个训练数据之间的距离,大小为(测试样本数, 训练样本数量)的numpy数组
"""
num_test = X_test.shape[1]
num_train = X_train.shape[1]
distances = np.zeros((num_test, num_train))
# (X_test - X_train)*(X_test - X_train) = -2X_test*X_train X_test*X_test X_train*X_train
dist1 = np.multiply(np.dot(X_test.T,X_train), -2) # -2X_test*X_train, shape (num_test, num_train)
dist2 = np.sum(np.square(X_test.T), axis=1, keepdims=True) # X_test*X_test, shape (num_test, 1)
dist3 = np.sum(np.square(X_train), axis=0,keepdims=True) # X_train*X_train, shape(1, num_train)
distances = np.sqrt(dist1 dist2 dist3)
return distances
def predict(X_test, X_train, Y_train, k = 1):
"""
输入:
X_test -- 由numpy数组表示的测试集，大小为(图片长度 * 图片高度 * 3 , 测试样本数)
X_train -- 由numpy数组表示的训练集，大小为(图片长度 * 图片高度 * 3 , 训练样本数)
Y_train -- 由numpy数组（向量）表示的训练标签，大小为 (1, 训练样本数)
k -- 选取与训练集最近邻的数量
输出:
Y_prediction -- 包含X_test中所有预测值的numpy数组（向量）
distances -- 由numpy数组表示的测试数据与各个训练数据之间的距离,大小为(测试样本数, 训练样本数)
"""
distances = distance(X_test, X_train)
num_test = X_test.shape[1]
Y_prediction = np.zeros(num_test)
for i in range(num_test):
dists_min_k = np.argsort(distances[i])[:k] # 按照距离递增次序进行排序,选取距离最小的k个点
y_labels_k = Y_train[0,dists_min_k] # 确定前k个点的所在类别
Y_prediction[i] = np.argmax(np.bincount(y_labels_k)) # 返回前k个点中出现频率最高的类别作为测试数据的预测分类
return Y_prediction, distances
def model(X_test, Y_test, X_train, Y_train, k = 1, print_correct = False):
"""
输入：
X_test -- 由numpy数组表示的测试集，大小为(图片长度 * 图片高度 * 3 , 测试样本数)
X_train -- 由numpy数组表示的训练集，大小为(图片长度 * 图片高度 * 3 , 训练样本数)
Y_train -- 由numpy数组（向量）表示的训练标签，大小为 (1, 训练样本数)
Y_test -- 由numpy数组（向量）表示的测试标签，大小为 (1, 测试样本数)
k -- 选取与训练集最近邻的数量
print_correct -- 设置为true时，打印正确率
输出：
d -- 包含模型信息的字典
"""
Y_prediction, distances = predict(X_test, X_train, Y_train, k)
num_correct = np.sum(Y_prediction == Y_test)
accuracy = np.mean(Y_prediction == Y_test)
if print_correct:
print('Correct %d/%d: The test accuracy: %f' % (num_correct, X_test.shape[1], accuracy))
d = {"k": k,
"Y_prediction": Y_prediction,
"distances" : distances,
"accuracy": accuracy}
return d
def load_CIFAR_batch(filename):
with open(filename, 'rb') as f:
datadict = pickle.load(f,encoding='latin1')
X = datadict['data']
Y = datadict['labels']
X = X.reshape(10000, 3, 32, 32).transpose(0,2,3,1).astype("float")
Y = np.array(Y)
return X, Y
def load_CIFAR10():
xs = []
ys = []
for b in range(1,6):
f = os.path.join('F:C-and-Python-AlgorithnpythonTensorflowdata', 'cifar-10-batches-py', 'data_batch_%d' % (b, ))
X, Y = load_CIFAR_batch(f)
xs.append(X)
ys.append(Y)
Xtr = np.concatenate(xs)
Ytr = np.concatenate(ys)
del X, Y
Xte, Yte = load_CIFAR_batch(os.path.join('F:C-and-Python-AlgorithnpythonTensorflowdata', 'cifar-10-batches-py', 'test_batch'))
return Xtr, Ytr, Xte, Yte
X_train, y_train, X_test, y_test = load_CIFAR10()
classes = ['plane', 'car', 'bird', 'cat', 'dear', 'dog', 'frog', 'horse', 'ship', 'truck']
num_classes = len(classes)
num_each_class = 7
for y, cls in enumerate(classes):
idxs = np.flatnonzero(y_train == y)
idxs = np.random.choice(idxs, num_each_class, replace=False)
for i, idx in enumerate(idxs):
plt_idx = i * num_classes (y 1)
plt.subplot(num_each_class, num_classes, plt_idx)
plt.imshow(X_train[idx].astype('uint8'))
plt.axis('off')
if i == 0:
plt.title(cls)
plt.show()
X_train = np.reshape(X_train, (X_train.shape[0], -1)).T
X_test = np.reshape(X_test, (X_test.shape[0], -1)).T
Y_set_train = y_train[:10000].reshape(1,-1)
Y_set_test = y_test[:1000].reshape(1,-1)
X_set_train = X_train[:,:10000]
X_set_test = X_test[:,:1000]
models = {}
for k in [1, 3, 5, 10]:
print ("k = " str(k))
models[str(k)] = model(X_set_test, Y_set_test, X_set_train, Y_set_train, k, print_correct = True)
print ('n' "-------------------------------------------------------" 'n')
models = {}
k = []
accuracys = []
for i in range(1,11):
models[str(i)] = model(X_set_test, Y_set_test, X_set_train, Y_set_train, i, print_correct = False)
k.append(models[str(i)]["k"])
accuracys.append(models[str(i)]["accuracy"])
plt.plot(k, accuracys)
plt.ylabel('accuracy')
plt.xlabel('k')
plt.show()
