TF之DCGAN：基于TF利用DCGAN測試MNIST數(shù)據(jù)集并進(jìn)行生成

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目錄

測試結(jié)果

測試過程全記錄

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測試結(jié)果

train_00_0099		train_00_0799
train_00_0899		train_01_0506
train_01_0606		train_02_0213
train_02_0313		train_02_1013
train_03_0020		train_03_0720

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測試過程全記錄

1140~1410

……開始測試 {'batch_size': <absl.flags._flag.Flag object at 0x000002A2FFDB1B38>,'beta1': <absl.flags._flag.Flag object at 0x000002A2FE967DA0>,'checkpoint_dir': <absl.flags._flag.Flag object at 0x000002A281135A20>,'crop': <absl.flags._flag.BooleanFlag object at 0x000002A281135B70>,'dataset': <absl.flags._flag.Flag object at 0x000002A281135908>,'epoch': <absl.flags._flag.Flag object at 0x000002A2F7728048>,'h': <tensorflow.python.platform.app._HelpFlag object at 0x000002A281135C50>,'help': <tensorflow.python.platform.app._HelpFlag object at 0x000002A281135C50>,'helpfull': <tensorflow.python.platform.app._HelpfullFlag object at 0x000002A281135CC0>,'helpshort': <tensorflow.python.platform.app._HelpshortFlag object at 0x000002A281135D30>,'input_fname_pattern': <absl.flags._flag.Flag object at 0x000002A281135978>,'input_height': <absl.flags._flag.Flag object at 0x000002A2810ABCC0>,'input_width': <absl.flags._flag.Flag object at 0x000002A281135780>,'learning_rate': <absl.flags._flag.Flag object at 0x000002A2F92D7AC8>,'output_height': <absl.flags._flag.Flag object at 0x000002A2811357F0>,'output_width': <absl.flags._flag.Flag object at 0x000002A281135898>,'sample_dir': <absl.flags._flag.Flag object at 0x000002A281135A90>,'train': <absl.flags._flag.BooleanFlag object at 0x000002A281135AC8>,'train_size': <absl.flags._flag.Flag object at 0x000002A2FE974400>,'visualize': <absl.flags._flag.BooleanFlag object at 0x000002A281135BE0>} 2018-10-06 11:32:10.690386: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 data_MNIST\mnist --------- Variables: name (type shape) [size] --------- generator/g_h0_lin/Matrix:0 (float32_ref 110x1024) [112640, bytes: 450560] generator/g_h0_lin/bias:0 (float32_ref 1024) [1024, bytes: 4096] generator/g_bn0/beta:0 (float32_ref 1024) [1024, bytes: 4096] generator/g_bn0/gamma:0 (float32_ref 1024) [1024, bytes: 4096] generator/g_h1_lin/Matrix:0 (float32_ref 1034x6272) [6485248, bytes: 25940992] generator/g_h1_lin/bias:0 (float32_ref 6272) [6272, bytes: 25088] generator/g_bn1/beta:0 (float32_ref 6272) [6272, bytes: 25088] generator/g_bn1/gamma:0 (float32_ref 6272) [6272, bytes: 25088] generator/g_h2/w:0 (float32_ref 5x5x128x138) [441600, bytes: 1766400] generator/g_h2/biases:0 (float32_ref 128) [128, bytes: 512] generator/g_bn2/beta:0 (float32_ref 128) [128, bytes: 512] generator/g_bn2/gamma:0 (float32_ref 128) [128, bytes: 512] generator/g_h3/w:0 (float32_ref 5x5x1x138) [3450, bytes: 13800] generator/g_h3/biases:0 (float32_ref 1) [1, bytes: 4] discriminator/d_h0_conv/w:0 (float32_ref 5x5x11x11) [3025, bytes: 12100] discriminator/d_h0_conv/biases:0 (float32_ref 11) [11, bytes: 44] discriminator/d_h1_conv/w:0 (float32_ref 5x5x21x74) [38850, bytes: 155400] discriminator/d_h1_conv/biases:0 (float32_ref 74) [74, bytes: 296] discriminator/d_bn1/beta:0 (float32_ref 74) [74, bytes: 296] discriminator/d_bn1/gamma:0 (float32_ref 74) [74, bytes: 296] discriminator/d_h2_lin/Matrix:0 (float32_ref 3636x1024) [3723264, bytes: 14893056] discriminator/d_h2_lin/bias:0 (float32_ref 1024) [1024, bytes: 4096] discriminator/d_bn2/beta:0 (float32_ref 1024) [1024, bytes: 4096] discriminator/d_bn2/gamma:0 (float32_ref 1024) [1024, bytes: 4096] discriminator/d_h3_lin/Matrix:0 (float32_ref 1034x1) [1034, bytes: 4136] discriminator/d_h3_lin/bias:0 (float32_ref 1) [1, bytes: 4] Total size of variables: 10834690 Total bytes of variables: 43338760[*] Reading checkpoints...[*] Failed to find a checkpoint[!] Load failed... Epoch: [ 0] [ 0/1093] time: 3.3617, d_loss: 1.79891801, g_loss: 0.73078763 Epoch: [ 0] [ 1/1093] time: 6.4123, d_loss: 1.46442509, g_loss: 0.61579478 Epoch: [ 0] [ 2/1093] time: 8.7562, d_loss: 1.49022853, g_loss: 0.67894053 Epoch: [ 0] [ 3/1093] time: 10.9214, d_loss: 1.40174472, g_loss: 0.66220653 Epoch: [ 0] [ 4/1093] time: 13.3050, d_loss: 1.40663481, g_loss: 0.69936526 Epoch: [ 0] [ 5/1093] time: 15.5709, d_loss: 1.38957083, g_loss: 0.68421012 Epoch: [ 0] [ 6/1093] time: 17.8600, d_loss: 1.39213061, g_loss: 0.68934584 Epoch: [ 0] [ 7/1093] time: 20.4708, d_loss: 1.39794362, g_loss: 0.69806755 Epoch: [ 0] [ 8/1093] time: 23.0654, d_loss: 1.43503237, g_loss: 0.70846951 Epoch: [ 0] [ 9/1093] time: 25.5358, d_loss: 1.39276147, g_loss: 0.70669782 Epoch: [ 0] [ 10/1093] time: 28.2617, d_loss: 1.42136300, g_loss: 0.70364445 Epoch: [ 0] [ 11/1093] time: 30.8038, d_loss: 1.40051103, g_loss: 0.70014894 Epoch: [ 0] [ 12/1093] time: 33.3130, d_loss: 1.37765169, g_loss: 0.70824486 Epoch: [ 0] [ 13/1093] time: 35.6096, d_loss: 1.38219857, g_loss: 0.69451976 Epoch: [ 0] [ 14/1093] time: 37.8537, d_loss: 1.36866033, g_loss: 0.70824432 Epoch: [ 0] [ 15/1093] time: 40.1426, d_loss: 1.36621869, g_loss: 0.69405836 Epoch: [ 0] [ 16/1093] time: 42.7074, d_loss: 1.37535453, g_loss: 0.69518888 Epoch: [ 0] [ 17/1093] time: 44.8565, d_loss: 1.36989605, g_loss: 0.69930756 Epoch: [ 0] [ 18/1093] time: 46.7869, d_loss: 1.36563087, g_loss: 0.69781649 Epoch: [ 0] [ 19/1093] time: 48.7288, d_loss: 1.36397326, g_loss: 0.70866680 Epoch: [ 0] [ 20/1093] time: 51.0654, d_loss: 1.38101411, g_loss: 0.69544500 Epoch: [ 0] [ 21/1093] time: 53.5399, d_loss: 1.46281934, g_loss: 0.70643008 Epoch: [ 0] [ 22/1093] time: 56.5684, d_loss: 1.43966162, g_loss: 0.71961737 Epoch: [ 0] [ 23/1093] time: 59.5954, d_loss: 1.42399430, g_loss: 0.72861439 Epoch: [ 0] [ 24/1093] time: 62.9032, d_loss: 1.41276562, g_loss: 0.70471978 Epoch: [ 0] [ 25/1093] time: 65.7187, d_loss: 1.48300290, g_loss: 0.71538234 Epoch: [ 0] [ 26/1093] time: 68.6204, d_loss: 1.39843416, g_loss: 0.68771482 Epoch: [ 0] [ 27/1093] time: 70.8153, d_loss: 1.42166626, g_loss: 0.69409549 Epoch: [ 0] [ 28/1093] time: 73.5776, d_loss: 1.39594829, g_loss: 0.68035471 Epoch: [ 0] [ 29/1093] time: 76.6749, d_loss: 1.39489424, g_loss: 0.69306409 Epoch: [ 0] [ 30/1093] time: 79.8282, d_loss: 1.41070235, g_loss: 0.68208236 Epoch: [ 0] [ 31/1093] time: 83.5562, d_loss: 1.39976072, g_loss: 0.69344074 Epoch: [ 0] [ 32/1093] time: 86.5431, d_loss: 1.39875138, g_loss: 0.69864786 Epoch: [ 0] [ 33/1093] time: 89.7386, d_loss: 1.39117682, g_loss: 0.68384939 Epoch: [ 0] [ 34/1093] time: 92.1129, d_loss: 1.39306462, g_loss: 0.68603516 Epoch: [ 0] [ 35/1093] time: 94.6717, d_loss: 1.39766645, g_loss: 0.67713618 Epoch: [ 0] [ 36/1093] time: 97.4150, d_loss: 1.39619994, g_loss: 0.68300879 Epoch: [ 0] [ 37/1093] time: 99.9408, d_loss: 1.39534819, g_loss: 0.69076747 Epoch: [ 0] [ 38/1093] time: 103.1213, d_loss: 1.39753985, g_loss: 0.68903100 Epoch: [ 0] [ 39/1093] time: 105.8520, d_loss: 1.41161013, g_loss: 0.69302136 Epoch: [ 0] [ 40/1093] time: 108.9503, d_loss: 1.38997078, g_loss: 0.68370312 Epoch: [ 0] [ 41/1093] time: 112.2070, d_loss: 1.39786303, g_loss: 0.69124269 Epoch: [ 0] [ 42/1093] time: 115.2431, d_loss: 1.38943410, g_loss: 0.69021893 Epoch: [ 0] [ 43/1093] time: 118.6511, d_loss: 1.38621378, g_loss: 0.68407494 Epoch: [ 0] [ 44/1093] time: 122.0462, d_loss: 1.39240563, g_loss: 0.69688046 Epoch: [ 0] [ 45/1093] time: 125.3139, d_loss: 1.39452100, g_loss: 0.69252259 Epoch: [ 0] [ 46/1093] time: 129.0117, d_loss: 1.39167857, g_loss: 0.68246353 Epoch: [ 0] [ 47/1093] time: 132.8489, d_loss: 1.39049268, g_loss: 0.69009811 Epoch: [ 0] [ 48/1093] time: 136.4826, d_loss: 1.39105415, g_loss: 0.69570535 Epoch: [ 0] [ 49/1093] time: 139.8832, d_loss: 1.38744533, g_loss: 0.68307704 Epoch: [ 0] [ 50/1093] time: 142.6343, d_loss: 1.39128542, g_loss: 0.68657452 Epoch: [ 0] [ 51/1093] time: 145.0365, d_loss: 1.39720774, g_loss: 0.68289292 Epoch: [ 0] [ 52/1093] time: 148.8226, d_loss: 1.40998244, g_loss: 0.69946194 Epoch: [ 0] [ 53/1093] time: 151.4981, d_loss: 1.42358077, g_loss: 0.69425476 Epoch: [ 0] [ 54/1093] time: 154.4366, d_loss: 1.40655017, g_loss: 0.69315112 Epoch: [ 0] [ 55/1093] time: 157.9840, d_loss: 1.39314961, g_loss: 0.67903620 Epoch: [ 0] [ 56/1093] time: 160.5293, d_loss: 1.39538550, g_loss: 0.68701828 Epoch: [ 0] [ 57/1093] time: 162.8455, d_loss: 1.40030372, g_loss: 0.68119174 Epoch: [ 0] [ 58/1093] time: 165.5109, d_loss: 1.39839721, g_loss: 0.68374062 Epoch: [ 0] [ 59/1093] time: 168.1250, d_loss: 1.40220833, g_loss: 0.67849696 Epoch: [ 0] [ 60/1093] time: 170.4443, d_loss: 1.40346980, g_loss: 0.68534362 Epoch: [ 0] [ 61/1093] time: 172.5757, d_loss: 1.40919614, g_loss: 0.68264174 Epoch: [ 0] [ 62/1093] time: 175.3375, d_loss: 1.41680074, g_loss: 0.69107366 Epoch: [ 0] [ 63/1093] time: 178.1931, d_loss: 1.42677331, g_loss: 0.68684256 Epoch: [ 0] [ 64/1093] time: 180.9363, d_loss: 1.41873085, g_loss: 0.68174267 Epoch: [ 0] [ 65/1093] time: 183.4142, d_loss: 1.41352820, g_loss: 0.69168335 Epoch: [ 0] [ 66/1093] time: 186.2004, d_loss: 1.40492952, g_loss: 0.68485790 Epoch: [ 0] [ 67/1093] time: 188.9013, d_loss: 1.41416049, g_loss: 0.69247150 Epoch: [ 0] [ 68/1093] time: 191.3907, d_loss: 1.44085050, g_loss: 0.70080090 Epoch: [ 0] [ 69/1093] time: 193.6596, d_loss: 1.42936659, g_loss: 0.70780182 Epoch: [ 0] [ 70/1093] time: 196.2392, d_loss: 1.39855242, g_loss: 0.68066621 Epoch: [ 0] [ 71/1093] time: 198.6732, d_loss: 1.39962685, g_loss: 0.68119228 Epoch: [ 0] [ 72/1093] time: 201.1359, d_loss: 1.39792156, g_loss: 0.68046838 Epoch: [ 0] [ 73/1093] time: 203.9913, d_loss: 1.40156364, g_loss: 0.68185544 Epoch: [ 0] [ 74/1093] time: 206.5057, d_loss: 1.40137339, g_loss: 0.68439347 Epoch: [ 0] [ 75/1093] time: 208.9730, d_loss: 1.39628625, g_loss: 0.68880224 Epoch: [ 0] [ 76/1093] time: 212.1802, d_loss: 1.39695120, g_loss: 0.69053137 Epoch: [ 0] [ 77/1093] time: 215.1069, d_loss: 1.39827728, g_loss: 0.67404974 Epoch: [ 0] [ 78/1093] time: 217.8231, d_loss: 1.39441288, g_loss: 0.68811285 Epoch: [ 0] [ 79/1093] time: 220.8017, d_loss: 1.39862061, g_loss: 0.68243313 Epoch: [ 0] [ 80/1093] time: 223.6711, d_loss: 1.39560962, g_loss: 0.68420863 Epoch: [ 0] [ 81/1093] time: 226.1243, d_loss: 1.39474165, g_loss: 0.68446684 Epoch: [ 0] [ 82/1093] time: 228.9125, d_loss: 1.39735079, g_loss: 0.68914992 Epoch: [ 0] [ 83/1093] time: 231.7087, d_loss: 1.40495729, g_loss: 0.67565703 Epoch: [ 0] [ 84/1093] time: 234.3499, d_loss: 1.40376186, g_loss: 0.68402076 Epoch: [ 0] [ 85/1093] time: 236.8927, d_loss: 1.39633703, g_loss: 0.67996454 Epoch: [ 0] [ 86/1093] time: 239.8556, d_loss: 1.40431571, g_loss: 0.68185967 Epoch: [ 0] [ 87/1093] time: 242.7527, d_loss: 1.40456629, g_loss: 0.68880403 Epoch: [ 0] [ 88/1093] time: 245.2765, d_loss: 1.39363539, g_loss: 0.68647277 Epoch: [ 0] [ 89/1093] time: 247.9097, d_loss: 1.39768720, g_loss: 0.68281728 Epoch: [ 0] [ 90/1093] time: 250.6797, d_loss: 1.40258384, g_loss: 0.69015211 Epoch: [ 0] [ 91/1093] time: 252.9605, d_loss: 1.41010988, g_loss: 0.69163489 Epoch: [ 0] [ 92/1093] time: 255.8331, d_loss: 1.39705300, g_loss: 0.67692769 Epoch: [ 0] [ 93/1093] time: 258.7976, d_loss: 1.41552734, g_loss: 0.69169050 Epoch: [ 0] [ 94/1093] time: 262.1104, d_loss: 1.39865696, g_loss: 0.68793559 Epoch: [ 0] [ 95/1093] time: 265.0370, d_loss: 1.40191650, g_loss: 0.68027002 Epoch: [ 0] [ 96/1093] time: 267.7568, d_loss: 1.40628874, g_loss: 0.67845261 Epoch: [ 0] [ 97/1093] time: 270.7154, d_loss: 1.40095508, g_loss: 0.68664324 Epoch: [ 0] [ 98/1093] time: 273.6299, d_loss: 1.41269326, g_loss: 0.68330830 Epoch: [ 0] [ 99/1093] time: 276.4041, d_loss: 1.41343331, g_loss: 0.69674391 [Sample] d_loss: 1.39404178, g_loss: 0.71861243 Epoch: [ 0] [ 100/1093] time: 279.9370, d_loss: 1.39926529, g_loss: 0.69326425 Epoch: [ 0] [ 101/1093] time: 282.8589, d_loss: 1.39894390, g_loss: 0.68361241 Epoch: [ 0] [ 102/1093] time: 285.4811, d_loss: 1.39818084, g_loss: 0.69090337 Epoch: [ 0] [ 103/1093] time: 287.6454, d_loss: 1.39627695, g_loss: 0.67909706 Epoch: [ 0] [ 104/1093] time: 290.3276, d_loss: 1.39514160, g_loss: 0.68727589 Epoch: [ 0] [ 105/1093] time: 293.5694, d_loss: 1.40148556, g_loss: 0.68616998 Epoch: [ 0] [ 106/1093] time: 296.7065, d_loss: 1.39823532, g_loss: 0.68184149 Epoch: [ 0] [ 107/1093] time: 299.5040, d_loss: 1.40077090, g_loss: 0.67544007 Epoch: [ 0] [ 108/1093] time: 302.5080, d_loss: 1.40159750, g_loss: 0.68739390 Epoch: [ 0] [ 109/1093] time: 305.3266, d_loss: 1.40064311, g_loss: 0.68674183 Epoch: [ 0] [ 110/1093] time: 308.2463, d_loss: 1.40190828, g_loss: 0.68489563……Epoch: [ 0] [ 190/1093] time: 535.9742, d_loss: 1.39696872, g_loss: 0.67972469 Epoch: [ 0] [ 191/1093] time: 538.4506, d_loss: 1.39499533, g_loss: 0.68089843 Epoch: [ 0] [ 192/1093] time: 541.1816, d_loss: 1.39483309, g_loss: 0.68199342 Epoch: [ 0] [ 193/1093] time: 544.6827, d_loss: 1.39154720, g_loss: 0.69034952 Epoch: [ 0] [ 194/1093] time: 548.6390, d_loss: 1.38941956, g_loss: 0.68652773 Epoch: [ 0] [ 195/1093] time: 551.9678, d_loss: 1.39027929, g_loss: 0.69264108 Epoch: [ 0] [ 196/1093] time: 555.3258, d_loss: 1.39162266, g_loss: 0.68833613 Epoch: [ 0] [ 197/1093] time: 558.5404, d_loss: 1.40050042, g_loss: 0.68856359 Epoch: [ 0] [ 198/1093] time: 561.3181, d_loss: 1.39854860, g_loss: 0.69332385 Epoch: [ 0] [ 199/1093] time: 563.8952, d_loss: 1.40790129, g_loss: 0.69219285 [Sample] d_loss: 1.39614487, g_loss: 0.70220172 Epoch: [ 0] [ 200/1093] time: 566.5791, d_loss: 1.39575028, g_loss: 0.68371403 Epoch: [ 0] [ 201/1093] time: 568.9093, d_loss: 1.39769495, g_loss: 0.68171024 Epoch: [ 0] [ 202/1093] time: 571.4728, d_loss: 1.40282321, g_loss: 0.67665672 Epoch: [ 0] [ 203/1093] time: 574.0684, d_loss: 1.40040171, g_loss: 0.68347836 Epoch: [ 0] [ 204/1093] time: 576.6086, d_loss: 1.40370631, g_loss: 0.67588425 Epoch: [ 0] [ 205/1093] time: 579.1860, d_loss: 1.40058494, g_loss: 0.67948377 Epoch: [ 0] [ 206/1093] time: 581.7698, d_loss: 1.40094650, g_loss: 0.68511415 Epoch: [ 0] [ 207/1093] time: 584.3541, d_loss: 1.39703560, g_loss: 0.68563807 Epoch: [ 0] [ 208/1093] time: 586.9515, d_loss: 1.39535570, g_loss: 0.69189703 Epoch: [ 0] [ 209/1093] time: 589.5623, d_loss: 1.39087117, g_loss: 0.68965638 Epoch: [ 0] [ 210/1093] time: 592.1490, d_loss: 1.39308906, g_loss: 0.68321383……Epoch: [ 0] [ 889/1093] time: 2314.8393, d_loss: 1.39859378, g_loss: 0.67322266 Epoch: [ 0] [ 890/1093] time: 2316.9278, d_loss: 1.39070845, g_loss: 0.68732977 Epoch: [ 0] [ 891/1093] time: 2319.3591, d_loss: 1.39387286, g_loss: 0.67873466 Epoch: [ 0] [ 892/1093] time: 2321.4178, d_loss: 1.39172828, g_loss: 0.68356216 Epoch: [ 0] [ 893/1093] time: 2323.4089, d_loss: 1.39842272, g_loss: 0.67815489 Epoch: [ 0] [ 894/1093] time: 2325.6301, d_loss: 1.39376366, g_loss: 0.68304271 Epoch: [ 0] [ 895/1093] time: 2328.0387, d_loss: 1.39139628, g_loss: 0.67735171 Epoch: [ 0] [ 896/1093] time: 2330.0398, d_loss: 1.39796066, g_loss: 0.67579186 Epoch: [ 0] [ 897/1093] time: 2332.2183, d_loss: 1.39888477, g_loss: 0.66883886 Epoch: [ 0] [ 898/1093] time: 2334.6396, d_loss: 1.39262605, g_loss: 0.67790604 Epoch: [ 0] [ 899/1093] time: 2336.6380, d_loss: 1.38774049, g_loss: 0.68282270 [Sample] d_loss: 1.38685536, g_loss: 0.70143592 Epoch: [ 0] [ 900/1093] time: 2339.1794, d_loss: 1.39559400, g_loss: 0.67823637 Epoch: [ 0] [ 901/1093] time: 2341.5979, d_loss: 1.39618373, g_loss: 0.67359304 Epoch: [ 0] [ 902/1093] time: 2343.6090, d_loss: 1.40060043, g_loss: 0.68315041 Epoch: [ 0] [ 903/1093] time: 2345.6101, d_loss: 1.38607645, g_loss: 0.68459594 Epoch: [ 0] [ 904/1093] time: 2347.6186, d_loss: 1.38612366, g_loss: 0.68465877 Epoch: [ 0] [ 905/1093] time: 2349.8598, d_loss: 1.38972747, g_loss: 0.68110597 Epoch: [ 0] [ 906/1093] time: 2352.2383, d_loss: 1.40021336, g_loss: 0.67477131 Epoch: [ 0] [ 907/1093] time: 2354.2594, d_loss: 1.38780701, g_loss: 0.68614316 Epoch: [ 0] [ 908/1093] time: 2356.4380, d_loss: 1.39729989, g_loss: 0.68168002 Epoch: [ 0] [ 909/1093] time: 2358.8492, d_loss: 1.39604807, g_loss: 0.68169260 Epoch: [ 0] [ 910/1093] time: 2360.8703, d_loss: 1.39347506, g_loss: 0.67698503……Epoch: [ 0] [ 990/1093] time: 2534.4882, d_loss: 1.38051999, g_loss: 0.68829250 Epoch: [ 0] [ 991/1093] time: 2536.8594, d_loss: 1.38707495, g_loss: 0.69181627 Epoch: [ 0] [ 992/1093] time: 2538.9105, d_loss: 1.39524150, g_loss: 0.68155080 Epoch: [ 0] [ 993/1093] time: 2540.9216, d_loss: 1.39088154, g_loss: 0.68005645 Epoch: [ 0] [ 994/1093] time: 2543.1603, d_loss: 1.38700223, g_loss: 0.68155348 Epoch: [ 0] [ 995/1093] time: 2545.5215, d_loss: 1.40298247, g_loss: 0.66744435 Epoch: [ 0] [ 996/1093] time: 2547.5300, d_loss: 1.40880179, g_loss: 0.66607797 Epoch: [ 0] [ 997/1093] time: 2549.5310, d_loss: 1.39295077, g_loss: 0.67571455 Epoch: [ 0] [ 998/1093] time: 2551.8797, d_loss: 1.39118791, g_loss: 0.68550998 Epoch: [ 0] [ 999/1093] time: 2554.1409, d_loss: 1.38995099, g_loss: 0.68077219 [Sample] d_loss: 1.39188242, g_loss: 0.69870007 Epoch: [ 0] [1000/1093] time: 2556.5095, d_loss: 1.38937902, g_loss: 0.68420708 Epoch: [ 0] [1001/1093] time: 2559.4411, d_loss: 1.38841224, g_loss: 0.67964196 Epoch: [ 0] [1002/1093] time: 2561.3995, d_loss: 1.39025033, g_loss: 0.68857718 Epoch: [ 0] [1003/1093] time: 2563.4106, d_loss: 1.38774192, g_loss: 0.68713319 Epoch: [ 0] [1004/1093] time: 2565.7818, d_loss: 1.38517952, g_loss: 0.69962525 Epoch: [ 0] [1005/1093] time: 2568.0208, d_loss: 1.39758313, g_loss: 0.68758988 Epoch: [ 0] [1006/1093] time: 2570.0219, d_loss: 1.39658952, g_loss: 0.69050717 Epoch: [ 0] [1007/1093] time: 2572.0104, d_loss: 1.39825773, g_loss: 0.67399806 Epoch: [ 0] [1008/1093] time: 2574.2516, d_loss: 1.39735007, g_loss: 0.68345094 Epoch: [ 0] [1009/1093] time: 2576.4203, d_loss: 1.39032114, g_loss: 0.67591566 Epoch: [ 0] [1010/1093] time: 2578.4213, d_loss: 1.39701056, g_loss: 0.67272741……Epoch: [ 0] [1080/1093] time: 2729.6181, d_loss: 1.38660502, g_loss: 0.67934191 Epoch: [ 0] [1081/1093] time: 2731.6592, d_loss: 1.39765692, g_loss: 0.67786539 Epoch: [ 0] [1082/1093] time: 2733.8804, d_loss: 1.38977814, g_loss: 0.67776024 Epoch: [ 0] [1083/1093] time: 2736.3117, d_loss: 1.39641953, g_loss: 0.67741239 Epoch: [ 0] [1084/1093] time: 2738.3027, d_loss: 1.39849305, g_loss: 0.66936278 Epoch: [ 0] [1085/1093] time: 2740.2938, d_loss: 1.39174080, g_loss: 0.67819309 Epoch: [ 0] [1086/1093] time: 2742.3049, d_loss: 1.39430928, g_loss: 0.67690992 Epoch: [ 0] [1087/1093] time: 2744.5361, d_loss: 1.38831007, g_loss: 0.67887449 Epoch: [ 0] [1088/1093] time: 2746.8773, d_loss: 1.38743389, g_loss: 0.67928505 Epoch: [ 0] [1089/1093] time: 2748.8884, d_loss: 1.40019858, g_loss: 0.66898370 Epoch: [ 0] [1090/1093] time: 2750.8895, d_loss: 1.38798690, g_loss: 0.67247820 Epoch: [ 0] [1091/1093] time: 2753.2107, d_loss: 1.39350247, g_loss: 0.67379618 Epoch: [ 0] [1092/1093] time: 2755.4819, d_loss: 1.39420724, g_loss: 0.67820472 Epoch: [ 1] [ 0/1093] time: 2757.4730, d_loss: 1.39512217, g_loss: 0.67989075 Epoch: [ 1] [ 1/1093] time: 2759.4640, d_loss: 1.40053773, g_loss: 0.67416751 Epoch: [ 1] [ 2/1093] time: 2761.6852, d_loss: 1.39061642, g_loss: 0.68139255 Epoch: [ 1] [ 3/1093] time: 2764.0665, d_loss: 1.39106679, g_loss: 0.68662095 Epoch: [ 1] [ 4/1093] time: 2766.0576, d_loss: 1.39566541, g_loss: 0.68151307 Epoch: [ 1] [ 5/1093] time: 2768.2087, d_loss: 1.39311624, g_loss: 0.67771947 Epoch: [ 1] [ 6/1093] time: 2770.6400, d_loss: 1.38920772, g_loss: 0.68134636 [Sample] d_loss: 1.37139106, g_loss: 0.70543092 Epoch: [ 1] [ 7/1093] time: 2773.0913, d_loss: 1.39394724, g_loss: 0.68192399 Epoch: [ 1] [ 8/1093] time: 2775.0624, d_loss: 1.38887393, g_loss: 0.68720746 Epoch: [ 1] [ 9/1093] time: 2777.4136, d_loss: 1.38760364, g_loss: 0.67848784 Epoch: [ 1] [ 10/1093] time: 2779.6648, d_loss: 1.39168441, g_loss: 0.68020177……Epoch: [ 1] [ 100/1093] time: 2974.2737, d_loss: 1.39194596, g_loss: 0.67957735 Epoch: [ 1] [ 101/1093] time: 2976.2621, d_loss: 1.38731110, g_loss: 0.68144447 Epoch: [ 1] [ 102/1093] time: 2978.5533, d_loss: 1.39706790, g_loss: 0.67597866 Epoch: [ 1] [ 103/1093] time: 2980.8618, d_loss: 1.39810014, g_loss: 0.66924357 Epoch: [ 1] [ 104/1093] time: 2982.8729, d_loss: 1.38598788, g_loss: 0.67554963 Epoch: [ 1] [ 105/1093] time: 2984.8740, d_loss: 1.39240956, g_loss: 0.67254972 Epoch: [ 1] [ 106/1093] time: 2986.9812, d_loss: 1.39499450, g_loss: 0.67016041 [Sample] d_loss: 1.38732648, g_loss: 0.69703865 Epoch: [ 1] [ 107/1093] time: 2990.3271, d_loss: 1.40116143, g_loss: 0.66683507 Epoch: [ 1] [ 108/1093] time: 2992.7527, d_loss: 1.39175665, g_loss: 0.68012154 Epoch: [ 1] [ 109/1093] time: 2995.0739, d_loss: 1.39712453, g_loss: 0.67622381 Epoch: [ 1] [ 110/1093] time: 2997.6827, d_loss: 1.39206731, g_loss: 0.68065107……Epoch: [ 1] [ 200/1093] time: 3202.6353, d_loss: 1.38041210, g_loss: 0.69372916 Epoch: [ 1] [ 201/1093] time: 3204.9065, d_loss: 1.37933481, g_loss: 0.68821752 Epoch: [ 1] [ 202/1093] time: 3206.9749, d_loss: 1.38175058, g_loss: 0.68613887 Epoch: [ 1] [ 203/1093] time: 3209.2561, d_loss: 1.39573455, g_loss: 0.67698872 Epoch: [ 1] [ 204/1093] time: 3211.8648, d_loss: 1.39549482, g_loss: 0.67765439 Epoch: [ 1] [ 205/1093] time: 3213.9159, d_loss: 1.39421272, g_loss: 0.67087078 Epoch: [ 1] [ 206/1093] time: 3215.9443, d_loss: 1.38698030, g_loss: 0.68094480 [Sample] d_loss: 1.38046920, g_loss: 0.69818783 Epoch: [ 1] [ 207/1093] time: 3218.7558, d_loss: 1.38357759, g_loss: 0.68195403 Epoch: [ 1] [ 208/1093] time: 3220.9143, d_loss: 1.38065100, g_loss: 0.68955791 Epoch: [ 1] [ 209/1093] time: 3222.9153, d_loss: 1.39242363, g_loss: 0.67996120 Epoch: [ 1] [ 210/1093] time: 3225.2766, d_loss: 1.39360881, g_loss: 0.67260170 Epoch: [ 1] [ 211/1093] time: 3227.5352, d_loss: 1.38966787, g_loss: 0.68173468……Epoch: [ 1] [ 300/1093] time: 3423.0672, d_loss: 1.38942528, g_loss: 0.68868965 Epoch: [ 1] [ 301/1093] time: 3425.2884, d_loss: 1.39816928, g_loss: 0.67360890 Epoch: [ 1] [ 302/1093] time: 3427.7397, d_loss: 1.39097309, g_loss: 0.67551196 Epoch: [ 1] [ 303/1093] time: 3430.0009, d_loss: 1.38649738, g_loss: 0.68443769 Epoch: [ 1] [ 304/1093] time: 3432.1621, d_loss: 1.37904358, g_loss: 0.68674159 Epoch: [ 1] [ 305/1093] time: 3434.4833, d_loss: 1.38382614, g_loss: 0.68362451 Epoch: [ 1] [ 306/1093] time: 3436.7545, d_loss: 1.39781308, g_loss: 0.67674035 [Sample] d_loss: 1.38065791, g_loss: 0.70156980 Epoch: [ 1] [ 307/1093] time: 3439.1758, d_loss: 1.38384795, g_loss: 0.68276435 Epoch: [ 1] [ 308/1093] time: 3441.1669, d_loss: 1.38682365, g_loss: 0.67517352 Epoch: [ 1] [ 309/1093] time: 3443.1579, d_loss: 1.39301312, g_loss: 0.67435873 Epoch: [ 1] [ 310/1093] time: 3445.3991, d_loss: 1.38605368, g_loss: 0.67695403 Epoch: [ 1] [ 311/1093] time: 3447.7679, d_loss: 1.39315736, g_loss: 0.67680848 Epoch: [ 1] [ 312/1093] time: 3449.7789, d_loss: 1.39378428, g_loss: 0.67591465 Epoch: [ 1] [ 313/1093] time: 3451.9869, d_loss: 1.38802958, g_loss: 0.68172151 Epoch: [ 1] [ 314/1093] time: 3454.4282, d_loss: 1.39341950, g_loss: 0.67019951 Epoch: [ 1] [ 315/1093] time: 3456.4468, d_loss: 1.38873637, g_loss: 0.67581522……6023 Epoch: [ 1] [ 500/1093] time: 3862.5952, d_loss: 1.37781966, g_loss: 0.68809289 Epoch: [ 1] [ 501/1093] time: 3864.8864, d_loss: 1.39578390, g_loss: 0.67059171 Epoch: [ 1] [ 502/1093] time: 3866.8775, d_loss: 1.37757528, g_loss: 0.69209492 Epoch: [ 1] [ 503/1093] time: 3868.8760, d_loss: 1.39398217, g_loss: 0.67311525 Epoch: [ 1] [ 504/1093] time: 3871.1472, d_loss: 1.39142919, g_loss: 0.67788839 Epoch: [ 1] [ 505/1093] time: 3873.6359, d_loss: 1.39205325, g_loss: 0.67508668 Epoch: [ 1] [ 506/1093] time: 3875.6370, d_loss: 1.39611387, g_loss: 0.67204535 [Sample] d_loss: 1.37556362, g_loss: 0.69815457 Epoch: [ 1] [ 507/1093] time: 3877.9957, d_loss: 1.39341450, g_loss: 0.67685163 Epoch: [ 1] [ 508/1093] time: 3880.4070, d_loss: 1.39084995, g_loss: 0.67754412 Epoch: [ 1] [ 509/1093] time: 3882.5755, d_loss: 1.40043855, g_loss: 0.66707742 Epoch: [ 1] [ 510/1093] time: 3884.5566, d_loss: 1.38664675, g_loss: 0.67468828 Epoch: [ 1] [ 511/1093] time: 3886.9479, d_loss: 1.39450240, g_loss: 0.66686535 Epoch: [ 1] [ 512/1093] time: 3889.1964, d_loss: 1.38870108, g_loss: 0.67924225 Epoch: [ 1] [ 513/1093] time: 3891.3575, d_loss: 1.39083517, g_loss: 0.68065596 Epoch: [ 1] [ 514/1093] time: 3893.6961, d_loss: 1.38016534, g_loss: 0.68610257 Epoch: [ 1] [ 515/1093] time: 3895.9473, d_loss: 1.38265920, g_loss: 0.68078399 Epoch: [ 1] [ 516/1093] time: 3897.9557, d_loss: 1.39135432, g_loss: 0.67949045 Epoch: [ 1] [ 517/1093] time: 3899.9467, d_loss: 1.38820958, g_loss: 0.67711371 Epoch: [ 1] [ 518/1093] time: 3902.2179, d_loss: 1.39466333, g_loss: 0.68058121……Epoch: [ 1] [1077/1093] time: 5125.0453, d_loss: 1.37844777, g_loss: 0.68651271 Epoch: [ 1] [1078/1093] time: 5127.2238, d_loss: 1.38850927, g_loss: 0.68094480 Epoch: [ 1] [1079/1093] time: 5129.5851, d_loss: 1.37683725, g_loss: 0.68991780 Epoch: [ 1] [1080/1093] time: 5131.8035, d_loss: 1.39222741, g_loss: 0.66837865 Epoch: [ 1] [1081/1093] time: 5133.8046, d_loss: 1.38264728, g_loss: 0.67701787 Epoch: [ 1] [1082/1093] time: 5135.7957, d_loss: 1.39265454, g_loss: 0.67443299 Epoch: [ 1] [1083/1093] time: 5138.0342, d_loss: 1.39083576, g_loss: 0.68285644 Epoch: [ 1] [1084/1093] time: 5140.3855, d_loss: 1.39100790, g_loss: 0.67561376 Epoch: [ 1] [1085/1093] time: 5142.3541, d_loss: 1.38509417, g_loss: 0.67930484 Epoch: [ 1] [1086/1093] time: 5144.3652, d_loss: 1.38570714, g_loss: 0.67512459 Epoch: [ 1] [1087/1093] time: 5146.8339, d_loss: 1.37933540, g_loss: 0.67861497 Epoch: [ 1] [1088/1093] time: 5149.1052, d_loss: 1.39024305, g_loss: 0.67276442 Epoch: [ 1] [1089/1093] time: 5151.1162, d_loss: 1.37893343, g_loss: 0.68706435 Epoch: [ 1] [1090/1093] time: 5153.1038, d_loss: 1.38589072, g_loss: 0.67717320 Epoch: [ 1] [1091/1093] time: 5155.4151, d_loss: 1.38973820, g_loss: 0.67712557 Epoch: [ 1] [1092/1093] time: 5157.8440, d_loss: 1.38368809, g_loss: 0.67974091 Epoch: [ 2] [ 0/1093] time: 5159.8150, d_loss: 1.38032269, g_loss: 0.67759383 Epoch: [ 2] [ 1/1093] time: 5162.0339, d_loss: 1.37580657, g_loss: 0.67377681 Epoch: [ 2] [ 2/1093] time: 5164.4152, d_loss: 1.37951207, g_loss: 0.67664278 Epoch: [ 2] [ 3/1093] time: 5166.4536, d_loss: 1.39463484, g_loss: 0.67749333 Epoch: [ 2] [ 4/1093] time: 5168.4547, d_loss: 1.38351607, g_loss: 0.67323297 Epoch: [ 2] [ 5/1093] time: 5170.9160, d_loss: 1.39039516, g_loss: 0.66864181 Epoch: [ 2] [ 6/1093] time: 5173.2147, d_loss: 1.39086890, g_loss: 0.68247157 Epoch: [ 2] [ 7/1093] time: 5175.3358, d_loss: 1.40376759, g_loss: 0.67604411 Epoch: [ 2] [ 8/1093] time: 5177.3142, d_loss: 1.39150715, g_loss: 0.67578733 Epoch: [ 2] [ 9/1093] time: 5179.6255, d_loss: 1.37265015, g_loss: 0.68143678 Epoch: [ 2] [ 10/1093] time: 5182.0736, d_loss: 1.39045727, g_loss: 0.68101263 Epoch: [ 2] [ 11/1093] time: 5184.2047, d_loss: 1.39368677, g_loss: 0.67329615 Epoch: [ 2] [ 12/1093] time: 5186.1958, d_loss: 1.39578104, g_loss: 0.67907357 Epoch: [ 2] [ 13/1093] time: 5188.6146, d_loss: 1.38878369, g_loss: 0.67477858 [Sample] d_loss: 1.36533904, g_loss: 0.70099354 Epoch: [ 2] [ 14/1093] time: 5191.1659, d_loss: 1.39303446, g_loss: 0.68040711 Epoch: [ 2] [ 15/1093] time: 5193.3644, d_loss: 1.38526893, g_loss: 0.67990983 Epoch: [ 2] [ 16/1093] time: 5195.7657, d_loss: 1.39147758, g_loss: 0.68214095 Epoch: [ 2] [ 17/1093] time: 5197.8844, d_loss: 1.36999416, g_loss: 0.69020271……Epoch: [ 2] [ 910/1093] time: 7159.6691, d_loss: 1.38843203, g_loss: 0.67605901 Epoch: [ 2] [ 911/1093] time: 7161.7976, d_loss: 1.40062439, g_loss: 0.66792578 Epoch: [ 2] [ 912/1093] time: 7164.0088, d_loss: 1.38792086, g_loss: 0.67560351 Epoch: [ 2] [ 913/1093] time: 7166.4575, d_loss: 1.38766861, g_loss: 0.67637527 [Sample] d_loss: 1.38370931, g_loss: 0.69774455 Epoch: [ 2] [ 914/1093] time: 7168.9888, d_loss: 1.39563513, g_loss: 0.67776477 Epoch: [ 2] [ 915/1093] time: 7171.2900, d_loss: 1.38675511, g_loss: 0.67512888 Epoch: [ 2] [ 916/1093] time: 7173.6588, d_loss: 1.38995445, g_loss: 0.67824239 Epoch: [ 2] [ 917/1093] time: 7175.6899, d_loss: 1.38771570, g_loss: 0.67128199 Epoch: [ 2] [ 918/1093] time: 7177.9085, d_loss: 1.38684642, g_loss: 0.68519258 Epoch: [ 2] [ 919/1093] time: 7180.3298, d_loss: 1.37333655, g_loss: 0.68652695……Epoch: [ 3] [ 362/1093] time: 8356.1996, d_loss: 1.39512014, g_loss: 0.66916156 Epoch: [ 3] [ 363/1093] time: 8358.3508, d_loss: 1.39369631, g_loss: 0.67236710 Epoch: [ 3] [ 364/1093] time: 8360.7621, d_loss: 1.38735843, g_loss: 0.68572378 Epoch: [ 3] [ 365/1093] time: 8362.7831, d_loss: 1.39971066, g_loss: 0.67346537 Epoch: [ 3] [ 366/1093] time: 8364.7842, d_loss: 1.39366436, g_loss: 0.67099309 Epoch: [ 3] [ 367/1093] time: 8367.0154, d_loss: 1.38990140, g_loss: 0.67454803 Epoch: [ 3] [ 368/1093] time: 8369.3366, d_loss: 1.38183749, g_loss: 0.68153870 Epoch: [ 3] [ 369/1093] time: 8371.3877, d_loss: 1.38687146, g_loss: 0.67545623 Epoch: [ 3] [ 370/1093] time: 8373.4989, d_loss: 1.38756406, g_loss: 0.68393183 Epoch: [ 3] [ 371/1093] time: 8375.8701, d_loss: 1.39338064, g_loss: 0.68219018 Epoch: [ 3] [ 372/1093] time: 8378.0713, d_loss: 1.38763750, g_loss: 0.67938375 Epoch: [ 3] [ 373/1093] time: 8380.0724, d_loss: 1.39371848, g_loss: 0.67651957 Epoch: [ 3] [ 374/1093] time: 8382.3936, d_loss: 1.38683343, g_loss: 0.67617160 Epoch: [ 3] [ 375/1093] time: 8384.6548, d_loss: 1.37663138, g_loss: 0.68140066 Epoch: [ 3] [ 376/1093] time: 8386.6659, d_loss: 1.37809563, g_loss: 0.68609798 Epoch: [ 3] [ 377/1093] time: 8388.6870, d_loss: 1.39943898, g_loss: 0.67443997 Epoch: [ 3] [ 378/1093] time: 8390.6980, d_loss: 1.39024842, g_loss: 0.67799813 Epoch: [ 3] [ 379/1093] time: 8393.0593, d_loss: 1.38977277, g_loss: 0.67707658 Epoch: [ 3] [ 380/1093] time: 8395.2905, d_loss: 1.38423812, g_loss: 0.68118286 Epoch: [ 3] [ 381/1093] time: 8397.4416, d_loss: 1.38743722, g_loss: 0.67777479 Epoch: [ 3] [ 382/1093] time: 8399.8829, d_loss: 1.37790775, g_loss: 0.68277538 Epoch: [ 3] [ 383/1093] time: 8402.1041, d_loss: 1.38662457, g_loss: 0.68058980 Epoch: [ 3] [ 384/1093] time: 8404.1052, d_loss: 1.39429832, g_loss: 0.67511570 Epoch: [ 3] [ 385/1093] time: 8406.5865, d_loss: 1.38111138, g_loss: 0.68313456 Epoch: [ 3] [ 386/1093] time: 8408.8477, d_loss: 1.38022339, g_loss: 0.68807602 Epoch: [ 3] [ 387/1093] time: 8410.8788, d_loss: 1.37367630, g_loss: 0.68106210 Epoch: [ 3] [ 388/1093] time: 8413.1800, d_loss: 1.37601101, g_loss: 0.68398643 Epoch: [ 3] [ 389/1093] time: 8415.5213, d_loss: 1.38206851, g_loss: 0.68312538 Epoch: [ 3] [ 390/1093] time: 8417.5339, d_loss: 1.39440620, g_loss: 0.67587590 Epoch: [ 3] [ 391/1093] time: 8419.7451, d_loss: 1.38435912, g_loss: 0.68598908 Epoch: [ 3] [ 392/1093] time: 8422.1564, d_loss: 1.38480914, g_loss: 0.67896384 Epoch: [ 3] [ 393/1093] time: 8424.1875, d_loss: 1.39561296, g_loss: 0.67151248 Epoch: [ 3] [ 394/1093] time: 8426.1785, d_loss: 1.38200879, g_loss: 0.67769241 Epoch: [ 3] [ 395/1093] time: 8428.5098, d_loss: 1.38265324, g_loss: 0.67953098 Epoch: [ 3] [ 396/1093] time: 8430.8210, d_loss: 1.38887477, g_loss: 0.68306112 Epoch: [ 3] [ 397/1093] time: 8432.8221, d_loss: 1.37987733, g_loss: 0.68379599 Epoch: [ 3] [ 398/1093] time: 8435.1133, d_loss: 1.38668215, g_loss: 0.68350947 Epoch: [ 3] [ 399/1093] time: 8437.4845, d_loss: 1.38988137, g_loss: 0.67655754 Epoch: [ 3] [ 400/1093] time: 8439.5957, d_loss: 1.39809549, g_loss: 0.66322517 Epoch: [ 3] [ 401/1093] time: 8441.5667, d_loss: 1.38576388, g_loss: 0.67762470 Epoch: [ 3] [ 402/1093] time: 8443.5578, d_loss: 1.39277625, g_loss: 0.67869860 Epoch: [ 3] [ 403/1093] time: 8445.9391, d_loss: 1.37714362, g_loss: 0.68563044 Epoch: [ 3] [ 404/1093] time: 8448.1903, d_loss: 1.38713384, g_loss: 0.68173122 Epoch: [ 3] [ 405/1093] time: 8450.1813, d_loss: 1.38332641, g_loss: 0.67876709 Epoch: [ 3] [ 406/1093] time: 8452.2124, d_loss: 1.38762641, g_loss: 0.67437690 Epoch: [ 3] [ 407/1093] time: 8454.6137, d_loss: 1.39600587, g_loss: 0.67091662 Epoch: [ 3] [ 408/1093] time: 8456.9349, d_loss: 1.39475024, g_loss: 0.67384183 Epoch: [ 3] [ 409/1093] time: 8458.9060, d_loss: 1.38960707, g_loss: 0.67936569 Epoch: [ 3] [ 410/1093] time: 8461.1472, d_loss: 1.40030944, g_loss: 0.67041624 Epoch: [ 3] [ 411/1093] time: 8463.5084, d_loss: 1.39593017, g_loss: 0.67498016 Epoch: [ 3] [ 412/1093] time: 8465.5195, d_loss: 1.38999593, g_loss: 0.67841613 Epoch: [ 3] [ 413/1093] time: 8467.6707, d_loss: 1.38826776, g_loss: 0.67693788 Epoch: [ 3] [ 414/1093] time: 8470.0919, d_loss: 1.38349032, g_loss: 0.68139064 Epoch: [ 3] [ 415/1093] time: 8472.1430, d_loss: 1.39280987, g_loss: 0.67648333 Epoch: [ 3] [ 416/1093] time: 8474.1467, d_loss: 1.38741899, g_loss: 0.68393362 Epoch: [ 3] [ 417/1093] time: 8476.4279, d_loss: 1.38289893, g_loss: 0.68440443 Epoch: [ 3] [ 418/1093] time: 8478.8592, d_loss: 1.38225627, g_loss: 0.68390000 Epoch: [ 3] [ 419/1093] time: 8480.8303, d_loss: 1.38956904, g_loss: 0.68032801 Epoch: [ 3] [ 420/1093] time: 8483.0515, d_loss: 1.39274383, g_loss: 0.67899847 [Sample] d_loss: 1.38313830, g_loss: 0.69713199 Epoch: [ 3] [ 421/1093] time: 8485.8330, d_loss: 1.38047338, g_loss: 0.68255627 Epoch: [ 3] [ 422/1093] time: 8487.8740, d_loss: 1.38204312, g_loss: 0.68332297 Epoch: [ 3] [ 423/1093] time: 8489.8651, d_loss: 1.39266825, g_loss: 0.67830092 Epoch: [ 3] [ 424/1093] time: 8491.8662, d_loss: 1.37269580, g_loss: 0.68564689 Epoch: [ 3] [ 425/1093] time: 8494.2675, d_loss: 1.38354051, g_loss: 0.67787158 Epoch: [ 3] [ 426/1093] time: 8496.5987, d_loss: 1.39322877, g_loss: 0.67212951 Epoch: [ 3] [ 427/1093] time: 8498.5898, d_loss: 1.38431156, g_loss: 0.68219298 Epoch: [ 3] [ 428/1093] time: 8500.9110, d_loss: 1.38419461, g_loss: 0.68294287 Epoch: [ 3] [ 429/1093] time: 8503.6765, d_loss: 1.38120306, g_loss: 0.68784416 Epoch: [ 3] [ 430/1093] time: 8505.8076, d_loss: 1.37416363, g_loss: 0.68105757 Epoch: [ 3] [ 431/1093] time: 8508.4290, d_loss: 1.38731599, g_loss: 0.67775297 Epoch: [ 3] [ 432/1093] time: 8511.1404, d_loss: 1.37534189, g_loss: 0.69095331 Epoch: [ 3] [ 433/1093] time: 8513.5317, d_loss: 1.37824667, g_loss: 0.68170595 Epoch: [ 3] [ 434/1093] time: 8516.2832, d_loss: 1.39408314, g_loss: 0.67305529 Epoch: [ 3] [ 435/1093] time: 8518.4143, d_loss: 1.38113260, g_loss: 0.68300515 Epoch: [ 3] [ 436/1093] time: 8520.6555, d_loss: 1.37323284, g_loss: 0.68911874 Epoch: [ 3] [ 437/1093] time: 8523.2069, d_loss: 1.38123012, g_loss: 0.68157446 Epoch: [ 3] [ 438/1093] time: 8525.7382, d_loss: 1.40273654, g_loss: 0.67261004 Epoch: [ 3] [ 439/1093] time: 8527.9470, d_loss: 1.39703226, g_loss: 0.67010546 Epoch: [ 3] [ 440/1093] time: 8530.5494, d_loss: 1.39484859, g_loss: 0.67308128 Epoch: [ 3] [ 441/1093] time: 8533.0077, d_loss: 1.38215089, g_loss: 0.67669988 Epoch: [ 3] [ 442/1093] time: 8535.0588, d_loss: 1.39523888, g_loss: 0.67494458 Epoch: [ 3] [ 443/1093] time: 8537.3100, d_loss: 1.39211106, g_loss: 0.68104178 Epoch: [ 3] [ 444/1093] time: 8539.8213, d_loss: 1.39172995, g_loss: 0.67493176 Epoch: [ 3] [ 445/1093] time: 8542.0925, d_loss: 1.37271404, g_loss: 0.68719661

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總結(jié)

以上是生活随笔為你收集整理的TF之DCGAN：基于TF利用DCGAN测试MNIST数据集并进行生成过程全记录的全部內(nèi)容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網(wǎng)站內(nèi)容還不錯，歡迎將生活随笔推薦給好友。