The neural network MnasNet uses a training method with reinforcement to select the appropriate mobile device architecture.
The MnasNet system contains:
- object management based on a recurrent neural network for learning and selecting the architecture of the model;
- he coach who builds the models and teaches their accuracy;
- an output machine that calculates the speed of the model on the phones using TensorFlow.
The team used multi-criteria optimization to achieve high speed and accuracy. In addition, scientists used the learning algorithm with reinforcement and remuneration function. Thus, MnasNet finds Pareto optimality for each platform. For each mobile device, individual features are created that require a specific architecture.
Automated Neural Design Approach
To achieve the optimal balance between the flexibility of search and the scope of feasible solutions, a hierarchical approach has been applied. It represents a convolutional neural network in the form of a series of blocks. After, sequential search to assign layers of architecture to each block is being used. Due to this, each layer uses different operations and connections. In this case, the layers of blocks are copied by force.
Scientists have included the search speed of architecture in the function of rewarding the search algorithm.
According to the developers, the system picks up the model 1.5 times faster than MobileNetV2 and 2.4 times faster than NASNet, while taking into account the optimal accuracy and speed. As a proof, the result was demonstrated by the example of working with ImageNet image database.