  In traditional neural networks for image processing, the inputs of the neural
networks should be the same size such as 224*224*3. But how can we train the
neural net model with different input size? A common way to do is image
deformation which accompany a problem of information loss (e.g. image crop or
wrap). Sequence model(RNN, LSTM, etc.) can accept different size of input like
text and audio. But one disadvantage for sequence model is that the previous
information will become more fragmentary during the transfer in time step, it
will make the network hard to train especially for long sequential data. In
this paper we propose a new network structure called Attention Incorporate
Network(AIN). It solve the problem of different size of inputs including:
images, text, audio, and extract the key features of the inputs by attention
mechanism, pay different attention depends on the importance of the features
not rely on the data size. Experimentally, AIN achieve a higher accuracy,
better convergence comparing to the same size of other network structure
