One of the most important things that affect ANN's performance is the selection of hyper-parameters, but there is not a specific rule to determine the hyper-parameters of the algorithm. Although there is no single well-known method in hyper-parameter tuning, this issue has been discussed in ...
然而,一个神经网络的连接方式、网络的层数、每层的节点数这些参数,则不是学习出来的,而是人为事先设置的。对于这些人为设置的参数,我们称之为超参数(Hyper-Parameters)。 接下来,我们将要介绍神经网络的训练算法:反向传播算法。 反向传播算法(Back Propagation) 我们以监督学习为例来解释反向传播算法。 我们可以首先随...
然而,一个神经网络的连接方式、网络的层数、每层的节点数这些参数,则不是学习出来的,而是人为事先设置的。对于这些人为设置的参数,我们称之为超参数(Hyper-Parameters)。 接下来,我们将要介绍神经网络的训练算法:反向传播算法。 反向传播算法(Back Propagation) 我们以监督学习为例来解释反向传播算法。 我们可以首先随...
对于这些人为设置的参数,我们称之为超参数(Hyper-Parameters)。 接下来,我们将要介绍神经网络的训练算法:反向传播算法。 反向传播算法(Back Propagation) 我们以监督学习为例来解释反向传播算法。 我们可以首先随机初始化各个权重值,有了输入后,就可以分别计算隐藏层的输出ai,以及输出层的输出yi。 然后,我们按照下面的...
typically sigmoidal functions. Feed-forward ANNs are one of the simplest network architectures with no feedback mechanisms. The neurons are organized in layers. The number of neurons and layers are hyper-parameters of the algorithms. Too complex networks may lead to over-fitting phenomena. A prelim...
Cay Y (2013) Prediction of a gasoline engine performance with artificial neural network. Fuel 111:324–331. https://doi.org/10.1016/j.fuel.2012.12.040 Article Google Scholar Şahin F (2015) Effects of engine parameters on ionization current and modeling of excess air coefficient by artificia...
Artificial neural networks (ANN), due to their potential to capture building energy systems complex interactions are regarded as powerful surrogate models; however, the definition of optimal ANN structures and hyperparameters have been overlooked causing substandard prediction performance. The aim of this...
For each possible combination of values of hyperparameters among the considered candidate sets (the points of the ideal grid), a fivefold cross-validation procedure is used to assess the quality of the model for such combination. In the fivefold cross-validation, the training set is partitioned...
For all studied networks, we observe that AI Pontryagin reaches synchronization slightly faster than the AGM (Fig.4a–d). We optimized the hyperparameters (e.g., the number of training epochs) of the artificial neural network underlying AI Pontryagin such that the control energy and degree of ...
Fine-Tuning Neural Network Hyperparameters 神经网络的灵活性同样也是它最主要的缺点,因为有太多的参数可以调整,比方说,网络拓扑,层数,每一层神经元的个数,每一层的激活函数,权值初始化等等很多参数,那么如何来获取最优的参数呢? 当然,我们可以使用grid search方法,但这种方法需要花费大量的时间,并且只能计算出一部...