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The document summarizes the classification of waveform data using a neural network. It includes details on the training and validation datasets, network architectures for training, validation and runtime, and the training procedure using the Adam optimizer on the training network and dataset. The experimental results show the training learning curve and an evaluation accuracy of 98% on the validation dataset using the runtime network.
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- 2. Dataset : Training ? Number of data : 315 ¨C Variable : x ? Type : Scalar ¨C Variable : y ? Type : Scalar
- 3. Dataset : Validation ? Number of data : 100 ¨C Variable : x ? Type : Scalar ¨C Variable : y ? Type : Scalar
- 4. Network Architecture : Training Type Value Output 231,310 CostParameter 8,404 CostAdd 1,057,256 CostMultiply 363,441 CostMultiplyAdd 8,978,608 CostDivision 352,420 CostExp 352,420 CostIf 352,352
- 5. Network Architecture : convnit
- 6. Network Architecture : Validation Type Value Output 220,285 CostParameter 8,404 CostAdd 1,057,256 CostMultiply 363,441 CostMultiplyAdd 8,978,608 CostDivision 352,420 CostExp 352,420 CostIf 352,352
- 7. Network Architecture : Runtime Type Value Output 220,284 CostParameter 8,404 CostAdd 1,057,256 CostMultiply 363,441 CostMultiplyAdd 8,978,608 CostDivision 352,420 CostExp 352,420 CostIf 352,352
- 8. Training Procedure : Optimizer ? Optimize network "Training" using "Training" dataset. ¨C Batch size : 64 ¨C Solver : Adam ? Learning rate(Alpha) : 0.001 ? Beta1 : 0.9 ? Beta2 : 0.999 ? Epsilon : 1e-08 ¨C Weight decay is not applied.
- 9. Experimental Result : Learning Curve
- 10. Experimental Result : Evaluation ? Evaluate network "Runtime" using "Validation" dataset. ? Variable : y ¨C Accuracy : 0.98 ¨C Avg.Precision : 0.975 ¨C Avg.Recall : 0.9864 ¨C Avg.F-Measures : 0.9798
- 11. References ? Sony Corporation. Neural Network Console : Not just train and evaluate. You can design neural networks with fast and intuitive GUI. https://dl.sony.com/ ? Sony Corporation. Neural Network Libraries : An open source software to make research, development and implementation of neural network more efficient. https://nnabla.org/ ? BatchNormalization - Ioffe and Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. https://arxiv.org/abs/1502.03167 ? Convolution - Chen et al., DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. https://arxiv.org/abs/1606.00915, Yu et al., Multi-Scale Context Aggregation by Dilated Convolutions. https://arxiv.org/abs/1511.07122 ? Swish - Prajit Ramachandran, Barret Zoph, and Quoc V. Le, SEARCHING FOR ACTIVATION FUNCTIONS. https://arxiv.org/abs/1710.05941 ? Adam - Matthew D. Zeiler. ADADELTA: An Adaptive Learning Rate Method. https://arxiv.org/abs/1212.5701