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2026-10-21, Week 43 |
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* Edit or answer any Q&A by selecting Q&A number Hyper Link below + Write button (Save)
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ICACT20230240 Question.1
Questioner: sdk@etri.re.kr 2023-02-22 오후 9:57:50 |
ICACT20230240 Answer.1
Answer by Auhor vuvietvu@vnu.edu.vn 2023-02-22 오후 9:57:50
Chrome  Click!! |
| Can you show the hyperparameters such as model structure, loss function, number of epochs to represent the deep learning network?
Please show the parameters other than the activation function used in the comparison experiment. |
Thank you very much for the question. Here, I show some information about this: Loss function: BinaryCrossentropy
Optimizer: Adam - learing rate: 0.0001
Metrics: Categorical Accuracy
Epoch: 200, batch size: 64
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ICACT20230240 Question.2
Questioner: saby9996@terenz.ai 2023-02-24 오후 11:36:09 |
ICACT20230240 Answer.2
Answer by Auhor vuvietvu@vnu.edu.vn 2023-02-24 오후 11:36:09
Chrome Click!! |
| Apart from Activation Functions, the Model Structure is also necessary. Please xplain |
Architecture Connected Note
Layer Kernel Output
Shape Layer Kernel Output
Shape Layer Kernel Output
Shape
Input 13x1x1
Conv2D 1x1 13x1x32 Conv2D 3x3 13x1x32 Conv2D 5x5 13x1x32 Input Block
Identity A
BatchNorm 13x1x32 BatchNorm 13x1x32 BatchNorm 13x1x32 Conv2D x 3
Concat (A) 13x1x97 BatchNorm x 3
Conv2D 1x1 13x1x64 Conv2D 3x3 13x1x64 Conv2D 5x5 13x1x64 Concat
(Block_ID_A) Block
Identity B
BatchNorm 13x1x64 BatchNorm 13x1x64 BatchNorm 13x1x64 Conv2D x 3
Concat (B) 13x1x289 BatchNorm x 3
Conv2D 1x1 13x1x128 Conv2D 3x3 13x1x128 Conv2D 5x5 13x1x128 Concat
(Block_ID_B) Block
Identity C
BatchNorm 13x1x128 BatchNorm 13x1x128 BatchNorm 13x1x128 Conv2D x 3
Concat (C) 13x1x289 BatchNorm x 3
Concat
(A, B, C) 13x1x1059 Concat_A, _B,
_C Head
Global Average Pooling2D 1059 Concat
(A, B, C)
Classification 4 Global Average
Pooling2D
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