With the expansion of the Internet market,the traditional software development method has been difficult to meet the market demand due to the problems of long development cycle,tedious work,and difficult system ***,to...
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With the expansion of the Internet market,the traditional software development method has been difficult to meet the market demand due to the problems of long development cycle,tedious work,and difficult system ***,to improve software development efficiency,this study uses residual networks and bidirectional long short-term memory(BLSTM)networks to improve the pix2code *** experiment results show that after improving the visual module of the pix2code model using residual networks,the accuracy of the training set improves from 0.92 to 0.96,and the convergence time is shortened from 3 hours to 2 *** using a BLSTM network to improve the language module and decoding layer,the accuracy and convergence speed of the model have also been *** accuracy of the training set grew from 0.88 to 0.92,and the convergence time was shortened by 0.5 ***,models improved by BLSTM networks might exhibit overfitting,and thus this study uses Dropout and Xavier normal distribution to improve the memory *** results validate that the training set accuracy of the optimized BLSTM network remains around 0.92,but the accuracy of the test set has improved to a maximum of 85%.Dropout and Xavier normal distributions can effectively improve the overfitting problem of BLSTM *** they can also decrease the model’s stability,their gain is *** training and testing accuracy of the pix2code improved by residual network and BLSTM network are 0.95 and 0.82,respectively,while the code generation accuracy of the original pix2code is only *** above data indicate that the improved pix2code model has improved the accuracy and stability of code automatic generation.
作者：
Liu, YanbinHu, QidiShu, KunxianCQUPT
Chongqing Key Lab Big Data Bio Intelligence Chongqing Peoples R China CQUPT
Coll Comp Sci & Technol Chongqing Peoples R China CQUPT
Chongqing Key Lab Big Data Bio Intelligence Chongqing Peoples R China
pix2code is a framework based on deep learning to transform a graphical user interface screenshot created by the designer into computer coder with 77% of accuracy. The architecture is based on CNN and LSTM. LSTM has b...
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ISBN:
(纸本)9781538678619
pix2code is a framework based on deep learning to transform a graphical user interface screenshot created by the designer into computer coder with 77% of accuracy. The architecture is based on CNN and LSTM. LSTM has been broadly applied to natural language processing about language model, which is both general and effective at capturing long-term dependencies. However, the standard LSTM predicting in time sequence ignores the contextual information of the future, but sometimes it is not enough just to look at the previous word. Computer code is a relative spatial relationship and not only needs to recognize token but also fully understands the structure of all sequences. In order to solve the problem, the pix2code model is optimized by Bidirectional LSTM, which allows the output layer to get complete past and future context information for each point in the input sequence. The model's transforming accuracy in the test set has been significantly improved reaching 85%.
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