1 Introduction
A deep neural network for classification tasks is consist of two components: 1) features extractors, such as the convolutional neural network
[6, 12], the recurrent neural network
[9, 8], and the transformer [15], that extract the feature from the raw data and 2) the function approximator that finds the function mapping features to labels. Those two components are both important for the effectiveness and efficiency in solving classification tasks.In designing the function approximator that finds the function mapping features to labels, on the one hand, one has to make sure that the target function is in the range of searching or equivalently, the network should have the property of universal approximation [16, 7, 11, 13]. On the other hand, one should also consider the efficiency of potential networks. The nonlinearity is the main difficulty in designing the function approximator, one usually uses the method based on the nonlinear activation function [11, 5, 17, 14] or the nonlinear kernel function [13, 2] and thus yields classical networks such the feedforward neural network (MLP) [16, 7, 11] and the radial basis function network (RBF) [13, 2]. In the past decades, a lot of researches prove that those networks have the property of universal approximation [16, 7, 11, 13].
Although, classical networks such as the MLP are robust in most of classification tasks, they are not the most efficient [18]. E.g., they use large amount of parameters and take long times to train. Additionally, the choice of activation functions has a nonnegligible influence on the effectiveness and efficiency of the network. That is, we need new networks that is more efficient.
In this paper, we propose a new network that is efficient in finding the function that maps the feature to the label. Instead of using the nonlinear activation function, the new proposed network uses the fractional form to overcome the nonlinearity, thus for the sake of convenience, we name the network the ratio net. The ratio net is inspired by the previous work [18], where we find that the Pade approximant with fractional forms is highly efficient in searching a target function. We compare the effectiveness and efficiency of the ratio net with the classical networks such as the MLP and the RBF in the classification task on the mnist database of handwritten digits [10, 3] and the IMDb dataset [4] which is a binary sentiment analysis dataset. The result shows that the ratio net outperforms both the MLP and the RBF. The source code of the present paper is given in github .
The work is organized as follows: in Sec. 2, we give the structure of the ratio net. In Sec. 3, we compare the effectiveness and efficiency of the ratio net with the classical networks such as the MLP and the RBF in the classification task on the mnist database of handwritten digits and the IMDb dataset. Conclusions and outlooks are given in Sec. 4.
2 The structure of the ratio net
The nonlinearity is the main difficulty in designing the function approximator. One usually uses the method based on the nonlinear activation function and gives classical networks such as the MLP [11, 5, 17]. Others use the nonlinear kernel function and gives classical networks such as the RBF [13, 2]. In the past decades, those networks are proved to have the property of universal approximation [16, 7, 11, 13, 2]. In this section, we show that there is another way to handle the difficulty caused by the nonlinearity: the ratio net with fractional forms. We introduce the structure of the ratio net.
The structure of the ratio net.
The function approximator gives a function that maps a vector in
to another vector in . The structure of classical networks such as the MLP and the RBF are and where is the component of the output, is the activation function, and is the kernel function. , , , and are parameters. Usually,can be chosen from the sigmoid function, the hyperbolic tangent (tanh) function and the rectified linear unit (relu) function.
can be a Gauss function. As shown in Figs. (1) and (2).Here, we propose the ratio net. The structure of the ratio net is
(2.1) 
where and are parameters. In Eq. (3.1), instead of the nonlinear activation function or the nonlinear kernel function, the nonlinearity is overcomed by using the fractional form. As shown in Fig. (3).
The property of universal approximation of the ratio net: a brief discussion. In designing the function approximator, one has to make sure that the target function mapping the feature to the label is in the range of searching. That is, the network should have the property of universal approximation. Although, the rigorious proof of the property of universal approximation of the ratio net is not given, we show that, the property of universal approximation of the ratio net is inherited from the Pade approximant.
The conventional Pade approximant is given as [1]
(2.2) 
The Pade approximant has a Maclaurin expansion which agrees with a power series of order [1], that is
(2.3) 
For example, with a polymial of order , one can draw an elephant. Thus the Pade approximant is capable of approximating various kind of functions.
The conventional Pade approximant gives functions that maps a onedimension vector to another onedimension vector. The ratio net proposed in the paper is a generalization of the Pade approximant and gives functions that a vector in to another vector in . Thus, the property of universal approximation of the ratio net is inherited from the Pade approximant.
3 The classification task on the mnist database and the IMDb dataset
In this section, in order to show that the ratio net is capable in finding the function that maps the feature to the label efficiently, we compare the effectiveness and efficiency of the ratio net and the classical networks such as the MLP and the RBF in the classification task on the mnist database of handwritten digits and the IMDb dataset. The result shows that the ratio net outperforms both the MLP and the RBF.
3.1 The task on the mnist database
The mnist database of handwritten digits [10, 3] is an famous open dataset for image classification task. There are 60,000 training images and 10,000 test images in the database. In this section, we use the convolutional autoencoder network to extract features from a picture. As a result, we obtained a dimensional feature from a pixes picture. Then, an MLP, an RBF, and a ratio net with different structures are used to find the function that maps the dimensional feature to the label, which has different values.
The result is given as follows:
Structures  Number of parameters  The accuracy 

Where "the ratio net:" denotes a ratio net with the structure
(3.1) 
and so on. "MLP:" denotes a twolayered MLP with neurons in each layer and the activation function the hyperbolic tangent function and so on.
The efficiency of each methods are show in Fig. (5). It shows that the ratio net converges fast and outperforms the classical networks such as the MLP and the RBF.
3.2 The task on the IMDb dataset
The IMDb dataset is a binary sentiment analysis dataset consisting of 50,000 reviews from the Internet Movie Database (IMDb) labeled as positive or negative. The IMDb dataset is an famous open dataset for NLP classification task. In this section, we use the textcnn model to extract features from a sentence. As a result, we obtained a dimensional feature from a given sample in the IMDb dataset. Then, an MLP and a ratio net with different structures are used to find the function that maps the dimensional feature to the binary label.
The result is given as follows.
Structures  Number of parameters  The accuracy 

textcnn with random embedding 
The efficiency of each methods are show in Fig. (5)
4 Conclusions and outlooks
In this paper, we propose a new network that is efficient in finding the function that maps features to labels. Instead of using the nonlinear activation function, the new proposed network uses the fractional form to overcome the nonlinearity, thus for the sake of convenience, we name the network the ratio net. The ratio net is inspired by the previous work [18], where we find that the Pade approximant is highly efficient in searching a target function. We compare the effectiveness and efficiency of the ratio net and the classical networks such as the MLP and the RBF in the classification task on the mnist database of handwritten digits and the IMDb dataset. The result shows that the ratio net outperforms both the MLP and the RBF.
The ratio net can replace the MLP in various kinds of classification tasks and thus improve the effectiveness and efficiency.
5 Acknowledgments
We are very indebted to Prof. WuSheng Dai for his enlightenment and encouragement.
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