수행기록퀘스트1
1. MCU 선정
STM32L475VGTx MCU를 선택
2. 소프트웨어 팩 선택
-X cube AI 설정
3.MCU 설정 및 소프트웨어 팩 설정
4. Analyuze 시료
Analyzing model
C:/Users/kjh14/STM32Cube/Repository/Packs/STMicroelectronics/X-CUBE-AI/7.1.0/Utilities/windows/stm32ai analyze --name network -m F:/Util/Software tool/STM/en.fp-ai-sensing1/STM32CubeFunctionPack_SENSING1_V4.0.3/Utilities/AI_Ressources/models/cnn_gmp.h5 --type keras --compression 1 --verbosity 1 --workspace C:\Users\kjh14\AppData\Local\Temp\mxAI_workspace169198008605006123145192526529961 --output C:\Users\kjh14\.stm32cubemx\network_output
Neural Network Tools for STM32AI v1.6.0 (STM.ai v7.1.0-RC3)
Exec/report summary (analyze)
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model file : F:\Util\Software tool\STM\en.fp-ai-sensing1\STM32CubeFunctionPack_SENSING1_V4.0.3\Utilities\AI_Ressources\models\cnn_gmp.h5
type : keras
c_name : network
compression : None
workspace dir : C:\Users\kjh14\AppData\Local\Temp\mxAI_workspace169198008605006123145192526529961
output dir : C:\Users\kjh14\.stm32cubemx\network_output
model_name : cnn_gmp
model_hash : 954164d0a35496bd293bb6b3429c6a79
input 1/1 : 'input_0'
72 items, 288 B, ai_float, float, (1,24,3,1), domain:user/
output 1/1 : 'softmax_1'
5 items, 20 B, ai_float, float, (1,1,1,5), domain:user/
params # : 1,477 items (5.77 KiB)
macc : 68,928
weights (ro) : 5,908 B (5.77 KiB) (1 segment)
activations (rw) : 6,976 B (6.81 KiB) (1 segment)
ram (total) : 7,284 B (7.11 KiB) = 6,976 + 288 + 20
Model name - cnn_gmp ['input_0'] ['softmax_1']
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id layer (type) oshape param/size macc connected to | c_size c_macc c_type
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0 input_0 (Input) (None,24,3,1) |
quantize_1_conv2d (Conv2D) (None,20,3,16) 96/384 4,816 input_0 | +960(+19.9%) conv2d()[0]
quantize_1 (Nonlinearity) (None,20,3,16) 960 quantize_1_conv2d | -960(-100.0%)
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1 quantize_2_conv2d (Conv2D) (None,16,3,16) 1,296/5,184 61,456 quantize_1 | +1,536(+2.5%) optimized_conv2d()[1]
quantize_2 (Nonlinearity) (None,16,3,16) 768 quantize_2_conv2d | -768(-100.0%)
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2 quantize_3 (Pool) (None,1,1,16) 768 quantize_2 | -768(-100.0%)
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3 quantize_4_dense (Dense) (None,1,1,5) 85/340 85 quantize_3 | dense()[2]
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4 softmax_1 (Nonlinearity) (None,1,1,5) 75 quantize_4_dense | nl()/o[3]
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model/c-model: macc=68,928/68,928 weights=5,908/5,908 activations=--/6,976 io=--/308
Complexity report per layer - macc=68,928 weights=5,908 act=6,976 ram_io=308
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id name c_macc c_rom c_id
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0 quantize_1_conv2d || 8.4% || 6.5% [0]
1 quantize_2_conv2d |||||||||||||||| 91.4% |||||||||||||||| 87.7% [1]
3 quantize_4_dense | 0.1% | 5.8% [2]
4 softmax_1 | 0.1% | 0.0% [3]
Creating txt report file C:\Users\kjh14\.stm32cubemx\network_output\network_analyze_report.txt
elapsed time (analyze): 0.577s
Analyze complete on AI model
5. Network
개발환경 구축 완료
- 첨부파일
- STM32cubeMX.AI.zip 다운로드
로그인 후
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