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

目录

训练的数据集部分图片

输出结果

1、默认参数输出结果

训练过程全记录

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: 9086340
Total 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: 0.00766894
Epoch: [ 0] [   1/ 800] time: 28.0542, d_loss: 4.82881641, g_loss: 0.01297333
Epoch: [ 0] [   2/ 800] time: 40.2559, d_loss: 3.48951864, g_loss: 0.07677600
Epoch: [ 0] [   3/ 800] time: 53.2987, d_loss: 4.46177912, g_loss: 0.01912572
Epoch: [ 0] [   4/ 800] time: 66.6449, d_loss: 3.76898527, g_loss: 0.06732680
Epoch: [ 0] [   5/ 800] time: 80.2566, d_loss: 3.12670279, g_loss: 0.12792118
Epoch: [ 0] [   6/ 800] time: 94.6307, d_loss: 3.61706448, g_loss: 0.05859204
Epoch: [ 0] [   7/ 800] time: 108.9309, d_loss: 2.67836666, g_loss: 0.26883626
Epoch: [ 0] [   8/ 800] time: 122.1341, d_loss: 3.90734839, g_loss: 0.05641707
Epoch: [ 0] [   9/ 800] time: 135.7154, d_loss: 1.87382483, g_loss: 1.13096261
Epoch: [ 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: 0.39124209
Epoch: [ 0] [  81/ 800] time: 1192.4455, d_loss: 2.01820517, g_loss: 0.43641573
Epoch: [ 0] [  82/ 800] time: 1210.1161, d_loss: 2.14325690, g_loss: 0.41077107
Epoch: [ 0] [  83/ 800] time: 1226.0585, d_loss: 2.06479096, g_loss: 0.49251628
Epoch: [ 0] [  84/ 800] time: 1242.0143, d_loss: 2.23370504, g_loss: 0.43198395
Epoch: [ 0] [  85/ 800] time: 1257.2267, d_loss: 2.12133884, g_loss: 0.49163312
Epoch: [ 0] [  86/ 800] time: 1272.5151, d_loss: 2.12812853, g_loss: 0.45083773
Epoch: [ 0] [  87/ 800] time: 1289.7231, d_loss: 1.85827374, g_loss: 0.54915452
Epoch: [ 0] [  88/ 800] time: 1305.7893, d_loss: 1.75407577, g_loss: 0.59886670
Epoch: [ 0] [  89/ 800] time: 1324.8202, d_loss: 1.92280674, g_loss: 0.43640304
Epoch: [ 0] [  90/ 800] time: 1342.7920, d_loss: 1.90137959, g_loss: 0.45802355
Epoch: [ 0] [  91/ 800] time: 1361.9827, d_loss: 1.85933983, g_loss: 0.47512102
Epoch: [ 0] [  92/ 800] time: 1376.7853, d_loss: 1.83109379, g_loss: 0.53952801
Epoch: [ 0] [  93/ 800] time: 1391.9553, d_loss: 1.89624429, g_loss: 0.48314875
Epoch: [ 0] [  94/ 800] time: 1405.7957, d_loss: 1.95725751, g_loss: 0.50201762
Epoch: [ 0] [  95/ 800] time: 1419.8575, d_loss: 2.04467034, g_loss: 0.47200602
Epoch: [ 0] [  96/ 800] time: 1432.6235, d_loss: 1.86375761, g_loss: 0.63056684
Epoch: [ 0] [  97/ 800] time: 1446.1109, d_loss: 1.75833380, g_loss: 0.68587345
Epoch: [ 0] [  98/ 800] time: 1459.7021, d_loss: 1.61311054, g_loss: 0.56521410
Epoch: [ 0] [  99/ 800] time: 1473.4438, d_loss: 1.63083386, g_loss: 0.55198652
[Sample] d_loss: 1.56934571, g_loss: 0.58893394
Epoch: [ 0] [ 100/ 800] time: 1490.8011, d_loss: 2.02212882, g_loss: 0.38942879
Epoch: [ 0] [ 101/ 800] time: 1504.8573, d_loss: 2.08615398, g_loss: 0.41869015
Epoch: [ 0] [ 102/ 800] time: 1520.3561, d_loss: 1.94494843, g_loss: 0.52331185
Epoch: [ 0] [ 103/ 800] time: 1534.8911, d_loss: 1.68799090, g_loss: 0.57893807
Epoch: [ 0] [ 104/ 800] time: 1550.2059, d_loss: 1.73278153, g_loss: 0.55513334
Epoch: [ 0] [ 105/ 800] time: 1564.4857, d_loss: 1.66107357, g_loss: 0.58009803
Epoch: [ 0] [ 106/ 800] time: 1577.7365, d_loss: 1.62651777, g_loss: 0.68608046
Epoch: [ 0] [ 107/ 800] time: 1591.2906, d_loss: 1.68899119, g_loss: 0.64795619
Epoch: [ 0] [ 108/ 800] time: 1604.4354, d_loss: 1.64453030, g_loss: 0.66518682
Epoch: [ 0] [ 109/ 800] time: 1618.1593, d_loss: 1.56328249, g_loss: 0.66451979
Epoch: [ 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: 0.45402479
Epoch: [ 0] [ 161/ 800] time: 2400.4567, d_loss: 1.78833413, g_loss: 0.53086638
Epoch: [ 0] [ 162/ 800] time: 2415.2647, d_loss: 1.57849348, g_loss: 0.71513641
Epoch: [ 0] [ 163/ 800] time: 2429.8398, d_loss: 1.67605543, g_loss: 0.65658081
Epoch: [ 0] [ 164/ 800] time: 2447.2616, d_loss: 1.41697562, g_loss: 0.69170052
Epoch: [ 0] [ 165/ 800] time: 2462.9209, d_loss: 1.37472379, g_loss: 0.81910974
Epoch: [ 0] [ 166/ 800] time: 2479.5134, d_loss: 1.52106404, g_loss: 0.65593958
Epoch: [ 0] [ 167/ 800] time: 2499.4337, d_loss: 1.48481750, g_loss: 0.56352514
Epoch: [ 0] [ 168/ 800] time: 2515.0022, d_loss: 1.51672626, g_loss: 0.61658454
Epoch: [ 0] [ 169/ 800] time: 2529.4996, d_loss: 1.60589409, g_loss: 0.63836646
Epoch: [ 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: 0.54513580
Epoch: [ 0] [ 191/ 800] time: 2838.9723, d_loss: 1.55055904, g_loss: 0.58368361
Epoch: [ 0] [ 192/ 800] time: 2852.2630, d_loss: 1.59510207, g_loss: 0.66829801
Epoch: [ 0] [ 193/ 800] time: 2866.4205, d_loss: 1.46519923, g_loss: 0.61558247
Epoch: [ 0] [ 194/ 800] time: 2879.9993, d_loss: 1.32191777, g_loss: 0.80541551
Epoch: [ 0] [ 195/ 800] time: 2893.4340, d_loss: 1.01147175, g_loss: 1.06913197
Epoch: [ 0] [ 196/ 800] time: 2906.5733, d_loss: 0.93962598, g_loss: 0.83171976
Epoch: [ 0] [ 197/ 800] time: 2920.1912, d_loss: 1.17017913, g_loss: 0.67285419
Epoch: [ 0] [ 198/ 800] time: 2933.5356, d_loss: 1.59560084, g_loss: 0.56722575
Epoch: [ 0] [ 199/ 800] time: 2947.0078, d_loss: 1.79016471, g_loss: 0.63441348
[Sample] d_loss: 1.81597352, g_loss: 0.72201991
Epoch: [ 0] [ 200/ 800] time: 2962.8138, d_loss: 1.84360504, g_loss: 0.68355072
Epoch: [ 0] [ 201/ 800] time: 2976.0156, d_loss: 1.79623175, g_loss: 0.82725859
Epoch: [ 0] [ 202/ 800] time: 2990.1701, d_loss: 1.84564495, g_loss: 0.36759761
Epoch: [ 0] [ 203/ 800] time: 3003.2376, d_loss: 1.33034515, g_loss: 1.12043190
Epoch: [ 0] [ 204/ 800] time: 3016.9012, d_loss: 1.43244946, g_loss: 0.60710204
Epoch: [ 0] [ 205/ 800] time: 3031.2064, d_loss: 1.77543664, g_loss: 0.37925830
Epoch: [ 0] [ 206/ 800] time: 3044.6623, d_loss: 1.38716245, g_loss: 0.79690325
Epoch: [ 0] [ 207/ 800] time: 3058.6295, d_loss: 1.41732562, g_loss: 0.71504021
Epoch: [ 0] [ 208/ 800] time: 3075.1982, d_loss: 1.48065066, g_loss: 0.58098531
Epoch: [ 0] [ 209/ 800] time: 3092.2044, d_loss: 1.39409590, g_loss: 0.85311776
Epoch: [ 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: 0.62254345
Epoch: [ 0] [ 251/ 800] time: 3722.4864, d_loss: 1.43726230, g_loss: 0.59676802
Epoch: [ 0] [ 252/ 800] time: 3739.1110, d_loss: 1.39565313, g_loss: 0.61483824
Epoch: [ 0] [ 253/ 800] time: 3753.9008, d_loss: 1.64175820, g_loss: 0.55743980
Epoch: [ 0] [ 254/ 800] time: 3768.4591, d_loss: 2.25337219, g_loss: 0.39440048
Epoch: [ 0] [ 255/ 800] time: 3784.3170, d_loss: 2.21880293, g_loss: 0.43557072
Epoch: [ 0] [ 256/ 800] time: 3799.8508, d_loss: 1.92927480, g_loss: 0.60396165
Epoch: [ 0] [ 257/ 800] time: 3819.0884, d_loss: 1.54789436, g_loss: 0.62363708
Epoch: [ 0] [ 258/ 800] time: 3835.9283, d_loss: 1.45292878, g_loss: 0.78123999
Epoch: [ 0] [ 259/ 800] time: 3851.7583, d_loss: 1.38242722, g_loss: 0.71697128
Epoch: [ 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: 0.63133144
Epoch: [ 0] [ 291/ 800] time: 4362.6835, d_loss: 1.51562345, g_loss: 0.63072002
Epoch: [ 0] [ 292/ 800] time: 4376.7609, d_loss: 1.51966012, g_loss: 0.68376446
Epoch: [ 0] [ 293/ 800] time: 4391.5809, d_loss: 1.46159744, g_loss: 0.77321720
Epoch: [ 0] [ 294/ 800] time: 4405.9471, d_loss: 1.51635325, g_loss: 0.64838612
Epoch: [ 0] [ 295/ 800] time: 4421.1065, d_loss: 1.63491082, g_loss: 0.59127223
Epoch: [ 0] [ 296/ 800] time: 4436.1505, d_loss: 1.56633282, g_loss: 0.63173258
Epoch: [ 0] [ 297/ 800] time: 4451.4322, d_loss: 1.73018694, g_loss: 0.64139992
Epoch: [ 0] [ 298/ 800] time: 4466.8813, d_loss: 1.60332918, g_loss: 0.64779305
Epoch: [ 0] [ 299/ 800] time: 4482.4206, d_loss: 1.30365634, g_loss: 0.69317293
[Sample] d_loss: 1.52858722, g_loss: 0.66097701
Epoch: [ 0] [ 300/ 800] time: 4501.5354, d_loss: 1.54065537, g_loss: 0.61486077
Epoch: [ 0] [ 301/ 800] time: 4517.9595, d_loss: 1.40912437, g_loss: 0.62744296
Epoch: [ 0] [ 302/ 800] time: 4532.8548, d_loss: 1.83548975, g_loss: 0.48546115
Epoch: [ 0] [ 303/ 800] time: 4548.5219, d_loss: 1.78749907, g_loss: 0.54208493
Epoch: [ 0] [ 304/ 800] time: 4565.8423, d_loss: 1.59532309, g_loss: 0.70925272
Epoch: [ 0] [ 305/ 800] time: 4582.6995, d_loss: 1.55741489, g_loss: 0.69813800
Epoch: [ 0] [ 306/ 800] time: 4598.1985, d_loss: 1.46890306, g_loss: 0.65037167
Epoch: [ 0] [ 307/ 800] time: 4613.3077, d_loss: 1.47391725, g_loss: 0.66135353
Epoch: [ 0] [ 308/ 800] time: 4628.6944, d_loss: 1.47143006, g_loss: 0.68910688
Epoch: [ 0] [ 309/ 800] time: 4643.7088, d_loss: 1.49028301, g_loss: 0.67232418
Epoch: [ 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: 0.66190934
Epoch: [ 0] [ 351/ 800] time: 5277.8903, d_loss: 1.50694644, g_loss: 0.57145911
Epoch: [ 0] [ 352/ 800] time: 5292.1188, d_loss: 1.70610642, g_loss: 0.49781984
Epoch: [ 0] [ 353/ 800] time: 5306.9107, d_loss: 1.77215934, g_loss: 0.58978939
Epoch: [ 0] [ 354/ 800] time: 5321.5938, d_loss: 1.74831009, g_loss: 0.67320079
Epoch: [ 0] [ 355/ 800] time: 5336.4302, d_loss: 1.59669852, g_loss: 0.68336225
Epoch: [ 0] [ 356/ 800] time: 5351.4221, d_loss: 1.46689534, g_loss: 0.84482712
Epoch: [ 0] [ 357/ 800] time: 5367.1353, d_loss: 1.38674009, g_loss: 0.78510588
Epoch: [ 0] [ 358/ 800] time: 5384.3114, d_loss: 1.30605173, g_loss: 0.85381281
Epoch: [ 0] [ 359/ 800] time: 5398.4569, d_loss: 1.29629779, g_loss: 0.81868672
Epoch: [ 0] [ 360/ 800] time: 5413.3162, d_loss: 1.21817279, g_loss: 0.80424130
Epoch: [ 0] [ 361/ 800] time: 5427.5560, d_loss: 1.35527205, g_loss: 0.67310977
Epoch: [ 0] [ 362/ 800] time: 5441.6695, d_loss: 1.40627885, g_loss: 0.67996454
Epoch: [ 0] [ 363/ 800] time: 5459.0163, d_loss: 1.33116567, g_loss: 0.73797810
Epoch: [ 0] [ 364/ 800] time: 5478.6128, d_loss: 1.29250467, g_loss: 0.82915306
Epoch: [ 0] [ 365/ 800] time: 5495.0862, d_loss: 1.37827444, g_loss: 0.73634720
Epoch: [ 0] [ 366/ 800] time: 5514.7329, d_loss: 1.35434794, g_loss: 0.60365015
Epoch: [ 0] [ 367/ 800] time: 5529.6542, d_loss: 1.53991985, g_loss: 0.62364745
Epoch: [ 0] [ 368/ 800] time: 5543.7427, d_loss: 1.72570002, g_loss: 0.62098628
Epoch: [ 0] [ 369/ 800] time: 5561.1792, d_loss: 1.73738861, g_loss: 0.55012739
Epoch: [ 0] [ 370/ 800] time: 5575.9147, d_loss: 1.58512247, g_loss: 0.55001098
Epoch: [ 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]

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相关推荐

  1. 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

    TF之DCGAN:基于TF利用DCGAN测试MNIST数据集并进行生成 目录 测试结果 测试过程全记录 测试结果 train_00_0099 train_00_0799 train_00_0899 t ...

  2. TF之pix2pix：基于TF利用Facades数据集训练pix2pix模型、测试并进行生成过程全记录

    TF之pix2pix:基于TF利用Facades数据集训练pix2pix模型.测试并进行生成过程全记录 目录 TB监控 1.SCALARS 2.IMAGES 3.GRAPHS 4.DISTRIBUTI ...

  3. docker环境安装mysql、canal、elasticsearch，基于binlog利用canal实现mysql的数据同步到elasticsearch中

    文章目录 1.docker安装 1.1 基于ubuntu 1.2 基于centos7 2.数据卷统一管理 3.安装mysql 4.安装elasticsearch 5.es安装ik中文分词器 5.1 在 ...

  4. DL之Attention-ED：基于TF NMT利用带有Attention的 ED模型训练、测试(中英文平行语料库)实现将英文翻译为中文的LSTM翻译模型过程全记录

    DL之Attention-ED:基于TF NMT利用带有Attention的 ED模型训练(中英文平行语料库)实现将英文翻译为中文的LSTM翻译模型过程全记录 目录 测试输出结果 模型监控 训练过程全 ...

  5. DL之DCGNN：基于TF利用DCGAN实现在MNIST数据集上训练生成新样本

    DL之DCGNN:基于TF利用DCGAN实现在MNIST数据集上训练生成新样本 目录 输出结果 设计思路 实现部分代码 说明:所有图片文件丢失 输出结果 更新-- 设计思路 更新-- 实现部分代码 更 ...

  6. 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

    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 目录 训练 food_resized数据集展示 TB过程监控 1.SCALAR ...

  7. TF之LoR：基于tensorflow利用逻辑回归算LoR法实现手写数字图片识别提高准确率

    TF之LoR:基于tensorflow利用逻辑回归算LoR法实现手写数字图片识别提高准确率 目录 输出结果 设计代码 输出结果 设计代码 #TF之LoR:基于tensorflow实现手写数字图片识别准 ...

  8. RL之PG：基于TF利用策略梯度算法玩Cartpole游戏实现智能得高分

    RL之PG:基于TF利用策略梯度算法玩Cartpole游戏实现智能得高分 目录 输出结果 设计思路 测试过程 输出结果 视频观看地址:强化学习-基于TF利用策略梯度算法玩Cartpole游戏实现智能得 ...

  9. DL之LSTM之MvP：基于TF利用LSTM基于DIY时间训练csv文件数据预测后100个数据(多值预测)状态

    DL之LSTM之MvP:基于TF利用LSTM基于DIY时间训练csv文件数据预测后100个数据(多值预测)状态 目录 数据集csv文件内容 输出结果 设计思路 训练记录全过程 数据集csv文件内容 输 ...

最新文章

  1. [Linux].netrc或者_netrc使用可以
  2. 手机的次世代竞争年代
  3. 如何隐藏你的 Linux 的命令行历史
  4. vim高级技巧（split）_小花_新浪博客
  5. C语言选择结构和循环结构的汇总
  6. MySQL 创建索引和索引效率验证
  7. 判断语句_如何学好C语言判断语句？攻略if语句是第一步
  8. RsyncServer服务无法启动的解决方法
  9. 摩托罗拉Edge真机谍照曝光：挖孔瀑布屏+骁龙765
  10. Snapshot使用
  11. vue实现周日历切换（两种方式）
  12. 新型安卓木马SpyNote生成器遭泄露
  13. Wangle源码分析：编解码Handler
  14. android listview 图片闪烁,listView异步加载图片导致图片错位、闪烁、重复的问题的解决...
  15. 网络知识详解之：网络攻击与安全防护
  16. 安卓手机如何查看手机控制台输出
  17. 排序算法总结--希尔排序
  18. ug java环境变量设置_关于UG环境变量
  19. 北京，探索「宜居」的技术路径
  20. mysql事务转账_模拟数据库事务实现转账

热门文章

  1. tcp http https
  2. Office365 Manager Plus之报表
  3. 同余方程———扩展欧几里得
  4. Vim文件管理器NERD tree
  5. 阿里巴巴为什么要禁止使用存储过程？
  6. 图文并茂，万字详解，带你掌握 JVM 垃圾回收！
  7. 首次公开：京东数科强一致、高性能分布式事务中间件 JDTX
  8. 凭借这 10 大算法，就可以主宰世界！
  9. 如何成为一个卓越的程序员
  10. Java8 HashMap源码分析