基于Adam算法的猫狗模式识别系统的设计与实现代码大全.doc基于Adam算法的猫狗模式识别系统的设计与实现代码大全.doc基于Adam算法的猫狗模式识别系统的设计与实现代码大全.doc

自己微调版本，含注释和数据集，一个程序就能运行，是吴恩达深度学习课程的课后作业

包含bin文件，bat文件，cmd文件

MATLAB建模仿真 Adams建模

解决误覆盖该.mdl的问题

深度学习ADAM算法，分享给大家学习。 We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.