Posts

25秋深度学习训练营-第2周：卷积神经网络

第2周：卷积神经网络学习内容：https://oucai.club/classes/dl/week01#第2周-卷积神经网络学习视频：https://www.jianguoyun.com/p/Dde3HS8QrKKIBhi2xpEGIAA 视频学习至1小时06分，包括： CNN的基本结构：卷积、池化、全连接代码练习实验1: 使用 LeNet 对 MNIST 数据集分类代码见https://oucai.club/classes/dl/week02 Epoch [1/10], Step [100/938], Loss: 0.9421, Accuracy: 70.53% Epoch [1/10], Step [200/938], Loss: 0.2978, Accuracy: 80.96% Epoch [1/10], Step [300/938], Loss: 0.1998, Accuracy: 85.28% Epoch [1/10], Step [400/938], Loss: 0.1483, Accuracy: 87.91% Epoch [1/10], Step [500/938], Loss: 0.1318, Accuracy: 89.54% Epoch [1/10], Step [600/938], Loss: 0.1123, Accuracy: 90.72% Epoch [1/10], Step [700/938], Loss: 0.0962, Accuracy: 91.64% Epoch [1/10], Step [800/938], Loss: 0.1050, Accuracy: 92.31% Epoch [1/10], Step [900/938], Loss: 0.0876, Accuracy: 92.84% Test Loss: 0.0854, Test Accuracy: 97.36% Epoch [2/10], Step [100/938], Loss: 0.0724, Accuracy: 97.58% Epoch [2/10], Step [200/938], Loss: 0.0759, Accuracy: 97.56% Epoch [2/10], Step [300/938], Loss: 0.0701, Accuracy: 97.63% Epoch [2/10], Step [400/938], Loss: 0.0674, Accuracy: 97.72% Epoch [2/10], Step [500/938], Loss: 0.0710, Accuracy: 97.73% Epoch [2/10], Step [600/938], Loss: 0.0669, Accuracy: 97.77% Epoch [2/10], Step [700/938], Loss: 0.0625, Accuracy: 97.82% Epoch [2/10], Step [800/938], Loss: 0.0561, Accuracy: 97.88% Epoch [2/10], Step [900/938], Loss: 0.0608, Accuracy: 97.91% Test Loss: 0.0484, Test Accuracy: 98.39% Epoch [3/10], Step [100/938], Loss: 0.0442, Accuracy: 98.55% Epoch [3/10], Step [200/938], Loss: 0.0591, Accuracy: 98.39% Epoch [3/10], Step [300/938], Loss: 0.0406, Accuracy: 98.51% Epoch [3/10], Step [400/938], Loss: 0.0455, Accuracy: 98.55% Epoch [3/10], Step [500/938], Loss: 0.0498, Accuracy: 98.51% Epoch [3/10], Step [600/938], Loss: 0.0424, Accuracy: 98.51% Epoch [3/10], Step [700/938], Loss: 0.0452, Accuracy: 98.52% Epoch [3/10], Step [800/938], Loss: 0.0542, Accuracy: 98.49% Epoch [3/10], Step [900/938], Loss: 0.0410, Accuracy: 98.50% Test Loss: 0.0415, Test Accuracy: 98.62% Epoch [4/10], Step [100/938], Loss: 0.0353, Accuracy: 98.89% Epoch [4/10], Step [200/938], Loss: 0.0337, Accuracy: 98.92% Epoch [4/10], Step [300/938], Loss: 0.0398, Accuracy: 98.90% Epoch [4/10], Step [400/938], Loss: 0.0357, Accuracy: 98.89% Epoch [4/10], Step [500/938], Loss: 0.0416, Accuracy: 98.84% Epoch [4/10], Step [600/938], Loss: 0.0442, Accuracy: 98.80% Epoch [4/10], Step [700/938], Loss: 0.0336, Accuracy: 98.80% Epoch [4/10], Step [800/938], Loss: 0.0319, Accuracy: 98.85% Epoch [4/10], Step [900/938], Loss: 0.0400, Accuracy: 98.84% Test Loss: 0.0440, Test Accuracy: 98.60% Epoch [5/10], Step [100/938], Loss: 0.0287, Accuracy: 99.17% Epoch [5/10], Step [200/938], Loss: 0.0251, Accuracy: 99.23% Epoch [5/10], Step [300/938], Loss: 0.0291, Accuracy: 99.18% Epoch [5/10], Step [400/938], Loss: 0.0275, Accuracy: 99.18% Epoch [5/10], Step [500/938], Loss: 0.0351, Accuracy: 99.09% Epoch [5/10], Step [600/938], Loss: 0.0325, Accuracy: 99.07% Epoch [5/10], Step [700/938], Loss: 0.0320, Accuracy: 99.05% Epoch [5/10], Step [800/938], Loss: 0.0337, Accuracy: 99.03% Epoch [5/10], Step [900/938], Loss: 0.0245, Accuracy: 99.06% Test Loss: 0.0468, Test Accuracy: 98.52% Epoch [6/10], Step [100/938], Loss: 0.0277, Accuracy: 99.05% Epoch [6/10], Step [200/938], Loss: 0.0258, Accuracy: 99.11% Epoch [6/10], Step [300/938], Loss: 0.0264, Accuracy: 99.14% Epoch [6/10], Step [400/938], Loss: 0.0229, Accuracy: 99.18% Epoch [6/10], Step [500/938], Loss: 0.0194, Accuracy: 99.23% Epoch [6/10], Step [600/938], Loss: 0.0277, Accuracy: 99.19% Epoch [6/10], Step [700/938], Loss: 0.0233, Accuracy: 99.20% Epoch [6/10], Step [800/938], Loss: 0.0238, Accuracy: 99.20% Epoch [6/10], Step [900/938], Loss: 0.0288, Accuracy: 99.18% Test Loss: 0.0554, Test Accuracy: 98.28% Epoch [7/10], Step [100/938], Loss: 0.0193, Accuracy: 99.28% Epoch [7/10], Step [200/938], Loss: 0.0249, Accuracy: 99.28% Epoch [7/10], Step [300/938], Loss: 0.0211, Accuracy: 99.33% Epoch [7/10], Step [400/938], Loss: 0.0196, Accuracy: 99.37% Epoch [7/10], Step [500/938], Loss: 0.0257, Accuracy: 99.30% Epoch [7/10], Step [600/938], Loss: 0.0226, Accuracy: 99.30% Epoch [7/10], Step [700/938], Loss: 0.0212, Accuracy: 99.30% Epoch [7/10], Step [800/938], Loss: 0.0267, Accuracy: 99.29% Epoch [7/10], Step [900/938], Loss: 0.0181, Accuracy: 99.30% Test Loss: 0.0441, Test Accuracy: 98.65% Epoch [8/10], Step [100/938], Loss: 0.0145, Accuracy: 99.50% Epoch [8/10], Step [200/938], Loss: 0.0164, Accuracy: 99.48% Epoch [8/10], Step [300/938], Loss: 0.0179, Accuracy: 99.46% Epoch [8/10], Step [400/938], Loss: 0.0161, Accuracy: 99.44% Epoch [8/10], Step [500/938], Loss: 0.0165, Accuracy: 99.44% Epoch [8/10], Step [600/938], Loss: 0.0177, Accuracy: 99.45% Epoch [8/10], Step [700/938], Loss: 0.0211, Accuracy: 99.43% Epoch [8/10], Step [800/938], Loss: 0.0210, Accuracy: 99.41% Epoch [8/10], Step [900/938], Loss: 0.0194, Accuracy: 99.41% Test Loss: 0.0316, Test Accuracy: 99.08% Epoch [9/10], Step [100/938], Loss: 0.0100, Accuracy: 99.69% Epoch [9/10], Step [200/938], Loss: 0.0136, Accuracy: 99.63% Epoch [9/10], Step [300/938], Loss: 0.0176, Accuracy: 99.56% Epoch [9/10], Step [400/938], Loss: 0.0210, Accuracy: 99.50% Epoch [9/10], Step [500/938], Loss: 0.0144, Accuracy: 99.52% Epoch [9/10], Step [600/938], Loss: 0.0167, Accuracy: 99.50% Epoch [9/10], Step [700/938], Loss: 0.0228, Accuracy: 99.46% Epoch [9/10], Step [800/938], Loss: 0.0197, Accuracy: 99.46% Epoch [9/10], Step [900/938], Loss: 0.0136, Accuracy: 99.46% Test Loss: 0.0403, Test Accuracy: 98.86% Epoch [10/10], Step [100/938], Loss: 0.0097, Accuracy: 99.66% Epoch [10/10], Step [200/938], Loss: 0.0141, Accuracy: 99.59% Epoch [10/10], Step [300/938], Loss: 0.0089, Accuracy: 99.63% Epoch [10/10], Step [400/938], Loss: 0.0187, Accuracy: 99.59% Epoch [10/10], Step [500/938], Loss: 0.0182, Accuracy: 99.56% Epoch [10/10], Step [600/938], Loss: 0.0150, Accuracy: 99.55% Epoch [10/10], Step [700/938], Loss: 0.0113, Accuracy: 99.55% Epoch [10/10], Step [800/938], Loss: 0.0200, Accuracy: 99.51% Epoch [10/10], Step [900/938], Loss: 0.0195, Accuracy: 99.50% Test Loss: 0.0391, Test Accuracy: 98.93% Best Test Accuracy: 99.08% 经过多轮训练尝试，我们发现第十次的准确度并非最高，反而是第九次的准确度达到了99%以上 ...

25秋深度学习训练营-第1周：深度学习基础

第1周：深度学习基础学习内容：https://oucai.club/classes/dl/week01#第1周-深度学习基础学习视频：https://www.jianguoyun.com/p/Dde3HS8QrKKIBhi2xpEGIAA 学习要求：深度学习的入门知识 pytorch 基础练习，螺旋数据分类代码练习 1、视频学习学习视频：深度学习基础，主要内容包括：浅层神经⽹络：⽣物神经元到单层感知器，多层感知器，反向传播和梯度消失神经⽹络到深度学习：逐层预训练，⾃编码器和受限玻尔兹曼机 2、代码练习可以在Google Colaboratory开发Deep Learning Applications，它自带免费的Tesla K80 GPU。 1. PyTorch 基础练习什么是 PyTorch ? PyTorch是一个python库，它主要提供了两个高级功能： GPU加速的张量计算构建在反向自动求导系统上的深度神经网络 1. 定义数据 Tensor支持各种各样类型的数据，包括： torch.float32, torch.float64, torch.float16, torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64 2. 定义操作凡是用Tensor进行各种运算的，都是Function 最终，还是需要用Tensor来进行计算的，计算无非是基本运算，加减乘除，求幂求余布尔运算，大于小于，最大最小线性运算，矩阵乘法，求模，求行列式基本运算包括： abs/sqrt/div/exp/fmod/pow ，及一些三角函数 cos/ sin/ asin/ atan2/ cosh，及 ceil/round/floor/trunc 等布尔运算包括： gt/lt/ge/le/eq/ne，topk, sort, max/min 线性计算包括： trace, diag, mm/bmm，t，dot/cross，inverse，svd 等在对m与v进行矩阵乘法时，报错如下原因在于在前文代码中，m的dtype为float，而v的dtype为Long 因此，将代码m @ v改为m @ v.float()，把矩阵v的dtype转化为float进行乘法操作 ...

Quote

A good life is one inspired by love and guided by knowledge. 好的生活，由爱激扬、由知识引导。 Bertrand Russell A rose by any other name would smell as sweet.

计导试验报告6

实验 6 排序算法计算机类3班施家鑫 25020007105 Content 实验 6 排序算法 Content C语言实现生成100000个随机数实现冒泡排序功能实现快速排序功能分别记录两者执行时间，并进行比较完成主程序 Python语言实现生成100000个随机数到数组冒泡排序功能快速排序功能记录执行时间并进行比较主程序关于本文档的撰写 C语言实现随机生成 100000个随机数，进行冒泡排序和快速排序，并比较执行时间。生成100000个随机数引用头文件 #include <stdlib.h> // 使用其中的rand()函数 #include <time.h> // 记录执行时间 // 使用其中的time()函数为随机数做种子，以保证“随机” 使用time()为随机数做种 srand(time(NULL)); 生成100000个随机数 int a[100001] = {0}; for (int i = 0; i < 100000; i++) { a[i] = rand() % 1000000; } 实现冒泡排序功能 void bubble_sort(int a[], int n) { int i, j, temp; for (i = 0; i < n-1; i++) { for (j = 0; j < n-i-1; j++) { if (a[j] > a[j+1]) { temp = a[j]; a[j] = a[j+1]; a[j+1] = temp; } } } } 实现快速排序功能 void quick_sort(int a[], int low, int high) { if (low < high) { int pivot = a[high]; int i = low - 1; for (int j = low; j < high; j++) { if (a[j] < pivot) { i++; int temp = a[i]; a[i] = a[j]; a[j] = temp; } } int temp = a[i + 1]; a[i + 1] = a[high]; a[high] = temp; int pi = i + 1; quick_sort(a, low, pi - 1); quick_sort(a, pi + 1, high); } } 分别记录两者执行时间，并进行比较使用两个变量分别记录开始与结束时间 clock_t start, end; 在排序功能执行的代码前后分别加上 start = clock(); end = clock(); 记录对应的时间 ...