[5]Nadam(http://cs229.stanford.edu/proj2015/054_report.pdf) [6]On the importance of initialization and momentum in deep learning (http://www.cs.toronto.edu/~fritz/absps/momentum.pdf) [7]Keras中文文档(http://keras-cn...
本篇内容将对深度学习下的DP-SGD进行分析总结,隐语在此方向也有相关探索,敬请期待后续开源进展。 1.深度学习下的差分隐私 1.1 深度学习中的差分隐私的定义 在深度学习中,差分隐私的定义如下: (Definition 1. Differential Privacy in Deep Training System) 记数据集为 ,由所有 的子集组成的训练数据库记为 ,参数空...
SGD CONVERGES TO GLOBAL MINIMUM IN DEEP LEARNING VIA STAR-CONVEX PATH,程序员大本营,技术文章内容聚合第一站。
for i in range(nb_epochs): np.random.shuffle(data) # 在每个epoch,重新打乱数据 for example in data: # 加了这一层循环 params_grad = evaluate_gradient(loss_function, example, params) params = params - learning_rate * params_grad mini-batch gradient descent 每个mini-batch的训练样本计算一次g...
[3] I. Sutskever, J. Martens, G. Dahl, and G. Hinton, “On the importance of initialization and momentum in deep learning,” in International conference on machine learning, 2013, pp. 1139– 1147. [4] X. Chen, S. Liu, R. Sun, and M. Hong, “On the convergence of a class of...
第一篇就是前文提到的吐槽Adam最狠的 The Marginal Value of Adaptive Gradient Methods in Machine Learning 。文中说到,同样的一个优化问题,不同的优化算法可能会找到不同的答案,但自适应学习率的算法往往找到非常差的答案。他们通过一...
[6]On the importance of initialization and momentum in deep learning [7]Keras中文文档 [8]Alec Radford(图) [9]An overview of gradient descent optimization algorithms [10]Gradient Descent Only Converges to Minimizers [11]Deep Learning:Nature...
[6]On the importance of initialization and momentum in deep learning [7]Keras中文文档 [8]Alec Radford(图) [9]An overview of gradient descent optimization algorithms [10]Gradient Descent Only Converges to Minimizers [11]Deep Learning:Nature...
Hinton, “On the importance of initialization and momentum in deep learning,” in International conference on machine learning, 2013, pp. 1139– 1147. [4] X. Chen, S. Liu, R. Sun, and M. Hong, “On the convergence of a class of adam-type algorithms for non-convex optimization,” ...
DeepLearning代码解析--随机梯度下降SGD DeepLearning代码解析--随机梯度下降SGD 1、梯度下降（gradient decent） 梯度下降⽅法是我们求最优化的常⽤⽅法。常⽤的有批量梯度下降和随机梯度下降。 对于⼀个⽬标函数；我们⽬的min（J(Θ)），α是learningrate，表⽰每次向梯度负⽅向下降的步长，...