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Abstract Current deep learning technologies used a large number of parameters to achieve a high accuracy rate, and the number of parameters is commonly more than a hundred million for image-related tasks. To improve both training speed and accuracy in multi-clouds, distributed deep learning is also widely applied. Therefore, reducing the network scale or improving the training speed has become an urgent problem to be solved in multi-clouds. Concerning this issue, we proposed a game architecture in multi-clouds, which can be supported by resource provision and service schedule. Furthermore, we trained a deep learning network, which can ensure high accuracy while reducing the number of network parameters. An adapted game, called flappy bird, is used as an experimental environment to test our neural network. Experimental results showed that the decision logic of the flappy bird, including flight planning, avoidance, and sacrifice, is accurate. In addition, we published the parameters of the neural network, so other scholars can reuse our neural network parameters for further research. |
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