Correction: Journal of Cloud Computing (2024) 13:57
Anzeige
Following publication of the original article [1], we have been notified that there is duplicate of the body text in the published article.
Now the text is:
MLP ((model): Sequential ((0): Linear (in_features=3, out_features=200, bias=True)
1.
Dropout (p=0.2, inplace=False)
2.
ReLU ()
3.
Linear (in_features=200, out_features=2, bias=True)))
Anzeige
The improved MLP comprises linear layers, Dropout, and the ReLU activation function. This architecture is established using the Sequential class to construct a feedforward neural network for sample classification.
Initially, the linear layer conducts linear transformations to augment the feature information of the samples, with an input dimension of 2 and an output dimension of 200. Dropout is then implemented with a probability of 0.2 for random Dropout, mitigating overfitting. Subsequently, the ReLU non-linear activation function is employed to enhance the network?s non-linear expressive capability. Finally, the linear layer is utilized for dimension reduction and classification purposes.
MLP ((model): Sequential ((0): Linear (in_features=3, out_features=200, bias=True)
1.
Dropout (p=0.2, inplace=False)
2.
ReLU ()
3.
Linear (in_features=200, out_features=2, bias=True)))
It should be:
MLP ((model): Sequential ((0): Linear (in_features=3, out_features=200, bias=True)
1.
Dropout (p=0.2, inplace=False)
2.
ReLU ()
3.
Linear (in_features=200, out_features=2, bias=True)))
The improved MLP comprises linear layers, Dropout, and the ReLU activation function. This architecture is established using the Sequential class to construct a feedforward neural network for sample classification.
Initially, the linear layer conducts linear transformations to augment the feature information of the samples, with an input dimension of 2 and an output dimension of 200. Dropout is then implemented with a probability of 0.2 for random Dropout, mitigating overfitting. Subsequently, the ReLU non-linear activation function is employed to enhance the network?s non-linear expressive capability. Finally, the linear layer is utilized for dimension reduction and classification purposes.
The original article was updated.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.