  In real applications, generally small data sets can be obtained. At present,
most of the practical applications of machine learning use classic models based
on big data to solve the problem of small data sets. However, the deep neural
network model has complex structure, huge model parameters, and training
requires more advanced equipment, which brings certain difficulties to the
application. Therefore, this paper proposes the concept of union convolution,
designing a light deep network model union-net with a shallow network structure
and adapting to small data sets. This model combines convolutional network
units with different combinations of the same input to form a union module.
Each union module is equivalent to a convolutional layer. The serial input and
output between the 3 modules constitute a "3-layer" neural network. The output
of each union module is fused and added as the input of the last convolutional
layer to form a complex network with a 4-layer network structure. It solves the
problem that the deep network model network is too deep and the transmission
path is too long, which causes the loss of the underlying information
transmission. Because the model has fewer model parameters and fewer channels,
it can better adapt to small data sets. It solves the problem that the deep
network model is prone to overfitting in training small data sets. Use the
public data sets cifar10 and 17flowers to conduct multi-classification
experiments. Experiments show that the Union-net model can perform well in
classification of large data sets and small data sets. It has high practical
value in daily application scenarios. The model code is published at
https://github.com/yeaso/union-net
