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正确，但loss为0，接着的epoch训练和验证结果都是只有千分之几，loss为0，后期的epoch中 top_1_accuracy =0,top_5_accuracy=1,loss 约千万级别。 二、软件版本: -- CANN 版本 (0803 5.0
systemd长时间CPU冲高 【实际结果】 ``` time killall -wq aa real 4m40.946s user 0m0.010s sys 0m0.052s ``` ``` top # during killall PID USER PR NI VIRT RES
= 81, w = 54, size = 139968 [INFO 2018-06-04 23:34:36,719 layers.py:3251] output for __bat ch_norm_0__: c = 32, h = 81, w = 54,
ob/hrnet/configs/top_down/naive_litehrnet/coco/naive_litehrnet_18_coco_256x192.py) | 256x192 | 0.7M | 194.8M | 0.628 | 0.855 |
Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | | | | MIG
66qz68mKkZBSTOn-h7Vo_w-eJozas9TCCcDa-TPn3R85sR8Tm3jlhF52bITnZ27THc25cqw87s1xi6Gv82F9rjMCLeHn_gtA-ueu8top52GlkwXguecu5gyqS79PPD
IMG_REAL_W, imageH: IMG_REAL_H, scaleP: IMG_REAL_W / SCREEN_WIDTH, qualityWidth: DRAW_IMAGE_W, originalW: ORIGINAL_W, originalH:
pooling layer top_model = base_model.output top_model = GlobalAveragePooling2D()(top_model) # or just flatten the layers # top_model =
install php72w-fpm php72w-gd.x86_64 php72w-bcmath.x86_64 php72w-xml.x86_64 php72w-mbstring.x86_64 php72w-ldap.x86_64 php72w-mysqlnd
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