BUILDING, VISUALIZING AND EXECUTING DEEP LEARNING MODELS AS DATAFLOW GRAPHS
In recent years many frameworks have appeared, which enable users to easily build, visualize and execute deep learning networks on graphical interfaces. However, they do not always provide enough opporunities to automate this process. Generally, data processing programs can be organized into dataf...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Technical University of Kosice
2020-08-01
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| Series: | Acta Electrotechnica et Informatica |
| Subjects: | |
| Online Access: | http://www.aei.tuke.sk/papers/2020/2/04_Kruppai.pdf |
| Summary: | In recent years many frameworks have appeared, which enable users to easily build, visualize and execute deep learning networks
on graphical interfaces. However, they do not always provide enough opporunities to automate this process.
Generally, data processing programs can be organized into dataflow graphs that define the operations to be performed sequentially
on the data. The operation of deep learning neural networks can also be interpreted in a similar way, in which the input data to be
processed is a specific data set and the operations to be performed on the data are the layers of the net.
Due to architectural reasons, the entire deep learning neural network graph must be built before actual running, thus it is necessary
to change topological execution of dataflows to evaluation preceding graph building since knowing the layers separately is not enough
to operate the nets. As a solution for displaying editable program graphs, we created a framework in which data processing related to
Python packages can be described and the programs built from them can be visualized and executed (mostly) automatically |
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| ISSN: | 1335-8243 1338-3957 |