This paper presents an algorithm for searching for the minimum number of neurons in fully connected layers of an arbitrary network solving given problem, which does not require multiple training of the network with different number of neurons. The algorithm is based at training the initial wide network using the cross-validation method over at least two folds. Then by using truncated singular value decomposition autoencoder inserted after the studied layer of trained network we search the minimum number of neurons in inference only mode of the network. It is shown that the minimum number of neurons in a fully connected layer could be interpreted not as network hyperparameter associated with the other hyperparameters of the network, but as internal (latent) property of the solution, determined by the network architecture, the training dataset, layer position, and the quality metric used. So the minimum number of neurons can be estimated for each hidden fully connected layer independently. The proposed algorithm is the first approximation for estimating the minimum number of neurons in the layer, since, on the one hand, the algorithm does not guarantee that a neural network with the found number of neurons can be trained to the required quality, and on the other hand, it searches for the minimum number of neurons in a limited class of possible solutions. The solution was tested on several datasets in classification and regression problems.

该论文提出了一种算法，用于搜索解决给定问题的任意网络中完全连接层中的最少神经元数量，该算法不需要使用不同数量神经元的网络进行多次训练。该算法基于交叉验证方法在至少两个折叠上对初始宽网络进行训练。然后通过使用截断奇异值分解自动编码器插入在训练网络的所研究层之后，我们仅在网络的推断模式中搜索最少数量的神经元。

给定神经网络中全连接层的最小神经元数量（第一近似）