G1 - 生成对抗网络（GAN）

[Epoch 0/50] [Batch 299/938] [D loss: 1.420514] [G loss: 1.961581] [D real: 0.811836] [D fake: 0.694541] [Epoch 0/50] [Batch 599/938] [D loss: 0.922259] [G loss: 1.839481] [D real: 0.734683] [D fake: 0.444037] [Epoch 0/50] [Batch 899/938] [D loss: 0.883128] [G loss: 1.256595] [D real: 0.541903] [D fake: 0.187425] [Epoch 1/50] [Batch 299/938] [D loss: 0.952949] [G loss: 0.963832] [D real: 0.596297] [D fake: 0.311674] [Epoch 1/50] [Batch 599/938] [D loss: 0.950359] [G loss: 0.834425] [D real: 0.543845] [D fake: 0.203204] [Epoch 1/50] [Batch 899/938] [D loss: 0.973158] [G loss: 1.313089] [D real: 0.631495] [D fake: 0.311304] [Epoch 2/50] [Batch 299/938] [D loss: 0.812588] [G loss: 1.251890] [D real: 0.721250] [D fake: 0.340005] [Epoch 2/50] [Batch 599/938] [D loss: 0.804412] [G loss: 1.442456] [D real: 0.651448] [D fake: 0.206814] [Epoch 2/50] [Batch 899/938] [D loss: 0.796317] [G loss: 1.303452] [D real: 0.636756] [D fake: 0.235744] [Epoch 3/50] [Batch 299/938] [D loss: 0.818155] [G loss: 1.293481] [D real: 0.613244] [D fake: 0.196964] [Epoch 3/50] [Batch 599/938] [D loss: 0.929434] [G loss: 1.275021] [D real: 0.659611] [D fake: 0.259689] [Epoch 3/50] [Batch 899/938] [D loss: 0.712755] [G loss: 2.305767] [D real: 0.800935] [D fake: 0.339025] [Epoch 4/50] [Batch 299/938] [D loss: 0.740710] [G loss: 2.199127] [D real: 0.808014] [D fake: 0.370125] [Epoch 4/50] [Batch 599/938] [D loss: 0.796852] [G loss: 2.494107] [D real: 0.848230] [D fake: 0.427257] [Epoch 4/50] [Batch 899/938] [D loss: 0.801556] [G loss: 1.366514] [D real: 0.619396] [D fake: 0.125212] [Epoch 5/50] [Batch 299/938] [D loss: 0.866250] [G loss: 2.395396] [D real: 0.806042] [D fake: 0.434844] [Epoch 5/50] [Batch 599/938] [D loss: 0.802661] [G loss: 1.157616] [D real: 0.661669] [D fake: 0.212725] [Epoch 5/50] [Batch 899/938] [D loss: 0.886610] [G loss: 1.533640] [D real: 0.700454] [D fake: 0.274916] [Epoch 6/50] [Batch 299/938] [D loss: 0.677418] [G loss: 2.137760] [D real: 0.714654] [D fake: 0.156297] [Epoch 6/50] [Batch 599/938] [D loss: 0.852677] [G loss: 1.679850] [D real: 0.712336] [D fake: 0.336238] [Epoch 6/50] [Batch 899/938] [D loss: 0.894991] [G loss: 1.345476] [D real: 0.609528] [D fake: 0.158049] [Epoch 7/50] [Batch 299/938] [D loss: 0.749311] [G loss: 2.185987] [D real: 0.786740] [D fake: 0.332746] [Epoch 7/50] [Batch 599/938] [D loss: 0.823957] [G loss: 2.364408] [D real: 0.828811] [D fake: 0.423014] [Epoch 7/50] [Batch 899/938] [D loss: 0.811460] [G loss: 1.441192] [D real: 0.611505] [D fake: 0.110525] [Epoch 8/50] [Batch 299/938] [D loss: 0.653301] [G loss: 1.886070] [D real: 0.764065] [D fake: 0.245890] [Epoch 8/50] [Batch 599/938] [D loss: 0.843600] [G loss: 1.917097] [D real: 0.792145] [D fake: 0.408509] [Epoch 8/50] [Batch 899/938] [D loss: 0.798109] [G loss: 1.314119] [D real: 0.653977] [D fake: 0.185030] [Epoch 9/50] [Batch 299/938] [D loss: 0.947685] [G loss: 3.152684] [D real: 0.910022] [D fake: 0.502504] [Epoch 9/50] [Batch 599/938] [D loss: 0.959668] [G loss: 0.570251] [D real: 0.570070] [D fake: 0.106891] [Epoch 9/50] [Batch 899/938] [D loss: 0.856521] [G loss: 1.218792] [D real: 0.566056] [D fake: 0.080608] [Epoch 10/50] [Batch 299/938] [D loss: 0.935204] [G loss: 2.985981] [D real: 0.830788] [D fake: 0.465305] [Epoch 10/50] [Batch 599/938] [D loss: 0.692477] [G loss: 1.852279] [D real: 0.835356] [D fake: 0.337193] [Epoch 10/50] [Batch 899/938] [D loss: 0.763710] [G loss: 1.751910] [D real: 0.770129] [D fake: 0.313941] [Epoch 11/50] [Batch 299/938] [D loss: 0.703495] [G loss: 1.861948] [D real: 0.808757] [D fake: 0.338974] [Epoch 11/50] [Batch 599/938] [D loss: 0.815235] [G loss: 2.208552] [D real: 0.757724] [D fake: 0.324712] [Epoch 11/50] [Batch 899/938] [D loss: 0.997158] [G loss: 2.022480] [D real: 0.701744] [D fake: 0.380837] [Epoch 12/50] [Batch 299/938] [D loss: 0.759668] [G loss: 1.911369] [D real: 0.777231] [D fake: 0.329774] [Epoch 12/50] [Batch 599/938] [D loss: 0.845963] [G loss: 2.053480] [D real: 0.846165] [D fake: 0.441215] [Epoch 12/50] [Batch 899/938] [D loss: 1.091019] [G loss: 1.313121] [D real: 0.482313] [D fake: 0.064774] [Epoch 13/50] [Batch 299/938] [D loss: 0.860023] [G loss: 1.465194] [D real: 0.635496] [D fake: 0.124226] [Epoch 13/50] [Batch 599/938] [D loss: 0.756671] [G loss: 1.716278] [D real: 0.674119] [D fake: 0.216907] [Epoch 13/50] [Batch 899/938] [D loss: 0.716931] [G loss: 1.802271] [D real: 0.683680] [D fake: 0.195853] [Epoch 14/50] [Batch 299/938] [D loss: 1.083009] [G loss: 1.358789] [D real: 0.642891] [D fake: 0.376322] [Epoch 14/50] [Batch 599/938] [D loss: 1.075695] [G loss: 0.908844] [D real: 0.521514] [D fake: 0.123268] [Epoch 14/50] [Batch 899/938] [D loss: 0.943146] [G loss: 1.356610] [D real: 0.595750] [D fake: 0.180492] [Epoch 15/50] [Batch 299/938] [D loss: 0.929019] [G loss: 0.617656] [D real: 0.552842] [D fake: 0.151570] [Epoch 15/50] [Batch 599/938] [D loss: 1.052583] [G loss: 2.127165] [D real: 0.853073] [D fake: 0.523554] [Epoch 15/50] [Batch 899/938] [D loss: 1.021363] [G loss: 0.625215] [D real: 0.529443] [D fake: 0.186696] [Epoch 16/50] [Batch 299/938] [D loss: 0.929158] [G loss: 2.104063] [D real: 0.770136] [D fake: 0.399831] [Epoch 16/50] [Batch 599/938] [D loss: 0.832833] [G loss: 1.665707] [D real: 0.736168] [D fake: 0.343671] [Epoch 16/50] [Batch 899/938] [D loss: 0.730055] [G loss: 1.724510] [D real: 0.755085] [D fake: 0.289238] [Epoch 17/50] [Batch 299/938] [D loss: 0.677890] [G loss: 1.755648] [D real: 0.779917] [D fake: 0.276746] [Epoch 17/50] [Batch 599/938] [D loss: 0.920615] [G loss: 1.416380] [D real: 0.681394] [D fake: 0.310024] [Epoch 17/50] [Batch 899/938] [D loss: 0.937411] [G loss: 2.415898] [D real: 0.789968] [D fake: 0.450372] [Epoch 18/50] [Batch 299/938] [D loss: 0.841531] [G loss: 1.211814] [D real: 0.672196] [D fake: 0.268470] [Epoch 18/50] [Batch 599/938] [D loss: 0.806454] [G loss: 1.246511] [D real: 0.657565] [D fake: 0.237899] [Epoch 18/50] [Batch 899/938] [D loss: 0.965483] [G loss: 1.558758] [D real: 0.590535] [D fake: 0.202962] [Epoch 19/50] [Batch 299/938] [D loss: 0.941242] [G loss: 1.201063] [D real: 0.580414] [D fake: 0.159809] [Epoch 19/50] [Batch 599/938] [D loss: 0.763269] [G loss: 1.117927] [D real: 0.687018] [D fake: 0.217924] [Epoch 19/50] [Batch 899/938] [D loss: 1.208787] [G loss: 0.476900] [D real: 0.450625] [D fake: 0.153400] [Epoch 20/50] [Batch 299/938] [D loss: 0.938517] [G loss: 1.020504] [D real: 0.583086] [D fake: 0.211353] [Epoch 20/50] [Batch 599/938] [D loss: 0.814142] [G loss: 1.717330] [D real: 0.767125] [D fake: 0.357556] [Epoch 20/50] [Batch 899/938] [D loss: 0.914405] [G loss: 1.084474] [D real: 0.614264] [D fake: 0.197619] [Epoch 21/50] [Batch 299/938] [D loss: 0.911557] [G loss: 1.857509] [D real: 0.690771] [D fake: 0.324216] [Epoch 21/50] [Batch 599/938] [D loss: 0.846429] [G loss: 1.522789] [D real: 0.756585] [D fake: 0.380105] [Epoch 21/50] [Batch 899/938] [D loss: 0.903101] [G loss: 1.311370] [D real: 0.641948] [D fake: 0.270648] [Epoch 22/50] [Batch 299/938] [D loss: 1.136434] [G loss: 1.967754] [D real: 0.829407] [D fake: 0.539150] [Epoch 22/50] [Batch 599/938] [D loss: 0.761561] [G loss: 1.451730] [D real: 0.719943] [D fake: 0.253529] [Epoch 22/50] [Batch 899/938] [D loss: 0.947273] [G loss: 1.578539] [D real: 0.757281] [D fake: 0.402005] [Epoch 23/50] [Batch 299/938] [D loss: 0.984664] [G loss: 1.381901] [D real: 0.676672] [D fake: 0.345036] [Epoch 23/50] [Batch 599/938] [D loss: 1.056997] [G loss: 1.273649] [D real: 0.645240] [D fake: 0.341262] [Epoch 23/50] [Batch 899/938] [D loss: 0.846916] [G loss: 1.618449] [D real: 0.673545] [D fake: 0.255247] [Epoch 24/50] [Batch 299/938] [D loss: 1.020407] [G loss: 2.467137] [D real: 0.789029] [D fake: 0.483512] [Epoch 24/50] [Batch 599/938] [D loss: 1.039248] [G loss: 1.711153] [D real: 0.794231] [D fake: 0.498774] [Epoch 24/50] [Batch 899/938] [D loss: 0.891359] [G loss: 1.549422] [D real: 0.648600] [D fake: 0.242511] [Epoch 25/50] [Batch 299/938] [D loss: 0.828505] [G loss: 1.678849] [D real: 0.726778] [D fake: 0.317394] [Epoch 25/50] [Batch 599/938] [D loss: 0.835318] [G loss: 1.619812] [D real: 0.776715] [D fake: 0.385841] [Epoch 25/50] [Batch 899/938] [D loss: 0.903816] [G loss: 2.057058] [D real: 0.759536] [D fake: 0.398490] [Epoch 26/50] [Batch 299/938] [D loss: 0.963138] [G loss: 2.443241] [D real: 0.829611] [D fake: 0.456530] [Epoch 26/50] [Batch 599/938] [D loss: 1.219956] [G loss: 0.801282] [D real: 0.441290] [D fake: 0.112515] [Epoch 26/50] [Batch 899/938] [D loss: 1.282843] [G loss: 0.742314] [D real: 0.440508] [D fake: 0.091521] [Epoch 27/50] [Batch 299/938] [D loss: 1.044027] [G loss: 1.633780] [D real: 0.730091] [D fake: 0.396968] [Epoch 27/50] [Batch 599/938] [D loss: 1.039986] [G loss: 1.568461] [D real: 0.674084] [D fake: 0.377297] [Epoch 27/50] [Batch 899/938] [D loss: 0.949207] [G loss: 1.193219] [D real: 0.626887] [D fake: 0.216131] [Epoch 28/50] [Batch 299/938