伝説のアニメ、『キルミーベイベー』のアニメキャラの resnet-110によるキャラクター分類テストです。 SONY　Neural Network Console

1. C L A S S I F Y I M A G E S U S I N G D E E P R E S I D U A L N E T W O R K
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> K A I M I N G H E , X I A N G Y U Z H A N G , S H A O Q I N G R E N , J I A N
S U N .
> D E E P R E S I D U A L L E A R N I N G F O R I M A G E R E C O G N I T I O N .
> A R X I V : 1 5 1 2 . 0 3 3 8 5
D E S C R I P T I O N
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P R O B L E M T Y P E : M U L T I - C L A S S C L A S S I F I C A T I O N
I N P U T S : 3 2 X 3 2 R G B I M A G E
T A R G E T : 1 0 C L A S S
D A T A S E T - キ ル ミ ー ベ イ ベ ー
resnet-110

2. Dataset : Training
 Number of data : 514
 Variable : x (image)
 Type : Image
 Shape : 3, 128, 128
 Variable : y (label)
 Type : Scalar

3. Dataset : Examples of variable x in "Training"

4. Dataset : Validation
 Number of data : 58
 Variable : x (image)
 Type : Image
 Shape : 3, 128, 128
 Variable : y (label)
 Type : Scalar

5. Dataset : Examples of variable x in "Validation"

6. Network Architecture : Main
Type Value
Output 67,340,288
CostParameter 1,762,548
CostAdd 25,100,296
CostMultiply 16,778,240
CostMultiplyAdd 4,083,945,472
CostDivision 4
CostExp 4
CostIf 16,712,704

7. Training Procedure : Optimizer
 Optimize network "Main" using "Training" dataset.
 Batch size : 32
 Solver : Nesterov
 Learning rate: 0.05
 decayed with cosine annealing.
 Momentum : 0.9
 Weight decay : 0.0005

8. Experimental Result : Learning Curve

9. Experimental Result : Evaluation
 Evaluate network "MainRuntime" using "Validation"
dataset.
 Variable : y
 Accuracy : 0.8793
 Avg.Precision : 0.9243
 Avg.Recall : 0.8623
 Avg.F-Measures : 0.8812

10. 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
 ReLU - Vinod Nair, Geoffrey E. Hinton. Rectified Linear Units Improve Restricted Boltzmann
Machines.
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.165.6419&rep=rep1&type=pdf
 Nesterov - Matthew D. Zeiler. ADADELTA: An Adaptive Learning Rate Method.
https://arxiv.org/abs/1212.5701