use commands to trace breakpoint

本文介绍如何使用GDB调试工具,在特定条件下设置断点并查看变量状态。通过实例演示了如何在函数`func`中当参数`a`大于10时暂停执行,并观察变量`c`的值。

gdb commands can be used to execute gdb commands after a breakpoint occurred.

Take this as example .

#include <stdio.h>

int func(int a, int b, int c) {
  return a + b - c;
}

int main(int argc, char *argv[]){
  int i = 0;

  for (; i < 100; i++){
    func(i, i++, 1);
  }
  return 0;
}

we want to break func when a > 10 and checkout variable c

(gdb) break func if a > 10
Breakpoint 1 at 0x80483f0: file test-me.c, line 4.
(gdb) commands
Type commands for breakpoint(s) 1, one per line.
End with a line saying just "end".
>print a
>end
(gdb) r
Starting program: /home/alloc/test/a.out

Breakpoint 1, func (a=11, b=10, c=1) at test-me.c:4
4      return a + b - c;
$1 = 11
(gdb) quit



Solver failed when solving following set of constraints rand svt_axi_transaction::burst_size_enum burst_size = svt_axi_transaction::burst_size_enum::BURST_SIZE_256BIT; // rand_mode = OFF constraint slave_burst_size // (from this) (constraint_mode = ON) (/project/PANGU_FM/work/xiaojh/PANGU_FM/trunk/dv/vrf/hnic/env/cust_svt_axi_slave_transaction.sv:33) { (burst_size == svt_axi_transaction::burst_size_enum::BURST_SIZE_64BIT); } ======================================================= Note-[CNST-SATE] Standalone test extracted A standalone test-case for this failure has automatically been extracted from randomize serial 68 partition 9. To reproduce the error using the extracted testcase, please use the following command: cd /project/PANGU_FM/work/xiaojh/PANGU_FM/trunk/dv/vrf/hnic/sim/snps_m0/simv.cst/testcases; vcs -sverilog extracted_r_68_p_9_inconsistent_constraints.sv -R To reproduce the error using the original design and verbose logging, re-run simulation using: simv +ntb_solver_debug=trace +ntb_solver_debug_filter=68 To reproduce the error using the original design and debug the error with Verdi/DVE: 1. re-compile the original design with -debug_access+all, if not already done so % vcs -debug_access+all <other options> 2. re-run the simulation interactively with -gui/-verdi % simv -gui/-verdi <other options> 3. enter the following commands to begin interactive constraint inconsistency debug within Verdi/DVE I. set the breakpoint: verdi/dve> stop -solver -serial 68 II. run the simulation till it stops: verdi/dve> run III. step in the constraint solver: verdi/dve> step -solver
07-15
内容概要:本文介绍了基于贝叶斯优化的CNN-LSTM混合神经网络在时间序列预测中的应用,并提供了完整的Matlab代码实现。该模型结合了卷积神经网络(CNN)在特征提取方面的优势与长短期记忆网络(LSTM)在处理时序依赖问题上的强大能力,形成一种高效的混合预测架构。通过贝叶斯优化算法自动调参,提升了模型的预测精度与泛化能力,适用于风电、光伏、负荷、交通流等多种复杂非线性系统的预测任务。文中还展示了模型训练流程、参数优化机制及实际预测效果分析,突出其在科研与工程应用中的实用性。; 适合人群:具备一定机器学习基基于贝叶斯优化CNN-LSTM混合神经网络预测(Matlab代码实现)础和Matlab编程经验的高校研究生、科研人员及从事预测建模的工程技术人员,尤其适合关注深度学习与智能优化算法结合应用的研究者。; 使用场景及目标:①解决各类时间序列预测问题,如能源出力预测、电力负荷预测、环境数据预测等;②学习如何将CNN-LSTM模型与贝叶斯优化相结合,提升模型性能;③掌握Matlab环境下深度学习模型搭建与超参数自动优化的技术路线。; 阅读建议:建议读者结合提供的Matlab代码进行实践操作,重点关注贝叶斯优化模块与混合神经网络结构的设计逻辑,通过调整数据集和参数加深对模型工作机制的理解,同时可将其框架迁移至其他预测场景中验证效果。
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