DNNBrain是一个模块化框架,由IO、Base、Model、Algorithm四个模块组成,如下图所示。IO模块提供了管理与文件相关的输入和输出操作的工具。Base模块定义用于数组计算和数据转换的基础类。Model模块包含各种DNN模型。Algorithm模块定义了探索神经网络和大脑的各种算法。所有模块都提供了用户友好的API。针对各种研究场景开发了一...
3.1.3 The Melaflow Search Algorithm 宽松条件的计算图替代产生了与原计算图等效的计算图但具有不同运行时性能的计算图搜索空间。实际上,用计算图替代来优化DNN计算图是一个很简单的任务,难度在于所有可能的图替代序列的巨大搜索空间。在搜索空间中寻找最优计算图具有挑战性,因为搜索空间可以是无限的,这取决于使用哪...
[4] Y. Choi and M. Rhu, ‘PREMA: A Predictive Multi-task Scheduling Algorithm For Preemptible Neural Processing Units’. arXiv, Sep. 06, 2019. Accessed: Jul. 19, 2022. [Online]. Available: http://arxiv.org/abs/1909.04548 [5] E. Baek, D. Kwon, and J. Kim, ‘A Multi-Neural N...
current_x = 0.5 # the algorithm starts at x=0.5 learning_rate = 0.01 # step size multiplier num_iterations = 60 # the number of times to train the function #the derivative of the error function (x**4 = the power of 4 or x^4) def slope_at_given_x_val...
[1] Hinton G.E., Osindero S. and Teh Y. (2006) A Fast Learning Algorithm for Deep Belief Nets. Neural Computation, 2006. [2] Hinton, G. E. and Salakhutdinov, R. R. (2006) Reducing the Dimensionality of Data with Neural Networks. Science, Vol. 313, No. 5786, pp. 504-507, 28...
oneAPI Deep Neural Network Library (oneDNN) is an open-source cross-platform performance library of basic building blocks for deep learning applications. oneDNN project is part of the UXL Foundation and is an implementation of the oneAPI specification for oneDNN component....
首先,需要明确代价损失函数(loss function)与优化算法(optimization algorithm)的概念。 代价损失函数(loss function): 代价损失函数是指用于计算标签值y和预测值y ^ \hat{y} y^之间差异的函数。 例,下图为二元交叉熵损失函数(binary cross entropy loss); 目前,有很多种代价损失函数,具体使用哪种,需要结合具体...
The existing joint demodulation decoding iterative synchronization algorithm applies channel coding gain to the entire signal reception process, which can effectively reduce the synchronization threshold of the receiver, but the computational complexity is high. This paper makes use of t...
[31]:A. Y. Ng、M. I. Jordan、Y. Weiss等人的“On spectral clustering: Analysis and an algorithm”,发表于2002年的NeurIPS会议,卷2,页码为849 - 856。主要围绕光谱聚类的分析和算法相关内容展开研究。 [32]:K. Pearson的“Liii. on lines and planes of closest fit to systems of points in space...
[31]:A. Y. Ng、M. I. Jordan、Y. Weiss等人的“On spectral clustering: Analysis and an algorithm”,发表于2002年的NeurIPS会议,卷2,页码为849 - 856。主要围绕光谱聚类的分析和算法相关内容展开研究。 [32]:K. Pearson的“Liii. on lines and planes of closest fit to systems of points in space...