【cs231n学习笔记（2017）】——— 课程作业assignment1及拓展（KNN）

最新推荐文章于 2021-08-29 17:40:23 发布

Nicht_Sehen

最新推荐文章于 2021-08-29 17:40:23 发布

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分类专栏：计算机视觉机器学习

本文链接：https://blog.youkuaiyun.com/Nicht_sehen/article/details/80506655

本文介绍了在cs231n课程中，针对CIFAR-10数据集实现KNN模型的过程。详细讲解了L1和L2模型的构建，包括数据加载、模型训练和预测。通过交叉验证确定L2模型的最佳K值为8，并对比了L1和L2模型的性能。

构建模型

L1模型

代码实现：

import numpy as np


class KNN_L1:
    def __init__(self):
        pass

    def train(self,X, y):
        self.X_train = X
        self.y_train = y

    def predict(self, x):
        num_test = x.shape[0]
        y_pred = np.zeros(num_test, dtype=self.y_train.dtype)
        for i in range(num_test):
            distances = np.sum(np.abs(self.X_train-x[i, :]), axis=1)
            min_index = np.argmin(distances)
            y_pred[i] = self.y_train[min_index]
        return y_pred

将这个模型保存为cs231n_KNN_L1.py文件

L2模型

代码实现：

import numpy as np


class KNN_L2:

    def __init__(self):
        pass

    def train(self,X,y):
        self.X_train = X
        self.y_train = y

    def predict(self,X,k=1,num_loops=0):
        if num_loops==0:
            dists=self.compute_distances_no_loops(X)
        elif num_loops==1:
            dists=self.compute_distances_one_loops(X)
        elif num_loops==2:
            dists=self.compute_distances_one_loops(X)
        return self.predict_labels(dists,k=k)

    #双重循环
    def compute_distances_two_loops(self, X):
        num_test = X.shape[0]
        num_train = self.X_train.shape[0]
        dists = np.zeros((num_test,num_train))
        for i in range(num_test):
            for j in range(num_train):
                dists[i, j] = np.sqrt(np.sum((X[i, :]-self.X_train[j, :])**2))
        return dists

    # 一层循环
    def compute_distances_one_loop(self, X):
        num_test = X.shape[0]
        num_train = self.X_train.shape[0]
        dists = np.zeros((num_test, num_train))
        for i in range(num_test):
            dists[i, :] = np.sqrt(np.sum(np.square(self.X_train - X[i, :]), axis=1))
        return dists

    #无循环
    def compute_distances_no_loops(self, X):
        num_test = X.shape[0]
        num_train = self.X_train.shape[0]
        dists = np.zeros((num_test, num_train))
        test_sum = np.sum(np.square(X), axis=1)
        train_sum = np.sum(np.square(self.X_train), axis=1)
        inner_product = np.dot(X, self.X_train.T)
        dists = np.sqrt(-2 * inner_product + test_sum.reshape(-1, 1) + train_sum)
        return dists

    def predict_labels(self, dists, k=1):
        num_test = dists.shape[0]
        y_pred = np.zeros(num_test)
        for i