TF之DCGAN：基于TF利用DCGAN测试自己的数据集并进行生成过程全记录

TF之DCGAN：基于TF利用DCGAN测试自己的数据集并进行生成过程全记录训练的数据集部分图片以从网上收集了许多日式动画为例

输出结果1、默认参数输出结果train_00_0099

train_00_0399

train_00_0599

train_00_0799

train_01_0099

2、更改不同参数option=0 、option=1输出结果

使用option=0的可视化方法产生的图片使用option=1的可视化方法产生的图片GAN 模型隐空间中的插值可视化训练过程全记录1518~1910开始训练……{'batch_size': <absl.flags._flag.Flag object at 0x000002C943CD16A0>, 'beta1': <absl.flags._flag.Flag object at 0x000002C9463D5F60>, 'checkpoint_dir': <absl.flags._flag.Flag object at 0x000002C946422CC0>, 'crop': <absl.flags._flag.BooleanFlag object at 0x000002C946422E10>, 'dataset': <absl.flags._flag.Flag object at 0x000002C946422BA8>, 'epoch': <absl.flags._flag.Flag object at 0x000002C93CA90320>, 'h': <tensorflow.python.platform.app._HelpFlag object at 0x000002C946422EF0>, 'help': <tensorflow.python.platform.app._HelpFlag object at 0x000002C946422EF0>, 'helpfull': <tensorflow.python.platform.app._HelpfullFlag object at 0x000002C946422F60>, 'helpshort': <tensorflow.python.platform.app._HelpshortFlag object at 0x000002C946422FD0>, 'input_fname_pattern': <absl.flags._flag.Flag object at 0x000002C946422C18>, 'input_height': <absl.flags._flag.Flag object at 0x000002C943CD1B38>, 'input_width': <absl.flags._flag.Flag object at 0x000002C946422A20>, 'learning_rate': <absl.flags._flag.Flag object at 0x000002C93E5E7DA0>, 'output_height': <absl.flags._flag.Flag object at 0x000002C946422A90>, 'output_width': <absl.flags._flag.Flag object at 0x000002C946422B38>, 'sample_dir': <absl.flags._flag.Flag object at 0x000002C946422D30>, 'train': <absl.flags._flag.BooleanFlag object at 0x000002C946422D68>, 'train_size': <absl.flags._flag.Flag object at 0x000002C943CD10F0>, 'visualize': <absl.flags._flag.BooleanFlag object at 0x000002C946422E80>}2018-10-06 15:18:41.635062: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2---------Variables: name (type shape) [size]---------generator/g_h0_lin/Matrix:0 (float32_ref 100x4608) [460800, bytes: 1843200]generator/g_h0_lin/bias:0 (float32_ref 4608) [4608, bytes: 18432]generator/g_bn0/beta:0 (float32_ref 512) [512, bytes: 2048]generator/g_bn0/gamma:0 (float32_ref 512) [512, bytes: 2048]generator/g_h1/w:0 (float32_ref 5x5x256x512) [3276800, bytes: 13107200]generator/g_h1/biases:0 (float32_ref 256) [256, bytes: 1024]generator/g_bn1/beta:0 (float32_ref 256) [256, bytes: 1024]generator/g_bn1/gamma:0 (float32_ref 256) [256, bytes: 1024]generator/g_h2/w:0 (float32_ref 5x5x128x256) [819200, bytes: 3276800]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 5x5x64x128) [204800, bytes: 819200]generator/g_h3/biases:0 (float32_ref 64) [64, bytes: 256]generator/g_bn3/beta:0 (float32_ref 64) [64, bytes: 256]generator/g_bn3/gamma:0 (float32_ref 64) [64, bytes: 256]generator/g_h4/w:0 (float32_ref 5x5x3x64) [4800, bytes: 19200]generator/g_h4/biases:0 (float32_ref 3) [3, bytes: 12]discriminator/d_h0_conv/w:0 (float32_ref 5x5x3x64) [4800, bytes: 19200]discriminator/d_h0_conv/biases:0 (float32_ref 64) [64, bytes: 256]discriminator/d_h1_conv/w:0 (float32_ref 5x5x64x128) [204800, bytes: 819200]discriminator/d_h1_conv/biases:0 (float32_ref 128) [128, bytes: 512]discriminator/d_bn1/beta:0 (float32_ref 128) [128, bytes: 512]discriminator/d_bn1/gamma:0 (float32_ref 128) [128, bytes: 512]discriminator/d_h2_conv/w:0 (float32_ref 5x5x128x256) [819200, bytes: 3276800]discriminator/d_h2_conv/biases:0 (float32_ref 256) [256, bytes: 1024]discriminator/d_bn2/beta:0 (float32_ref 256) [256, bytes: 1024]discriminator/d_bn2/gamma:0 (float32_ref 256) [256, bytes: 1024]discriminator/d_h3_conv/w:0 (float32_ref 5x5x256x512) [3276800, bytes: 13107200]discriminator/d_h3_conv/biases:0 (float32_ref 512) [512, bytes: 2048]discriminator/d_bn3/beta:0 (float32_ref 512) [512, bytes: 2048]discriminator/d_bn3/gamma:0 (float32_ref 512) [512, bytes: 2048]discriminator/d_h4_lin/Matrix:0 (float32_ref 4608x1) [4608, bytes: 18432]discriminator/d_h4_lin/bias:0 (float32_ref 1) [1, bytes: 4]Total size of variables: 9086340Total bytes of variables: 36345360[*] Reading checkpoints... [*] Failed to find a checkpoint [!] Load failed...Epoch: [ 0] [ 0/ 800] time: 14.9779, d_loss: 5.05348301, g_loss: 0.00766894Epoch: [ 0] [ 1/ 800] time: 28.0542, d_loss: 4.82881641, g_loss: 0.01297333Epoch: [ 0] [ 2/ 800] time: 40.2559, d_loss: 3.48951864, g_loss: 0.07677600Epoch: [ 0] [ 3/ 800] time: 53.2987, d_loss: 4.46177912, g_loss: 0.01912572Epoch: [ 0] [ 4/ 800] time: 66.6449, d_loss: 3.76898527, g_loss: 0.06732680Epoch: [ 0] [ 5/ 800] time: 80.2566, d_loss: 3.12670279, g_loss: 0.12792118Epoch: [ 0] [ 6/ 800] time: 94.6307, d_loss: 3.61706448, g_loss: 0.05859204Epoch: [ 0] [ 7/ 800] time: 108.9309, d_loss: 2.67836666, g_loss: 0.26883626Epoch: [ 0] [ 8/ 800] time: 122.1341, d_loss: 3.90734839, g_loss: 0.05641707Epoch: [ 0] [ 9/ 800] time: 135.7154, d_loss: 1.87382483, g_loss: 1.13096261Epoch: [ 0] [ 10/ 800] time: 148.9689, d_loss: 6.14149714, g_loss: 0.00330601……Epoch: [ 0] [ 80/ 800] time: 1174.5982, d_loss: 2.07529640, g_loss: 0.39124209Epoch: [ 0] [ 81/ 800] time: 1192.4455, d_loss: 2.01820517, g_loss: 0.43641573Epoch: [ 0] [ 82/ 800] time: 1210.1161, d_loss: 2.14325690, g_loss: 0.41077107Epoch: [ 0] [ 83/ 800] time: 1226.0585, d_loss: 2.06479096, g_loss: 0.49251628Epoch: [ 0] [ 84/ 800] time: 1242.0143, d_loss: 2.23370504, g_loss: 0.43198395Epoch: [ 0] [ 85/ 800] time: 1257.2267, d_loss: 2.12133884, g_loss: 0.49163312Epoch: [ 0] [ 86/ 800] time: 1272.5151, d_loss: 2.12812853, g_loss: 0.45083773Epoch: [ 0] [ 87/ 800] time: 1289.7231, d_loss: 1.85827374, g_loss: 0.54915452Epoch: [ 0] [ 88/ 800] time: 1305.7893, d_loss: 1.75407577, g_loss: 0.59886670Epoch: [ 0] [ 89/ 800] time: 1324.8202, d_loss: 1.92280674, g_loss: 0.43640304Epoch: [ 0] [ 90/ 800] time: 1342.7920, d_loss: 1.90137959, g_loss: 0.45802355Epoch: [ 0] [ 91/ 800] time: 1361.9827, d_loss: 1.85933983, g_loss: 0.47512102Epoch: [ 0] [ 92/ 800] time: 1376.7853, d_loss: 1.83109379, g_loss: 0.53952801Epoch: [ 0] [ 93/ 800] time: 1391.9553, d_loss: 1.89624429, g_loss: 0.48314875Epoch: [ 0] [ 94/ 800] time: 1405.7957, d_loss: 1.95725751, g_loss: 0.50201762Epoch: [ 0] [ 95/ 800] time: 1419.8575, d_loss: 2.04467034, g_loss: 0.47200602Epoch: [ 0] [ 96/ 800] time: 1432.6235, d_loss: 1.86375761, g_loss: 0.63056684Epoch: [ 0] [ 97/ 800] time: 1446.1109, d_loss: 1.75833380, g_loss: 0.68587345Epoch: [ 0] [ 98/ 800] time: 1459.7021, d_loss: 1.61311054, g_loss: 0.56521410Epoch: [ 0] [ 99/ 800] time: 1473.4438, d_loss: 1.63083386, g_loss: 0.55198652[Sample] d_loss: 1.56934571, g_loss: 0.58893394Epoch: [ 0] [ 100/ 800] time: 1490.8011, d_loss: 2.02212882, g_loss: 0.38942879Epoch: [ 0] [ 101/ 800] time: 1504.8573, d_loss: 2.08615398, g_loss: 0.41869015Epoch: [ 0] [ 102/ 800] time: 1520.3561, d_loss: 1.94494843, g_loss: 0.52331185Epoch: [ 0] [ 103/ 800] time: 1534.8911, d_loss: 1.68799090, g_loss: 0.57893807Epoch: [ 0] [ 104/ 800] time: 1550.2059, d_loss: 1.73278153, g_loss: 0.55513334Epoch: [ 0] [ 105/ 800] time: 1564.4857, d_loss: 1.66107357, g_loss: 0.58009803Epoch: [ 0] [ 106/ 800] time: 1577.7365, d_loss: 1.62651777, g_loss: 0.68608046Epoch: [ 0] [ 107/ 800] time: 1591.2906, d_loss: 1.68899119, g_loss: 0.64795619Epoch: [ 0] [ 108/ 800] time: 1604.4354, d_loss: 1.64453030, g_loss: 0.66518682Epoch: [ 0] [ 109/ 800] time: 1618.1593, d_loss: 1.56328249, g_loss: 0.66451979Epoch: [ 0] [ 110/ 800] time: 1633.1294, d_loss: 1.51543558, g_loss: 0.77611113……Epoch: [ 0] [ 160/ 800] time: 2385.2872, d_loss: 1.92123890, g_loss: 0.45402479Epoch: [ 0] [ 161/ 800] time: 2400.4567, d_loss: 1.78833413, g_loss: 0.53086638Epoch: [ 0] [ 162/ 800] time: 2415.2647, d_loss: 1.57849348, g_loss: 0.71513641Epoch: [ 0] [ 163/ 800] time: 2429.8398, d_loss: 1.67605543, g_loss: 0.65658081Epoch: [ 0] [ 164/ 800] time: 2447.2616, d_loss: 1.41697562, g_loss: 0.69170052Epoch: [ 0] [ 165/ 800] time: 2462.9209, d_loss: 1.37472379, g_loss: 0.81910974Epoch: [ 0] [ 166/ 800] time: 2479.5134, d_loss: 1.52106404, g_loss: 0.65593958Epoch: [ 0] [ 167/ 800] time: 2499.4337, d_loss: 1.48481750, g_loss: 0.56352514Epoch: [ 0] [ 168/ 800] time: 2515.0022, d_loss: 1.51672626, g_loss: 0.61658454Epoch: [ 0] [ 169/ 800] time: 2529.4996, d_loss: 1.60589409, g_loss: 0.63836646Epoch: [ 0] [ 170/ 800] time: 2543.3981, d_loss: 1.44772625, g_loss: 0.65181255……Epoch: [ 0] [ 190/ 800] time: 2825.9758, d_loss: 1.47412062, g_loss: 0.54513580Epoch: [ 0] [ 191/ 800] time: 2838.9723, d_loss: 1.55055904, g_loss: 0.58368361Epoch: [ 0] [ 192/ 800] time: 2852.2630, d_loss: 1.59510207, g_loss: 0.66829801Epoch: [ 0] [ 193/ 800] time: 2866.4205, d_loss: 1.46519923, g_loss: 0.61558247Epoch: [ 0] [ 194/ 800] time: 2879.9993, d_loss: 1.32191777, g_loss: 0.80541551Epoch: [ 0] [ 195/ 800] time: 2893.4340, d_loss: 1.01147175, g_loss: 1.06913197Epoch: [ 0] [ 196/ 800] time: 2906.5733, d_loss: 0.93962598, g_loss: 0.83171976Epoch: [ 0] [ 197/ 800] time: 2920.1912, d_loss: 1.17017913, g_loss: 0.67285419Epoch: [ 0] [ 198/ 800] time: 2933.5356, d_loss: 1.59560084, g_loss: 0.56722575Epoch: [ 0] [ 199/ 800] time: 2947.0078, d_loss: 1.79016471, g_loss: 0.63441348[Sample] d_loss: 1.81597352, g_loss: 0.72201991Epoch: [ 0] [ 200/ 800] time: 2962.8138, d_loss: 1.84360504, g_loss: 0.68355072Epoch: [ 0] [ 201/ 800] time: 2976.0156, d_loss: 1.79623175, g_loss: 0.82725859Epoch: [ 0] [ 202/ 800] time: 2990.1701, d_loss: 1.84564495, g_loss: 0.36759761Epoch: [ 0] [ 203/ 800] time: 3003.2376, d_loss: 1.33034515, g_loss: 1.12043190Epoch: [ 0] [ 204/ 800] time: 3016.9012, d_loss: 1.43244946, g_loss: 0.60710204Epoch: [ 0] [ 205/ 800] time: 3031.2064, d_loss: 1.77543664, g_loss: 0.37925830Epoch: [ 0] [ 206/ 800] time: 3044.6623, d_loss: 1.38716245, g_loss: 0.79690325Epoch: [ 0] [ 207/ 800] time: 3058.6295, d_loss: 1.41732562, g_loss: 0.71504021Epoch: [ 0] [ 208/ 800] time: 3075.1982, d_loss: 1.48065066, g_loss: 0.58098531Epoch: [ 0] [ 209/ 800] time: 3092.2044, d_loss: 1.39409590, g_loss: 0.85311776Epoch: [ 0] [ 210/ 800] time: 3106.7110, d_loss: 1.55829871, g_loss: 0.71159673……Epoch: [ 0] [ 250/ 800] time: 3706.7694, d_loss: 1.48207712, g_loss: 0.62254345Epoch: [ 0] [ 251/ 800] time: 3722.4864, d_loss: 1.43726230, g_loss: 0.59676802Epoch: [ 0] [ 252/ 800] time: 3739.1110, d_loss: 1.39565313, g_loss: 0.61483824Epoch: [ 0] [ 253/ 800] time: 3753.9008, d_loss: 1.64175820, g_loss: 0.55743980Epoch: [ 0] [ 254/ 800] time: 3768.4591, d_loss: 2.25337219, g_loss: 0.39440048Epoch: [ 0] [ 255/ 800] time: 3784.3170, d_loss: 2.21880293, g_loss: 0.43557072Epoch: [ 0] [ 256/ 800] time: 3799.8508, d_loss: 1.92927480, g_loss: 0.60396165Epoch: [ 0] [ 257/ 800] time: 3819.0884, d_loss: 1.54789436, g_loss: 0.62363708Epoch: [ 0] [ 258/ 800] time: 3835.9283, d_loss: 1.45292878, g_loss: 0.78123999Epoch: [ 0] [ 259/ 800] time: 3851.7583, d_loss: 1.38242722, g_loss: 0.71697128Epoch: [ 0] [ 260/ 800] time: 3867.8912, d_loss: 1.42830288, g_loss: 0.72657067……Epoch: [ 0] [ 290/ 800] time: 4347.6360, d_loss: 1.51859045, g_loss: 0.63133144Epoch: [ 0] [ 291/ 800] time: 4362.6835, d_loss: 1.51562345, g_loss: 0.63072002Epoch: [ 0] [ 292/ 800] time: 4376.7609, d_loss: 1.51966012, g_loss: 0.68376446Epoch: [ 0] [ 293/ 800] time: 4391.5809, d_loss: 1.46159744, g_loss: 0.77321720Epoch: [ 0] [ 294/ 800] time: 4405.9471, d_loss: 1.51635325, g_loss: 0.64838612Epoch: [ 0] [ 295/ 800] time: 4421.1065, d_loss: 1.63491082, g_loss: 0.59127223Epoch: [ 0] [ 296/ 800] time: 4436.1505, d_loss: 1.56633282, g_loss: 0.63173258Epoch: [ 0] [ 297/ 800] time: 4451.4322, d_loss: 1.73018694, g_loss: 0.64139992Epoch: [ 0] [ 298/ 800] time: 4466.8813, d_loss: 1.60332918, g_loss: 0.64779305Epoch: [ 0] [ 299/ 800] time: 4482.4206, d_loss: 1.30365634, g_loss: 0.69317293[Sample] d_loss: 1.52858722, g_loss: 0.66097701Epoch: [ 0] [ 300/ 800] time: 4501.5354, d_loss: 1.54065537, g_loss: 0.61486077Epoch: [ 0] [ 301/ 800] time: 4517.9595, d_loss: 1.40912437, g_loss: 0.62744296Epoch: [ 0] [ 302/ 800] time: 4532.8548, d_loss: 1.83548975, g_loss: 0.48546115Epoch: [ 0] [ 303/ 800] time: 4548.5219, d_loss: 1.78749907, g_loss: 0.54208493Epoch: [ 0] [ 304/ 800] time: 4565.8423, d_loss: 1.59532309, g_loss: 0.70925272Epoch: [ 0] [ 305/ 800] time: 4582.6995, d_loss: 1.55741489, g_loss: 0.69813800Epoch: [ 0] [ 306/ 800] time: 4598.1985, d_loss: 1.46890306, g_loss: 0.65037167Epoch: [ 0] [ 307/ 800] time: 4613.3077, d_loss: 1.47391725, g_loss: 0.66135353Epoch: [ 0] [ 308/ 800] time: 4628.6944, d_loss: 1.47143006, g_loss: 0.68910688Epoch: [ 0] [ 309/ 800] time: 4643.7088, d_loss: 1.49028301, g_loss: 0.67232418Epoch: [ 0] [ 310/ 800] time: 4659.5347, d_loss: 1.59941697, g_loss: 0.67055005……Epoch: [ 0] [ 350/ 800] time: 5263.0389, d_loss: 1.52133381, g_loss: 0.66190934Epoch: [ 0] [ 351/ 800] time: 5277.8903, d_loss: 1.50694644, g_loss: 0.57145911Epoch: [ 0] [ 352/ 800] time: 5292.1188, d_loss: 1.70610642, g_loss: 0.49781984Epoch: [ 0] [ 353/ 800] time: 5306.9107, d_loss: 1.77215934, g_loss: 0.58978939Epoch: [ 0] [ 354/ 800] time: 5321.5938, d_loss: 1.74831009, g_loss: 0.67320079Epoch: [ 0] [ 355/ 800] time: 5336.4302, d_loss: 1.59669852, g_loss: 0.68336225Epoch: [ 0] [ 356/ 800] time: 5351.4221, d_loss: 1.46689534, g_loss: 0.84482712Epoch: [ 0] [ 357/ 800] time: 5367.1353, d_loss: 1.38674009, g_loss: 0.78510588Epoch: [ 0] [ 358/ 800] time: 5384.3114, d_loss: 1.30605173, g_loss: 0.85381281Epoch: [ 0] [ 359/ 800] time: 5398.4569, d_loss: 1.29629779, g_loss: 0.81868672Epoch: [ 0] [360/ 800] time: 5413.3162, d_loss: 1.21817279, g_loss: 0.80424130Epoch: [ 0] [ 361/ 800] time: 5427.5560, d_loss: 1.35527205, g_loss: 0.67310977Epoch: [ 0] [ 362/ 800] time: 5441.6695, d_loss: 1.40627885, g_loss: 0.67996454Epoch: [ 0] [ 363/ 800] time: 5459.0163, d_loss: 1.33116567, g_loss: 0.73797810Epoch: [ 0] [ 364/ 800] time: 5478.6128, d_loss: 1.29250467, g_loss: 0.82915306Epoch: [ 0] [ 365/ 800] time: 5495.0862, d_loss: 1.37827444, g_loss: 0.73634720Epoch: [ 0] [ 366/ 800] time: 5514.7329, d_loss: 1.35434794, g_loss: 0.60365015Epoch: [ 0] [ 367/ 800] time: 5529.6542, d_loss: 1.53991985, g_loss: 0.62364745Epoch: [ 0] [ 368/ 800] time: 5543.7427, d_loss: 1.72570002, g_loss: 0.62098628Epoch: [ 0] [ 369/ 800] time: 5561.1792, d_loss: 1.73738861, g_loss: 0.55012739Epoch: [ 0] [ 370/ 800] time: 5575.9147, d_loss: 1.58512247, g_loss: 0.55001098Epoch: [ 0] [ 371/ 800] time: 5592.3616, d_loss: 1.59266281, g_loss: 0.69175625……Epoch: [ 0] [ 399/ 800]……Epoch: [ 0] [ 499/ 800]……Epoch: [ 0] [ 599/ 800]……Epoch: [ 0] [ 699/ 800]……Epoch: [ 0] [ 799/ 800]……Epoch: [ 1] [ 99/ 800]