DL之pix2pix：基于TF利用pix2pix模型对food_resized数据集实现Auto Color自动上色技术—训练测试过程全记录
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DL之pix2pix：基于TF利用pix2pix模型对food_resized数据集实现Auto Color自动上色技术—训练测试过程全记录
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DL之pix2pix：基于TF利用pix2pix模型對food_resized數據集實現Auto Color自動上色技術
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目錄
訓練
food_resized數據集展示
TB過程監控
1、SCALARS
2、IMAGES
3、GRAPHS
4、DISTRIBUTIONS
輸出結果
訓練過程
測試
測試代碼
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訓練
food_resized數據集展示
food_resized數據集包含兩個文件夾分別是train(含有7472張)、val文件夾(含有1868張)。
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TB過程監控
1、SCALARS
2、IMAGES
| inputs_summary | outputs_summary | ||
| predict_fake_summary | predict_real_summary | ||
| targets_summary | ? | ? |
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3、GRAPHS
4、DISTRIBUTIONS
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訓練過程
1014~2300
開始訓練 aspect_ratio = 1.0 batch_size = 1 beta1 = 0.5 checkpoint = None display_freq = 0 flip = True gan_weight = 1.0 input_dir = datasets/colorlization/food_resized/train l1_weight = 100.0 lab_colorization = True lr = 0.0002 max_epochs = 70 max_steps = None mode = train ndf = 64 ngf = 64 output_dir = colorlization_food output_filetype = png progress_freq = 50 save_freq = 5000 scale_size = 286 seed = 1739403374 summary_freq = 100 trace_freq = 0 which_direction = AtoB examples count = 74722018-10-08 10:14:52.243605: parameter_count = 57176448parameter_count = 57176448 progress epoch 1 step 50 image/sec 0.3 remaining 775m discrim_loss 0.62115204 gen_loss_GAN 0.32054898 gen_loss_L1 0.053016115 recording summary progress epoch 1 step 100 image/sec 0.3 remaining 772m discrim_loss 0.94412124 gen_loss_GAN 0.49388388 gen_loss_L1 0.0746072 progress epoch 1 step 150 image/sec 0.3 remaining 764m discrim_loss 1.0989376 gen_loss_GAN 0.61638796 gen_loss_L1 0.091716774 recording summary progress epoch 1 step 200 image/sec 0.3 remaining 809m discrim_loss 1.1131265 gen_loss_GAN 0.8062535 gen_loss_L1 0.10253964 progress epoch 1 step 250 image/sec 0.3 remaining 800m discrim_loss 1.0876915 gen_loss_GAN 1.0041593 gen_loss_L1 0.105531484 recording summary progress epoch 1 step 300 image/sec 0.3 remaining 784m discrim_loss 1.1135826 gen_loss_GAN 1.1223749 gen_loss_L1 0.10621897 progress epoch 1 step 350 image/sec 0.3 remaining 773m discrim_loss 1.0985996 gen_loss_GAN 1.2152339 gen_loss_L1 0.115552105 recording summary progress epoch 1 step 400 image/sec 0.3 remaining 757m discrim_loss 1.0865264 gen_loss_GAN 1.256914 gen_loss_L1 0.11868134 progress epoch 1 step 450 image/sec 0.3 remaining 743m discrim_loss 1.0460857 gen_loss_GAN 1.2697574 gen_loss_L1 0.118321225 recording summary progress epoch 1 step 500 image/sec 0.3 remaining 731m discrim_loss 1.0442727 gen_loss_GAN 1.329202 gen_loss_L1 0.11831482 progress epoch 1 step 550 image/sec 0.3 remaining 719m discrim_loss 1.0788169 gen_loss_GAN 1.289733 gen_loss_L1 0.116190314 recording summary progress epoch 1 step 600 image/sec 0.3 remaining 712m discrim_loss 1.0257062 gen_loss_GAN 1.3354563 gen_loss_L1 0.117284976 progress epoch 1 step 650 image/sec 0.3 remaining 705m discrim_loss 1.0450379 gen_loss_GAN 1.3233901 gen_loss_L1 0.12282534 recording summary progress epoch 1 step 700 image/sec 0.3 remaining 702m discrim_loss 1.1048021 gen_loss_GAN 1.2816317 gen_loss_L1 0.12615271 progress epoch 1 step 750 image/sec 0.3 remaining 701m discrim_loss 1.1048292 gen_loss_GAN 1.2455337 gen_loss_L1 0.12358355 recording summary progress epoch 1 step 800 image/sec 0.3 remaining 697m discrim_loss 1.08388 gen_loss_GAN 1.2809262 gen_loss_L1 0.12027298 progress epoch 1 step 850 image/sec 0.3 remaining 697m discrim_loss 1.0998135 gen_loss_GAN 1.2463636 gen_loss_L1 0.11875751 recording summary progress epoch 1 step 900 image/sec 0.3 remaining 698m discrim_loss 1.0821528 gen_loss_GAN 1.2356712 gen_loss_L1 0.116755895 progress epoch 1 step 950 image/sec 0.3 remaining 695m discrim_loss 1.0927113 gen_loss_GAN 1.2259306 gen_loss_L1 0.11555332 recording summary progress epoch 1 step 1000 image/sec 0.3 remaining 690m discrim_loss 1.0598176 gen_loss_GAN 1.232778 gen_loss_L1 0.11947774 progress epoch 1 step 1050 image/sec 0.3 remaining 686m discrim_loss 1.0703127 gen_loss_GAN 1.2645109 gen_loss_L1 0.1193775 recording summary progress epoch 1 step 1100 image/sec 0.3 remaining 683m discrim_loss 1.0416253 gen_loss_GAN 1.298146 gen_loss_L1 0.12060161 progress epoch 1 step 1150 image/sec 0.3 remaining 680m discrim_loss 1.0654762 gen_loss_GAN 1.2623503 gen_loss_L1 0.11927286 recording summary progress epoch 1 step 1200 image/sec 0.3 remaining 677m discrim_loss 1.027089 gen_loss_GAN 1.3017185 gen_loss_L1 0.11801368 progress epoch 1 step 1250 image/sec 0.3 remaining 674m discrim_loss 0.9931668 gen_loss_GAN 1.3387213 gen_loss_L1 0.1153132 recording summary progress epoch 1 step 1300 image/sec 0.3 remaining 670m discrim_loss 1.0244601 gen_loss_GAN 1.3321183 gen_loss_L1 0.116006255 progress epoch 1 step 1350 image/sec 0.3 remaining 665m discrim_loss 1.0471783 gen_loss_GAN 1.2837918 gen_loss_L1 0.11803301 recording summary progress epoch 1 step 1400 image/sec 0.3 remaining 660m discrim_loss 1.0412923 gen_loss_GAN 1.2881491 gen_loss_L1 0.11771501 progress epoch 1 step 1450 image/sec 0.3 remaining 656m discrim_loss 1.0294466 gen_loss_GAN 1.2744259 gen_loss_L1 0.116808206 recording summary progress epoch 1 step 1500 image/sec 0.3 remaining 652m discrim_loss 1.0183508 gen_loss_GAN 1.292259 gen_loss_L1 0.113540195 progress epoch 1 step 1550 image/sec 0.3 remaining 647m discrim_loss 1.0143778 gen_loss_GAN 1.3468136 gen_loss_L1 0.118333735 recording summary progress epoch 1 step 1600 image/sec 0.3 remaining 643m discrim_loss 1.0525765 gen_loss_GAN 1.2853703 gen_loss_L1 0.118325554 progress epoch 1 step 1650 image/sec 0.3 remaining 642m discrim_loss 1.0654823 gen_loss_GAN 1.2534837 gen_loss_L1 0.118441276 recording summary 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step 6478 image/sec 0.4 remaining 47m discrim_loss 0.8745096 gen_loss_GAN 1.6244593 gen_loss_L1 0.11375605 recording summary progress epoch 2 step 6528 image/sec 0.4 remaining 44m discrim_loss 0.8032784 gen_loss_GAN 1.6290073 gen_loss_L1 0.111008376 progress epoch 2 step 6578 image/sec 0.4 remaining 42m discrim_loss 0.90288186 gen_loss_GAN 1.6073776 gen_loss_L1 0.11052054 recording summary progress epoch 2 step 6628 image/sec 0.4 remaining 39m discrim_loss 0.78720987 gen_loss_GAN 1.6435641 gen_loss_L1 0.11002786 progress epoch 2 step 6678 image/sec 0.4 remaining 37m discrim_loss 0.88915396 gen_loss_GAN 1.5216315 gen_loss_L1 0.112931326 recording summary progress epoch 2 step 6728 image/sec 0.4 remaining 35m discrim_loss 0.8614181 gen_loss_GAN 1.5692296 gen_loss_L1 0.1112233 progress epoch 2 step 6778 image/sec 0.4 remaining 32m discrim_loss 0.88211584 gen_loss_GAN 1.5681787 gen_loss_L1 0.11220956 recording summary progress epoch 2 step 6828 image/sec 0.4 remaining 30m discrim_loss 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progress epoch 2 step 7228 image/sec 0.4 remaining 11m discrim_loss 0.84345067 gen_loss_GAN 1.511617 gen_loss_L1 0.115258954 progress epoch 2 step 7278 image/sec 0.4 remaining 9m discrim_loss 0.81968087 gen_loss_GAN 1.5898192 gen_loss_L1 0.11567144 recording summary progress epoch 2 step 7328 image/sec 0.4 remaining 6m discrim_loss 0.8464879 gen_loss_GAN 1.6120932 gen_loss_L1 0.119259834 progress epoch 2 step 7378 image/sec 0.4 remaining 4m discrim_loss 0.7806106 gen_loss_GAN 1.6913474 gen_loss_L1 0.11713983 recording summary progress epoch 2 step 7428 image/sec 0.4 remaining 2m discrim_loss 0.844849 gen_loss_GAN 1.6245934 gen_loss_L1 0.11504359 recording summary progress epoch 2 step 7472 image/sec 0.4 remaining 0m discrim_loss 0.8442991 gen_loss_GAN 1.5988972 gen_loss_L1 0.117679566 saving model測試
測試代碼
loaded lab_colorization = True loaded ndf = 64 loaded ngf = 64 loaded which_direction = AtoB aspect_ratio = 1.0 batch_size = 1 beta1 = 0.5 checkpoint = colorization_food display_freq = 0 flip = False gan_weight = 1.0 input_dir = datasets/colorization/food_resized/val l1_weight = 100.0 lab_colorization = True lr = 0.0002 max_epochs = None max_steps = None mode = test ndf = 64 ngf = 64 output_dir = colorlization_food_test output_filetype = png progress_freq = 50 save_freq = 5000 scale_size = 256 seed = 997446065 summary_freq = 100 trace_freq = 0 which_direction = AtoB……Exception: input_dir does not exist?
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總結
以上是生活随笔為你收集整理的DL之pix2pix：基于TF利用pix2pix模型对food_resized数据集实现Auto Color自动上色技术—训练测试过程全记录的全部內容，希望文章能夠幫你解決所遇到的問題。
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