NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), 510.47 (or later R510), 515.65 (or later R515), 525.85 (or later R525), or 530.30 (or later R530). The CUDA driver's compatibility compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, R460, and R520 drivers, which are not forward- compatible with CUDA 12.1. For a complete list of supported drivers 0a0+4136153 TensorRT 8.6.1.6 23.05 22.04 NVIDIA CUDA 12.1.1 2.0.0 TensorRT 8.6.1.2 23.04 2.1.0a0+fe05266f TensorRT 8.6.1 23.03 NVIDIA CUDA 12.1.0 2.0.0a0+1767026 23.02 TensorRT 8.5.3 23.01 NVIDIA CUDA

类。有关详细信息，请参阅 HDF5Matrix 文档。 你也可以直接使用 HDF5 数据集： import h5py with h5py.File('input/file.hdf5', 'r') as f: x_data = f['x_data'] model.predict(x_data) 快速开始 36 3.3.19 Keras 配置文件保存在哪里？ 所有 Keras 数据存储的默认目录是： HDF5 文件，例如通过 keras.callbacks.ModelCheckpoint， Keras 使用了 h5py Python 包。h5py 是 Keras 的依赖项，应默认被安装。在基于 Debian 的发行版 本上，你需要再额外安装 libhdf5： sudo apt-get install libhdf5-serial-dev 如果你不确定是否安装了 h5py，则可以打开 Python e(path): while 1: f = open(path) for line in f: # create Numpy arrays of input data # and labels, from each line in the file x, y = process_line(line) yield (x, y) 模型 47 f.close() model.fit_

7: ” Sneaker ” , 8: ”Bag” , 9: ”Ankle␣Boot” , } import matplotlib . pyplot as plt f i g u r e = plt . f i g u r e () # 抽 取 索 引 为 100 的 数 据 来 显 示 img , l a b e l = training_data [ 1 0 0 ] plt . t 迭 代 取 出 的 数 据 量 # s h u f f l e ： 洗 牌 的 意 思， 先 把 数 据 打 乱， 然 后 再 分 为 不 同 的 batch Chapter 1. 准备章节 9 train_dataloader = DataLoader ( training_data , batch_size =64, s h u f f l e= True ) test_dataloader h u f f l e=True ) 我们写点程序检测一下 DataLoader： train_features , train_labels = next ( i t e r ( train_dataloader ) ) print ( f ” Feature ␣batch␣shape : ␣{ train_features . s i z e () }” ) print ( f ” Labels

区域卷积神经网络（R‐CNN）系列 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 599 13.8.1 R‐CNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 600 13.8.2 Fast R‐CNN . . . . . . . . . . . . . . . 601 13.8.3 Faster R‐CNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 603 13.8.4 Mask R‐CNN . . . . . . . . . . . . . . . . . . . . . . . import torchvision from PIL import Image from torch import nn from torch.nn import functional as F from torch.utils import data from torchvision import transforms 目标受众 本书面向学生（本科生或研究生）、工程师和研究人员，他

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problem min ω f (ω) s.t. gi(ω) ≤ 0, i = 1, · · · , k hj(ω) = 0, j = 1, · · · , l with variable ω ∈ Rn, domain D = �k i=1 domgi ∩�l j=1 domhj, optimal value p∗ Objective function f (ω) k inequality (SDU) SVM December 28, 2021 17 / 82 Lagrangian Lagrangian: L : Rn × Rk × Rl → R, with domL = D × Rk × Rl L(ω, α, β ) = f (ω) + k � i=1 αigi(ω) + l� j=1 β jhj(ω) Weighted sum of objective and constraint 18 / 82 Lagrange Dual Function The Lagrange dual function G : Rk × Rl → R G(α, β ) = inf ω∈D L(ω, α, β ) = inf ω∈D � �f (ω) + k � i=1 αigi(ω) + l� j=1 β jhj(ω) � � G is concave, can be −∞

Optimization Problems and Lagrangian Duality We now consider the following optimization problem min ω f(ω) (9) s.t. gi(ω) ≤ 0, i = 1, · · · , k (10) hj(ω) = 0, j = 1, · · · , l (11) where ω ∈ D is the the constraints. The aim of the above optimiza- tion problem is to minimizing the objective function f(ω) subject to the inequal- ity constraints g1(ω), · · · , gk(ω) and the equality constraints h1(ω), · · · , hl(ω). We construct the Lagrangian of the above optimization problem as L(ω, α, β ) = f(ω) + k � i=1 αigi(ω) + l � j=1 β jhj(ω) (12) In fact, L(ω, α, β ) can be treated as a weighted sum

(Contd.) Real valued random variable is a function of the outcome of a ran- domized experiment X : S → R Examples: Discrete random variables (S is discrete) X(s) = True if a randomly drawn person (s) from randomly drawn person (s) from (S) Examples: Continuous random variables (S is continuous) X(s) = r be the heart rate of a randomly drawn person s in our class S Feng Li (SDU) GDA, NB and EM September Variables Real valued random variable is a function of the outcome of a ran- domized experiment X : S → R For continuous random variable X P(a < X < b) = P({s ∈ S : a < X(s) < b}) For discrete random variable

com/course/courseMai n.htm?share=2&shareId=48000000184 7407&courseId=1209092816&_trace_c _p_k2_=9e74eb6f891d47cfaa6f00b5cb 5f617c https://study.163.com/course/courseMain.h tm?share=2&shareId=480000001847407& cour 图 1.22 Anaconda 安装界面-1 图 1.23Anaconda 安装界面-2 安装完成后，怎么验证 Anaconda 是否安装成功呢？通过键盘上的 Windows 键+R 键， 即可调出运行程序对话框，输入“cmd”并回车即打开 Windows 自带的命令行程序 cmd.exe。或者点击开始菜单，输入“cmd”也可搜索到 cmd.exe 程序，打开即可。输入 conda mse(b, w, points) # 计算当前的均方差，用于监控训练进度 if step%50 == 0: # 打印误差和实时的 w,b 值 print(f"iteration:{step}, loss:{loss}, w:{w}, b:{b}") return [b, w] # 返回最后一次的 w,b 主训练函数实现如下： 预览版202112

Light 2 (Digital) Orange Light 1 (Digital) Orange (Digital) Orange (Print) #B92B0F #F05F42 #F2765D #DE3412 Orange (Print) C00, M61, Y72, K00 Pantone 171 C Secondary Colors When #812CE5 R129, G44, B229 C70, M76, Y00, K00 Pantone 2665 C #B932CC R185, G50, B204 C48, M80, Y00, K00 Pantone 2582 C #CC2FAA R204, G47, B170 C40, M90, Y00, K00 Pantone Purple C #CC2F90 R204, G47, B144 C23, M83, Y00, K00 Pantone 2385 C #E12353 R225, G35, B83 C00, M94, Y64, K00 Pantone 192 C Tertiary Colors Our neutral and grayscale tertiary palette is meant for typography