DCGAN for MNIST (PyTorch)

Deep Convolution GANに以下の改善を行う。

  • すべてのプーリングレイヤを strided convolutions(discriminator)と fractional-stirided convolutions(generator)に変更する。
  • generator と discriminator に batchnormを使う。
  • 全結合隠れ層を取り除く。
  • ReLU 活性関数を generatorで使う。ただし、output層は tanhを使う。
  • LeakyReLU活性関数をdiscriminatorのすべての層で使う。

もとい!

公式チュートリアルにサンプルコードが公開されているので、それを参考に実装する。

In [45]:
% matplotlib inline
import torch
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable

if torch.cuda.is_available():
    import torch.cuda as t
else:
    import torch as t

from torchvision import datasets, models, transforms, utils
import torchvision.utils as vutils

import numpy as np
from numpy.random import normal
import matplotlib.pyplot as plt
import os

mnist datasetの準備

In [207]:
bs = 100
sz = 32
In [208]:
dataloader = torch.utils.data.DataLoader(
    datasets.MNIST('data/mnist', train=True, download=True,
                   transform=transforms.Compose([
                       transforms.Scale(sz),
                       transforms.ToTensor(),
                       transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
                   ])),
    batch_size=bs
)

Model

In [246]:
nz = 100
ngf = 32
ndf = 32
nc = 1
In [263]:
'''Discriminater'''
class netD(nn.Module):
    def __init__(self):
        super(netD, self).__init__()
        self.main = nn.Sequential(
            nn.Conv2d(nc, ndf, 4, 2, 1, bias=False),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf * 2),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf * 4),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(ndf * 4, 1, 4, 1, 0, bias=False),
            nn.Sigmoid()
        )

    def forward(self, x):
        #x = x.view(100, -1)
        x = self.main(x)
        return x

'''Generator'''
class netG(nn.Module):
    def __init__(self):
        super(netG, self).__init__()
        self.main = nn.Sequential(
            nn.ConvTranspose2d(nz, ngf * 4, 4, 1, 0, bias=False),
            nn.BatchNorm2d(ngf * 4),
            nn.ReLU(True),
            nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf * 2),
            nn.ReLU(True),
            nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf),
            nn.ReLU(True),
            nn.ConvTranspose2d( ngf,nc, 4, 2, 1, bias=False),
            nn.Tanh()
        )

    def forward(self, x):
        # x = x.view(bs,100)
        x = self.main(x)
        x = x.view(-1, 1, sz, sz)
        return x
In [264]:
criteion = nn.BCELoss()
net_D = netD()
net_G = netG()

if torch.cuda.is_available():
    D = net_D.cuda()
    G = net_G.cuda()
    criteion = criteion.cuda()    
In [265]:
print(net_D)
netD (
  (main): Sequential (
    (0): Conv2d(1, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (1): LeakyReLU (0.2, inplace)
    (2): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
    (4): LeakyReLU (0.2, inplace)
    (5): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (6): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
    (7): LeakyReLU (0.2, inplace)
    (8): Conv2d(128, 1, kernel_size=(4, 4), stride=(1, 1), bias=False)
    (9): Sigmoid ()
  )
)
In [266]:
print(net_G)
netG (
  (main): Sequential (
    (0): ConvTranspose2d(100, 128, kernel_size=(4, 4), stride=(1, 1), bias=False)
    (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
    (2): ReLU (inplace)
    (3): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
    (5): ReLU (inplace)
    (6): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (7): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True)
    (8): ReLU (inplace)
    (9): ConvTranspose2d(32, 1, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (10): Tanh ()
  )
)
In [267]:
optimizerD = optim.Adam(net_D.parameters(), lr = 0.00005)
optimizerG = optim.Adam(net_G.parameters(), lr = 0.00005)

Train

In [268]:
input = t.FloatTensor(bs, 1, sz, sz)
noise = t.FloatTensor(normal(0, 1,(bs, 100, 1, 1)))
fixed_noise = t.FloatTensor(bs, 100, 1, 1).normal_(0, 1)
label = t.FloatTensor(bs)

real_label = 1
fake_label = 0

input = Variable(input)
label = Variable(label)
noise = Variable(noise)
fixed_noise = Variable(fixed_noise)
In [269]:
niter = 4000
In [270]:
for epoch in range(niter):
    for i, data in enumerate(dataloader, 0):
        ############################
        # (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))
        ###########################
        # train with real (data)
        net_D.zero_grad()
        real, _ = data
        input.data.resize_(real.size()).copy_(real)
        label.data.resize_(bs).fill_(real_label)
        output = net_D(input)
        errD_real = criteion(output, label)
        errD_real.backward()
        D_x = output.data.mean()

        #train with fake (generated)
        noise.data.resize_(bs, 100, 1, 1)
        noise.data.normal_(0, 1)
        fake = net_G(noise)
        label.data.fill_(fake_label)
        output = net_D(fake.detach())
        errD_fake = criteion(output, label)
        errD_fake.backward()
        D_G_z1 = output.data.mean()

        errD = errD_real + errD_fake
        optimizerD.step()

        ############################
        # (2) Update G network: maximize log(D(G(z)))
        ###########################
        net_G.zero_grad()
        label.data.fill_(real_label)
        output = net_D(fake)
        errG = criteion(output, label)
        errG.backward()
        D_G_z2 = output.data.mean()
        optimizerG.step()
        if i % 100 == 0:
            print('[%d/%d][%d/%d] Loss_D: %.4f Loss_G: %.4f D(x): %.4f D(G(z)): %.4f / %.4f'
                 % (epoch, niter, i, len(dataloader),
                   errD.data[0], errG.data[0],  D_x, D_G_z1, D_G_z2))
    if epoch % 10 == 0:
        fake = net_G(fixed_noise)
        vutils.save_image(fake.data, '%s/fake_samples_epoch_%03d.png'
                              % ('results', epoch),normalize=True)
[0/4000][0/600] Loss_D: 1.4583 Loss_G: 0.6733 D(x): 0.4899 D(G(z)): 0.5208 / 0.5122
[0/4000][100/600] Loss_D: 0.8505 Loss_G: 1.0397 D(x): 0.7121 D(G(z)): 0.3881 / 0.3562
[0/4000][200/600] Loss_D: 0.7463 Loss_G: 1.4093 D(x): 0.6796 D(G(z)): 0.2835 / 0.2488
[0/4000][300/600] Loss_D: 0.4877 Loss_G: 1.7799 D(x): 0.7802 D(G(z)): 0.2050 / 0.1744
[0/4000][400/600] Loss_D: 0.2743 Loss_G: 2.1677 D(x): 0.8965 D(G(z)): 0.1476 / 0.1226
[0/4000][500/600] Loss_D: 0.2653 Loss_G: 2.4174 D(x): 0.8809 D(G(z)): 0.1229 / 0.0918
[1/4000][0/600] Loss_D: 0.3351 Loss_G: 2.3313 D(x): 0.8596 D(G(z)): 0.1513 / 0.1084
[1/4000][100/600] Loss_D: 0.1897 Loss_G: 2.5091 D(x): 0.9367 D(G(z)): 0.1142 / 0.0856
[1/4000][200/600] Loss_D: 0.1651 Loss_G: 3.0968 D(x): 0.9173 D(G(z)): 0.0717 / 0.0505
[1/4000][300/600] Loss_D: 0.1211 Loss_G: 3.2050 D(x): 0.9333 D(G(z)): 0.0482 / 0.0489
[1/4000][400/600] Loss_D: 0.0623 Loss_G: 3.7729 D(x): 0.9775 D(G(z)): 0.0377 / 0.0286
[1/4000][500/600] Loss_D: 0.0437 Loss_G: 4.2053 D(x): 0.9774 D(G(z)): 0.0203 / 0.0169
[2/4000][0/600] Loss_D: 0.0459 Loss_G: 5.1992 D(x): 0.9650 D(G(z)): 0.0093 / 0.0063
[2/4000][100/600] Loss_D: 0.0269 Loss_G: 4.5801 D(x): 0.9886 D(G(z)): 0.0151 / 0.0134
[2/4000][200/600] Loss_D: 0.0290 Loss_G: 4.7458 D(x): 0.9847 D(G(z)): 0.0132 / 0.0110
[2/4000][300/600] Loss_D: 0.0333 Loss_G: 4.4435 D(x): 0.9858 D(G(z)): 0.0186 / 0.0139
[2/4000][400/600] Loss_D: 0.0148 Loss_G: 5.9011 D(x): 0.9900 D(G(z)): 0.0047 / 0.0037
[2/4000][500/600] Loss_D: 0.0576 Loss_G: 4.2954 D(x): 0.9596 D(G(z)): 0.0144 / 0.0174
[3/4000][0/600] Loss_D: 0.0207 Loss_G: 4.9039 D(x): 0.9907 D(G(z)): 0.0111 / 0.0091
[3/4000][100/600] Loss_D: 0.0168 Loss_G: 5.0898 D(x): 0.9933 D(G(z)): 0.0100 / 0.0080
[3/4000][200/600] Loss_D: 0.0184 Loss_G: 5.3518 D(x): 0.9884 D(G(z)): 0.0063 / 0.0059
[3/4000][300/600] Loss_D: 0.0375 Loss_G: 4.2995 D(x): 0.9820 D(G(z)): 0.0171 / 0.0172
[3/4000][400/600] Loss_D: 0.0247 Loss_G: 4.5011 D(x): 0.9957 D(G(z)): 0.0200 / 0.0136
[3/4000][500/600] Loss_D: 0.0480 Loss_G: 4.9786 D(x): 0.9663 D(G(z)): 0.0113 / 0.0095
[4/4000][0/600] Loss_D: 0.0281 Loss_G: 4.7039 D(x): 0.9857 D(G(z)): 0.0129 / 0.0124
[4/4000][100/600] Loss_D: 0.0160 Loss_G: 5.3887 D(x): 0.9950 D(G(z)): 0.0109 / 0.0056
[4/4000][200/600] Loss_D: 0.0120 Loss_G: 5.2138 D(x): 0.9962 D(G(z)): 0.0081 / 0.0074
[4/4000][300/600] Loss_D: 0.0267 Loss_G: 5.4105 D(x): 0.9807 D(G(z)): 0.0061 / 0.0061
[4/4000][400/600] Loss_D: 0.0314 Loss_G: 4.5322 D(x): 0.9854 D(G(z)): 0.0159 / 0.0145
[4/4000][500/600] Loss_D: 0.0316 Loss_G: 4.9962 D(x): 0.9869 D(G(z)): 0.0178 / 0.0099
[5/4000][0/600] Loss_D: 0.0157 Loss_G: 5.4832 D(x): 0.9948 D(G(z)): 0.0103 / 0.0051
[5/4000][100/600] Loss_D: 0.0111 Loss_G: 5.5713 D(x): 0.9966 D(G(z)): 0.0076 / 0.0053
[5/4000][200/600] Loss_D: 0.0496 Loss_G: 7.2147 D(x): 0.9660 D(G(z)): 0.0011 / 0.0010
[5/4000][300/600] Loss_D: 0.0163 Loss_G: 6.0031 D(x): 0.9882 D(G(z)): 0.0039 / 0.0035
[5/4000][400/600] Loss_D: 0.0257 Loss_G: 5.1657 D(x): 0.9892 D(G(z)): 0.0143 / 0.0076
[5/4000][500/600] Loss_D: 0.0170 Loss_G: 4.9780 D(x): 0.9918 D(G(z)): 0.0084 / 0.0106
[6/4000][0/600] Loss_D: 0.0147 Loss_G: 5.3444 D(x): 0.9931 D(G(z)): 0.0075 / 0.0069
[6/4000][100/600] Loss_D: 0.0085 Loss_G: 6.4237 D(x): 0.9955 D(G(z)): 0.0035 / 0.0024
[6/4000][200/600] Loss_D: 0.0083 Loss_G: 5.8357 D(x): 0.9984 D(G(z)): 0.0066 / 0.0043
[6/4000][300/600] Loss_D: 0.0302 Loss_G: 6.3633 D(x): 0.9740 D(G(z)): 0.0013 / 0.0023
[6/4000][400/600] Loss_D: 0.0168 Loss_G: 5.3458 D(x): 0.9918 D(G(z)): 0.0072 / 0.0062
[6/4000][500/600] Loss_D: 0.0123 Loss_G: 5.5281 D(x): 0.9934 D(G(z)): 0.0056 / 0.0050
[7/4000][0/600] Loss_D: 0.0195 Loss_G: 4.1918 D(x): 0.9977 D(G(z)): 0.0169 / 0.0198
[7/4000][100/600] Loss_D: 0.0139 Loss_G: 6.9196 D(x): 0.9890 D(G(z)): 0.0021 / 0.0014
[7/4000][200/600] Loss_D: 0.0076 Loss_G: 5.8415 D(x): 0.9980 D(G(z)): 0.0055 / 0.0045
[7/4000][300/600] Loss_D: 0.0112 Loss_G: 6.0420 D(x): 0.9913 D(G(z)): 0.0023 / 0.0028
[7/4000][400/600] Loss_D: 0.0084 Loss_G: 6.7065 D(x): 0.9955 D(G(z)): 0.0038 / 0.0017
[7/4000][500/600] Loss_D: 0.0160 Loss_G: 5.0663 D(x): 0.9928 D(G(z)): 0.0083 / 0.0091
[8/4000][0/600] Loss_D: 0.0234 Loss_G: 6.2596 D(x): 0.9807 D(G(z)): 0.0025 / 0.0034
[8/4000][100/600] Loss_D: 0.0208 Loss_G: 4.9983 D(x): 0.9884 D(G(z)): 0.0083 / 0.0095
[8/4000][200/600] Loss_D: 0.0070 Loss_G: 5.8495 D(x): 0.9993 D(G(z)): 0.0063 / 0.0039
[8/4000][300/600] Loss_D: 0.0451 Loss_G: 4.0558 D(x): 0.9906 D(G(z)): 0.0341 / 0.0239
[8/4000][400/600] Loss_D: 0.0500 Loss_G: 4.2978 D(x): 0.9896 D(G(z)): 0.0376 / 0.0190
[8/4000][500/600] Loss_D: 0.0104 Loss_G: 5.6674 D(x): 0.9966 D(G(z)): 0.0068 / 0.0059
[9/4000][0/600] Loss_D: 0.0132 Loss_G: 6.7846 D(x): 0.9893 D(G(z)): 0.0018 / 0.0016
[9/4000][100/600] Loss_D: 0.0064 Loss_G: 6.7528 D(x): 0.9965 D(G(z)): 0.0028 / 0.0017
[9/4000][200/600] Loss_D: 0.0035 Loss_G: 7.1330 D(x): 0.9991 D(G(z)): 0.0025 / 0.0010
[9/4000][300/600] Loss_D: 0.0281 Loss_G: 6.4849 D(x): 0.9753 D(G(z)): 0.0010 / 0.0019
[9/4000][400/600] Loss_D: 0.0322 Loss_G: 4.5061 D(x): 0.9872 D(G(z)): 0.0185 / 0.0144
[9/4000][500/600] Loss_D: 0.0330 Loss_G: 6.4354 D(x): 0.9729 D(G(z)): 0.0028 / 0.0021
[10/4000][0/600] Loss_D: 0.0234 Loss_G: 3.9369 D(x): 0.9966 D(G(z)): 0.0197 / 0.0225
[10/4000][100/600] Loss_D: 0.0333 Loss_G: 5.5747 D(x): 0.9722 D(G(z)): 0.0029 / 0.0045
[10/4000][200/600] Loss_D: 0.0190 Loss_G: 4.6906 D(x): 0.9993 D(G(z)): 0.0180 / 0.0122
[10/4000][300/600] Loss_D: 0.0319 Loss_G: 4.9564 D(x): 0.9848 D(G(z)): 0.0125 / 0.0098
[10/4000][400/600] Loss_D: 0.0400 Loss_G: 3.7710 D(x): 0.9901 D(G(z)): 0.0270 / 0.0260
[10/4000][500/600] Loss_D: 0.0292 Loss_G: 5.3283 D(x): 0.9807 D(G(z)): 0.0060 / 0.0058
[11/4000][0/600] Loss_D: 0.0398 Loss_G: 3.8591 D(x): 0.9935 D(G(z)): 0.0321 / 0.0282
[11/4000][100/600] Loss_D: 0.0221 Loss_G: 5.4589 D(x): 0.9849 D(G(z)): 0.0058 / 0.0049
[11/4000][200/600] Loss_D: 0.0180 Loss_G: 5.8926 D(x): 0.9878 D(G(z)): 0.0040 / 0.0040
[11/4000][300/600] Loss_D: 0.0141 Loss_G: 5.6494 D(x): 0.9903 D(G(z)): 0.0040 / 0.0045
[11/4000][400/600] Loss_D: 0.0296 Loss_G: 4.7325 D(x): 0.9823 D(G(z)): 0.0107 / 0.0121
[11/4000][500/600] Loss_D: 0.0167 Loss_G: 4.8233 D(x): 0.9937 D(G(z)): 0.0100 / 0.0098
[12/4000][0/600] Loss_D: 0.0188 Loss_G: 4.0977 D(x): 0.9964 D(G(z)): 0.0149 / 0.0213
[12/4000][100/600] Loss_D: 0.0353 Loss_G: 5.8658 D(x): 0.9712 D(G(z)): 0.0025 / 0.0041
[12/4000][200/600] Loss_D: 0.0133 Loss_G: 4.8877 D(x): 0.9991 D(G(z)): 0.0123 / 0.0091
[12/4000][300/600] Loss_D: 0.0351 Loss_G: 3.5154 D(x): 0.9908 D(G(z)): 0.0250 / 0.0394
[12/4000][400/600] Loss_D: 0.0246 Loss_G: 4.4346 D(x): 0.9955 D(G(z)): 0.0197 / 0.0145
[12/4000][500/600] Loss_D: 0.0230 Loss_G: 6.3850 D(x): 0.9887 D(G(z)): 0.0100 / 0.0023
[13/4000][0/600] Loss_D: 0.0183 Loss_G: 6.4122 D(x): 0.9862 D(G(z)): 0.0033 / 0.0021
[13/4000][100/600] Loss_D: 0.0346 Loss_G: 7.1310 D(x): 0.9696 D(G(z)): 0.0010 / 0.0010
[13/4000][200/600] Loss_D: 0.0207 Loss_G: 5.4381 D(x): 0.9912 D(G(z)): 0.0104 / 0.0054
[13/4000][300/600] Loss_D: 0.0244 Loss_G: 6.1792 D(x): 0.9798 D(G(z)): 0.0034 / 0.0033
[13/4000][400/600] Loss_D: 0.0052 Loss_G: 6.2734 D(x): 0.9986 D(G(z)): 0.0037 / 0.0024
[13/4000][500/600] Loss_D: 0.0405 Loss_G: 5.2515 D(x): 0.9736 D(G(z)): 0.0038 / 0.0070
[14/4000][0/600] Loss_D: 0.0437 Loss_G: 3.6655 D(x): 0.9936 D(G(z)): 0.0351 / 0.0417
[14/4000][100/600] Loss_D: 0.0170 Loss_G: 7.8187 D(x): 0.9856 D(G(z)): 0.0008 / 0.0006
[14/4000][200/600] Loss_D: 0.0207 Loss_G: 5.9847 D(x): 0.9874 D(G(z)): 0.0065 / 0.0033
[14/4000][300/600] Loss_D: 0.0251 Loss_G: 5.0931 D(x): 0.9844 D(G(z)): 0.0079 / 0.0103
[14/4000][400/600] Loss_D: 0.0294 Loss_G: 4.3311 D(x): 0.9894 D(G(z)): 0.0170 / 0.0167
[14/4000][500/600] Loss_D: 0.0527 Loss_G: 4.3634 D(x): 0.9655 D(G(z)): 0.0107 / 0.0159
[15/4000][0/600] Loss_D: 0.0182 Loss_G: 4.5039 D(x): 0.9928 D(G(z)): 0.0103 / 0.0130
[15/4000][100/600] Loss_D: 0.0152 Loss_G: 5.0779 D(x): 0.9979 D(G(z)): 0.0129 / 0.0091
[15/4000][200/600] Loss_D: 0.0089 Loss_G: 5.9251 D(x): 0.9948 D(G(z)): 0.0035 / 0.0037
[15/4000][300/600] Loss_D: 0.0150 Loss_G: 5.8004 D(x): 0.9885 D(G(z)): 0.0030 / 0.0036
[15/4000][400/600] Loss_D: 0.0481 Loss_G: 4.1151 D(x): 0.9982 D(G(z)): 0.0444 / 0.0214
[15/4000][500/600] Loss_D: 0.0374 Loss_G: 3.8988 D(x): 0.9921 D(G(z)): 0.0286 / 0.0263
[16/4000][0/600] Loss_D: 0.0167 Loss_G: 5.8045 D(x): 0.9914 D(G(z)): 0.0077 / 0.0036
[16/4000][100/600] Loss_D: 0.0135 Loss_G: 6.2537 D(x): 0.9913 D(G(z)): 0.0038 / 0.0024
[16/4000][200/600] Loss_D: 0.0278 Loss_G: 4.4945 D(x): 0.9992 D(G(z)): 0.0266 / 0.0124
[16/4000][300/600] Loss_D: 0.0314 Loss_G: 3.8853 D(x): 0.9917 D(G(z)): 0.0191 / 0.0263
[16/4000][400/600] Loss_D: 0.0183 Loss_G: 5.9444 D(x): 0.9889 D(G(z)): 0.0063 / 0.0031
[16/4000][500/600] Loss_D: 0.0129 Loss_G: 5.4947 D(x): 0.9972 D(G(z)): 0.0100 / 0.0055
[17/4000][0/600] Loss_D: 0.0167 Loss_G: 4.2397 D(x): 0.9957 D(G(z)): 0.0122 / 0.0176
[17/4000][100/600] Loss_D: 0.0134 Loss_G: 6.7091 D(x): 0.9904 D(G(z)): 0.0027 / 0.0014
[17/4000][200/600] Loss_D: 0.0096 Loss_G: 5.5225 D(x): 0.9992 D(G(z)): 0.0087 / 0.0054
[17/4000][300/600] Loss_D: 0.0135 Loss_G: 5.8190 D(x): 0.9934 D(G(z)): 0.0058 / 0.0040
[17/4000][400/600] Loss_D: 0.0163 Loss_G: 5.0729 D(x): 0.9993 D(G(z)): 0.0155 / 0.0072
[17/4000][500/600] Loss_D: 0.0065 Loss_G: 6.5167 D(x): 0.9958 D(G(z)): 0.0022 / 0.0022
[18/4000][0/600] Loss_D: 0.0049 Loss_G: 6.0791 D(x): 0.9988 D(G(z)): 0.0037 / 0.0033
[18/4000][100/600] Loss_D: 0.0232 Loss_G: 6.8614 D(x): 0.9819 D(G(z)): 0.0015 / 0.0013
[18/4000][200/600] Loss_D: 0.0053 Loss_G: 5.5069 D(x): 0.9986 D(G(z)): 0.0039 / 0.0046
[18/4000][300/600] Loss_D: 0.0309 Loss_G: 5.2258 D(x): 0.9783 D(G(z)): 0.0059 / 0.0080
[18/4000][400/600] Loss_D: 0.0350 Loss_G: 4.5266 D(x): 0.9811 D(G(z)): 0.0115 / 0.0131
[18/4000][500/600] Loss_D: 0.0499 Loss_G: 5.1939 D(x): 0.9721 D(G(z)): 0.0051 / 0.0078
[19/4000][0/600] Loss_D: 0.0232 Loss_G: 4.9324 D(x): 0.9994 D(G(z)): 0.0222 / 0.0095
[19/4000][100/600] Loss_D: 0.0053 Loss_G: 6.9558 D(x): 0.9976 D(G(z)): 0.0029 / 0.0016
[19/4000][200/600] Loss_D: 0.0279 Loss_G: 5.9954 D(x): 0.9770 D(G(z)): 0.0012 / 0.0031
[19/4000][300/600] Loss_D: 0.0277 Loss_G: 4.4582 D(x): 0.9816 D(G(z)): 0.0065 / 0.0145
[19/4000][400/600] Loss_D: 0.0134 Loss_G: 5.1444 D(x): 0.9975 D(G(z)): 0.0109 / 0.0070
[19/4000][500/600] Loss_D: 0.0214 Loss_G: 5.0411 D(x): 0.9913 D(G(z)): 0.0120 / 0.0105
[20/4000][0/600] Loss_D: 0.0121 Loss_G: 5.5655 D(x): 0.9936 D(G(z)): 0.0055 / 0.0057
[20/4000][100/600] Loss_D: 0.0115 Loss_G: 6.3353 D(x): 0.9918 D(G(z)): 0.0031 / 0.0022
[20/4000][200/600] Loss_D: 0.0087 Loss_G: 6.3995 D(x): 0.9949 D(G(z)): 0.0034 / 0.0019
[20/4000][300/600] Loss_D: 0.0085 Loss_G: 5.0294 D(x): 0.9974 D(G(z)): 0.0058 / 0.0097
[20/4000][400/600] Loss_D: 0.0109 Loss_G: 6.2946 D(x): 0.9919 D(G(z)): 0.0025 / 0.0023
[20/4000][500/600] Loss_D: 0.0184 Loss_G: 5.2594 D(x): 0.9905 D(G(z)): 0.0077 / 0.0068
[21/4000][0/600] Loss_D: 0.0140 Loss_G: 5.2731 D(x): 0.9943 D(G(z)): 0.0081 / 0.0065
[21/4000][100/600] Loss_D: 0.0081 Loss_G: 5.5001 D(x): 0.9975 D(G(z)): 0.0055 / 0.0048
[21/4000][200/600] Loss_D: 0.0173 Loss_G: 5.7054 D(x): 0.9944 D(G(z)): 0.0111 / 0.0038
[21/4000][300/600] Loss_D: 0.0139 Loss_G: 4.9281 D(x): 0.9915 D(G(z)): 0.0049 / 0.0083
[21/4000][400/600] Loss_D: 0.0674 Loss_G: 4.4146 D(x): 0.9956 D(G(z)): 0.0568 / 0.0174
[21/4000][500/600] Loss_D: 0.0137 Loss_G: 5.1917 D(x): 0.9937 D(G(z)): 0.0067 / 0.0081
[22/4000][0/600] Loss_D: 0.0129 Loss_G: 5.2115 D(x): 0.9945 D(G(z)): 0.0070 / 0.0064
[22/4000][100/600] Loss_D: 0.0186 Loss_G: 5.6644 D(x): 0.9902 D(G(z)): 0.0064 / 0.0045
[22/4000][200/600] Loss_D: 0.0098 Loss_G: 5.7732 D(x): 0.9992 D(G(z)): 0.0089 / 0.0075
[22/4000][300/600] Loss_D: 0.0519 Loss_G: 5.3750 D(x): 0.9608 D(G(z)): 0.0016 / 0.0065
[22/4000][400/600] Loss_D: 0.0364 Loss_G: 3.2274 D(x): 0.9995 D(G(z)): 0.0350 / 0.0514
[22/4000][500/600] Loss_D: 0.0151 Loss_G: 5.7490 D(x): 0.9937 D(G(z)): 0.0085 / 0.0038
[23/4000][0/600] Loss_D: 0.0297 Loss_G: 5.4422 D(x): 0.9793 D(G(z)): 0.0059 / 0.0054
[23/4000][100/600] Loss_D: 0.0224 Loss_G: 5.7041 D(x): 0.9836 D(G(z)): 0.0037 / 0.0040
[23/4000][200/600] Loss_D: 0.0058 Loss_G: 7.5284 D(x): 0.9953 D(G(z)): 0.0010 / 0.0007
[23/4000][300/600] Loss_D: 0.0215 Loss_G: 4.1006 D(x): 0.9965 D(G(z)): 0.0177 / 0.0218
[23/4000][400/600] Loss_D: 0.0155 Loss_G: 5.9333 D(x): 0.9971 D(G(z)): 0.0123 / 0.0050
[23/4000][500/600] Loss_D: 0.0201 Loss_G: 3.9720 D(x): 0.9945 D(G(z)): 0.0143 / 0.0240
[24/4000][0/600] Loss_D: 0.0170 Loss_G: 5.2324 D(x): 0.9912 D(G(z)): 0.0074 / 0.0064
[24/4000][100/600] Loss_D: 0.0267 Loss_G: 8.6456 D(x): 0.9799 D(G(z)): 0.0003 / 0.0003
[24/4000][200/600] Loss_D: 0.0109 Loss_G: 5.6551 D(x): 0.9989 D(G(z)): 0.0097 / 0.0051
[24/4000][300/600] Loss_D: 0.0309 Loss_G: 3.8027 D(x): 0.9934 D(G(z)): 0.0237 / 0.0324
[24/4000][400/600] Loss_D: 0.0356 Loss_G: 4.2823 D(x): 0.9860 D(G(z)): 0.0186 / 0.0174
[24/4000][500/600] Loss_D: 0.0325 Loss_G: 5.3249 D(x): 0.9799 D(G(z)): 0.0054 / 0.0056
[25/4000][0/600] Loss_D: 0.0327 Loss_G: 3.3077 D(x): 0.9964 D(G(z)): 0.0282 / 0.0449
[25/4000][100/600] Loss_D: 0.0053 Loss_G: 7.0892 D(x): 0.9968 D(G(z)): 0.0020 / 0.0011
[25/4000][200/600] Loss_D: 0.0214 Loss_G: 6.0966 D(x): 0.9883 D(G(z)): 0.0083 / 0.0030
[25/4000][300/600] Loss_D: 0.0413 Loss_G: 5.5926 D(x): 0.9742 D(G(z)): 0.0077 / 0.0053
[25/4000][400/600] Loss_D: 0.0234 Loss_G: 4.7018 D(x): 0.9954 D(G(z)): 0.0183 / 0.0117
[25/4000][500/600] Loss_D: 0.0521 Loss_G: 5.0089 D(x): 0.9693 D(G(z)): 0.0157 / 0.0099
[26/4000][0/600] Loss_D: 0.0190 Loss_G: 5.3465 D(x): 0.9863 D(G(z)): 0.0044 / 0.0059
[26/4000][100/600] Loss_D: 0.0425 Loss_G: 7.0930 D(x): 0.9665 D(G(z)): 0.0017 / 0.0012
[26/4000][200/600] Loss_D: 0.0194 Loss_G: 5.5221 D(x): 0.9931 D(G(z)): 0.0118 / 0.0048
[26/4000][300/600] Loss_D: 0.0133 Loss_G: 5.3891 D(x): 0.9940 D(G(z)): 0.0068 / 0.0065
[26/4000][400/600] Loss_D: 0.0240 Loss_G: 4.9055 D(x): 0.9934 D(G(z)): 0.0158 / 0.0114
[26/4000][500/600] Loss_D: 0.0371 Loss_G: 4.3043 D(x): 0.9917 D(G(z)): 0.0263 / 0.0225
[27/4000][0/600] Loss_D: 0.0348 Loss_G: 3.8016 D(x): 0.9926 D(G(z)): 0.0263 / 0.0313
[27/4000][100/600] Loss_D: 0.0379 Loss_G: 5.4233 D(x): 0.9725 D(G(z)): 0.0048 / 0.0064
[27/4000][200/600] Loss_D: 0.0123 Loss_G: 6.8725 D(x): 0.9914 D(G(z)): 0.0034 / 0.0012
[27/4000][300/600] Loss_D: 0.0133 Loss_G: 4.9791 D(x): 0.9994 D(G(z)): 0.0126 / 0.0093
[27/4000][400/600] Loss_D: 0.0156 Loss_G: 5.9467 D(x): 0.9913 D(G(z)): 0.0065 / 0.0034
[27/4000][500/600] Loss_D: 0.0393 Loss_G: 6.3075 D(x): 0.9771 D(G(z)): 0.0022 / 0.0023
[28/4000][0/600] Loss_D: 0.0497 Loss_G: 4.9014 D(x): 0.9813 D(G(z)): 0.0260 / 0.0098
[28/4000][100/600] Loss_D: 0.0478 Loss_G: 4.9777 D(x): 0.9834 D(G(z)): 0.0146 / 0.0091
[28/4000][200/600] Loss_D: 0.0239 Loss_G: 5.1270 D(x): 0.9917 D(G(z)): 0.0150 / 0.0086
[28/4000][300/600] Loss_D: 0.0304 Loss_G: 4.5994 D(x): 0.9796 D(G(z)): 0.0071 / 0.0172
[28/4000][400/600] Loss_D: 0.0268 Loss_G: 4.6256 D(x): 0.9975 D(G(z)): 0.0238 / 0.0132
[28/4000][500/600] Loss_D: 0.0107 Loss_G: 5.9704 D(x): 0.9928 D(G(z)): 0.0032 / 0.0039
[29/4000][0/600] Loss_D: 0.1413 Loss_G: 9.8566 D(x): 0.9076 D(G(z)): 0.0001 / 0.0001
[29/4000][100/600] Loss_D: 0.0153 Loss_G: 5.4854 D(x): 0.9944 D(G(z)): 0.0093 / 0.0069
[29/4000][200/600] Loss_D: 0.0221 Loss_G: 4.2034 D(x): 0.9963 D(G(z)): 0.0181 / 0.0192
[29/4000][300/600] Loss_D: 0.0285 Loss_G: 4.5905 D(x): 0.9897 D(G(z)): 0.0174 / 0.0134
[29/4000][400/600] Loss_D: 0.0177 Loss_G: 6.4779 D(x): 0.9873 D(G(z)): 0.0045 / 0.0023
[29/4000][500/600] Loss_D: 0.0540 Loss_G: 4.7669 D(x): 0.9737 D(G(z)): 0.0119 / 0.0121
[30/4000][0/600] Loss_D: 0.0684 Loss_G: 3.5928 D(x): 0.9983 D(G(z)): 0.0617 / 0.0427
[30/4000][100/600] Loss_D: 0.0346 Loss_G: 5.8301 D(x): 0.9790 D(G(z)): 0.0069 / 0.0040
[30/4000][200/600] Loss_D: 0.0103 Loss_G: 6.6071 D(x): 0.9924 D(G(z)): 0.0025 / 0.0016
[30/4000][300/600] Loss_D: 0.3093 Loss_G: 3.8214 D(x): 0.9918 D(G(z)): 0.2230 / 0.0400
[30/4000][400/600] Loss_D: 0.0598 Loss_G: 5.1490 D(x): 0.9685 D(G(z)): 0.0165 / 0.0079
[30/4000][500/600] Loss_D: 0.2032 Loss_G: 5.5491 D(x): 0.8794 D(G(z)): 0.0023 / 0.0073
[31/4000][0/600] Loss_D: 0.3519 Loss_G: 8.7604 D(x): 0.8185 D(G(z)): 0.0002 / 0.0002
[31/4000][100/600] Loss_D: 0.0297 Loss_G: 5.8127 D(x): 0.9834 D(G(z)): 0.0107 / 0.0035
[31/4000][200/600] Loss_D: 0.0363 Loss_G: 5.0433 D(x): 0.9825 D(G(z)): 0.0168 / 0.0087
[31/4000][300/600] Loss_D: 0.1372 Loss_G: 4.8375 D(x): 0.9099 D(G(z)): 0.0056 / 0.0143
[31/4000][400/600] Loss_D: 0.0482 Loss_G: 5.4184 D(x): 0.9742 D(G(z)): 0.0129 / 0.0065
[31/4000][500/600] Loss_D: 0.0124 Loss_G: 5.5754 D(x): 0.9950 D(G(z)): 0.0071 / 0.0060
[32/4000][0/600] Loss_D: 0.0533 Loss_G: 3.6932 D(x): 0.9795 D(G(z)): 0.0271 / 0.0456
[32/4000][100/600] Loss_D: 0.0232 Loss_G: 4.5650 D(x): 0.9920 D(G(z)): 0.0133 / 0.0135
[32/4000][200/600] Loss_D: 0.0148 Loss_G: 5.3049 D(x): 0.9989 D(G(z)): 0.0134 / 0.0074
[32/4000][300/600] Loss_D: 0.0732 Loss_G: 4.7389 D(x): 0.9740 D(G(z)): 0.0164 / 0.0116
[32/4000][400/600] Loss_D: 0.0513 Loss_G: 6.6460 D(x): 0.9592 D(G(z)): 0.0031 / 0.0017
[32/4000][500/600] Loss_D: 0.1010 Loss_G: 4.3334 D(x): 0.9533 D(G(z)): 0.0141 / 0.0149
[33/4000][0/600] Loss_D: 0.0777 Loss_G: 3.4117 D(x): 0.9807 D(G(z)): 0.0384 / 0.0466
[33/4000][100/600] Loss_D: 0.0746 Loss_G: 5.9954 D(x): 0.9436 D(G(z)): 0.0060 / 0.0042
[33/4000][200/600] Loss_D: 0.0322 Loss_G: 4.9772 D(x): 0.9902 D(G(z)): 0.0193 / 0.0087
[33/4000][300/600] Loss_D: 0.0297 Loss_G: 4.8024 D(x): 0.9937 D(G(z)): 0.0228 / 0.0112
[33/4000][400/600] Loss_D: 0.0661 Loss_G: 3.5844 D(x): 0.9857 D(G(z)): 0.0478 / 0.0423
[33/4000][500/600] Loss_D: 0.0497 Loss_G: 5.0600 D(x): 0.9705 D(G(z)): 0.0132 / 0.0088
[34/4000][0/600] Loss_D: 0.1098 Loss_G: 3.1484 D(x): 0.9857 D(G(z)): 0.0857 / 0.0605
[34/4000][100/600] Loss_D: 0.0290 Loss_G: 5.1954 D(x): 0.9867 D(G(z)): 0.0138 / 0.0090
[34/4000][200/600] Loss_D: 0.0363 Loss_G: 7.2023 D(x): 0.9741 D(G(z)): 0.0031 / 0.0011
[34/4000][300/600] Loss_D: 0.0575 Loss_G: 4.7596 D(x): 0.9635 D(G(z)): 0.0119 / 0.0120
[34/4000][400/600] Loss_D: 0.0911 Loss_G: 6.5178 D(x): 0.9323 D(G(z)): 0.0031 / 0.0027
[34/4000][500/600] Loss_D: 0.0475 Loss_G: 6.3898 D(x): 0.9625 D(G(z)): 0.0030 / 0.0026
[35/4000][0/600] Loss_D: 0.0608 Loss_G: 3.9193 D(x): 0.9819 D(G(z)): 0.0388 / 0.0292
[35/4000][100/600] Loss_D: 0.1373 Loss_G: 4.7893 D(x): 0.9433 D(G(z)): 0.0122 / 0.0127
[35/4000][200/600] Loss_D: 0.0549 Loss_G: 5.3084 D(x): 0.9784 D(G(z)): 0.0069 / 0.0063
[35/4000][300/600] Loss_D: 0.0636 Loss_G: 6.5685 D(x): 0.9549 D(G(z)): 0.0043 / 0.0025
[35/4000][400/600] Loss_D: 0.0965 Loss_G: 4.6090 D(x): 0.9521 D(G(z)): 0.0233 / 0.0171
[35/4000][500/600] Loss_D: 0.0243 Loss_G: 6.3961 D(x): 0.9830 D(G(z)): 0.0063 / 0.0030
[36/4000][0/600] Loss_D: 0.0328 Loss_G: 6.4059 D(x): 0.9785 D(G(z)): 0.0084 / 0.0026
[36/4000][100/600] Loss_D: 0.0289 Loss_G: 4.8943 D(x): 0.9927 D(G(z)): 0.0204 / 0.0135
[36/4000][200/600] Loss_D: 0.0235 Loss_G: 4.9015 D(x): 0.9964 D(G(z)): 0.0194 / 0.0115
[36/4000][300/600] Loss_D: 0.0784 Loss_G: 6.5640 D(x): 0.9577 D(G(z)): 0.0023 / 0.0019
[36/4000][400/600] Loss_D: 0.0524 Loss_G: 4.0011 D(x): 0.9941 D(G(z)): 0.0441 / 0.0288
[36/4000][500/600] Loss_D: 0.0226 Loss_G: 5.9078 D(x): 0.9849 D(G(z)): 0.0063 / 0.0049
[37/4000][0/600] Loss_D: 0.0552 Loss_G: 5.4299 D(x): 0.9700 D(G(z)): 0.0157 / 0.0071
[37/4000][100/600] Loss_D: 0.0287 Loss_G: 5.7458 D(x): 0.9853 D(G(z)): 0.0109 / 0.0080
[37/4000][200/600] Loss_D: 0.0791 Loss_G: 7.1558 D(x): 0.9424 D(G(z)): 0.0015 / 0.0014
[37/4000][300/600] Loss_D: 0.0782 Loss_G: 4.6399 D(x): 0.9706 D(G(z)): 0.0115 / 0.0157
[37/4000][400/600] Loss_D: 0.0399 Loss_G: 4.3240 D(x): 0.9837 D(G(z)): 0.0216 / 0.0178
[37/4000][500/600] Loss_D: 0.0774 Loss_G: 5.2213 D(x): 0.9571 D(G(z)): 0.0070 / 0.0081
[38/4000][0/600] Loss_D: 0.0451 Loss_G: 4.7974 D(x): 0.9768 D(G(z)): 0.0114 / 0.0112
[38/4000][100/600] Loss_D: 0.0216 Loss_G: 4.4794 D(x): 0.9973 D(G(z)): 0.0186 / 0.0162
[38/4000][200/600] Loss_D: 0.0219 Loss_G: 6.7290 D(x): 0.9871 D(G(z)): 0.0075 / 0.0024
[38/4000][300/600] Loss_D: 0.0500 Loss_G: 5.6503 D(x): 0.9631 D(G(z)): 0.0097 / 0.0081
[38/4000][400/600] Loss_D: 0.1088 Loss_G: 5.5792 D(x): 0.9450 D(G(z)): 0.0081 / 0.0064
[38/4000][500/600] Loss_D: 0.0508 Loss_G: 5.1057 D(x): 0.9586 D(G(z)): 0.0039 / 0.0083
[39/4000][0/600] Loss_D: 0.0660 Loss_G: 5.1460 D(x): 0.9667 D(G(z)): 0.0178 / 0.0090
[39/4000][100/600] Loss_D: 0.0277 Loss_G: 5.0814 D(x): 0.9888 D(G(z)): 0.0157 / 0.0088
[39/4000][200/600] Loss_D: 0.1090 Loss_G: 3.2656 D(x): 0.9964 D(G(z)): 0.0961 / 0.0513
[39/4000][300/600] Loss_D: 0.3058 Loss_G: 2.0767 D(x): 0.9887 D(G(z)): 0.2236 / 0.1700
[39/4000][400/600] Loss_D: 0.0496 Loss_G: 6.5793 D(x): 0.9718 D(G(z)): 0.0064 / 0.0021
[39/4000][500/600] Loss_D: 0.0717 Loss_G: 4.4750 D(x): 0.9591 D(G(z)): 0.0113 / 0.0160
[40/4000][0/600] Loss_D: 0.0239 Loss_G: 7.6247 D(x): 0.9811 D(G(z)): 0.0030 / 0.0009
[40/4000][100/600] Loss_D: 0.0335 Loss_G: 5.3945 D(x): 0.9854 D(G(z)): 0.0147 / 0.0066
[40/4000][200/600] Loss_D: 0.0498 Loss_G: 5.5435 D(x): 0.9831 D(G(z)): 0.0270 / 0.0065
[40/4000][300/600] Loss_D: 0.0274 Loss_G: 7.4191 D(x): 0.9791 D(G(z)): 0.0024 / 0.0008
[40/4000][400/600] Loss_D: 0.0488 Loss_G: 5.7045 D(x): 0.9718 D(G(z)): 0.0160 / 0.0060
[40/4000][500/600] Loss_D: 0.0651 Loss_G: 4.4725 D(x): 0.9640 D(G(z)): 0.0155 / 0.0170
[41/4000][0/600] Loss_D: 0.0761 Loss_G: 4.1495 D(x): 0.9744 D(G(z)): 0.0387 / 0.0226
[41/4000][100/600] Loss_D: 0.0398 Loss_G: 4.8696 D(x): 0.9769 D(G(z)): 0.0138 / 0.0111
[41/4000][200/600] Loss_D: 0.0382 Loss_G: 5.5100 D(x): 0.9933 D(G(z)): 0.0296 / 0.0088
[41/4000][300/600] Loss_D: 0.1428 Loss_G: 4.3012 D(x): 0.9476 D(G(z)): 0.0240 / 0.0221
[41/4000][400/600] Loss_D: 0.1584 Loss_G: 6.3666 D(x): 0.9022 D(G(z)): 0.0035 / 0.0028
[41/4000][500/600] Loss_D: 0.0245 Loss_G: 5.0860 D(x): 0.9975 D(G(z)): 0.0213 / 0.0114
[42/4000][0/600] Loss_D: 0.1870 Loss_G: 9.1120 D(x): 0.8883 D(G(z)): 0.0002 / 0.0002
[42/4000][100/600] Loss_D: 0.0975 Loss_G: 7.5505 D(x): 0.9356 D(G(z)): 0.0015 / 0.0007
[42/4000][200/600] Loss_D: 0.0616 Loss_G: 3.8727 D(x): 0.9839 D(G(z)): 0.0426 / 0.0297
[42/4000][300/600] Loss_D: 0.0490 Loss_G: 5.3011 D(x): 0.9665 D(G(z)): 0.0088 / 0.0067
[42/4000][400/600] Loss_D: 0.0937 Loss_G: 4.2388 D(x): 0.9613 D(G(z)): 0.0356 / 0.0199
[42/4000][500/600] Loss_D: 0.0725 Loss_G: 3.8486 D(x): 0.9664 D(G(z)): 0.0301 / 0.0300
[43/4000][0/600] Loss_D: 0.0413 Loss_G: 5.7884 D(x): 0.9781 D(G(z)): 0.0152 / 0.0060
[43/4000][100/600] Loss_D: 0.0900 Loss_G: 4.5424 D(x): 0.9640 D(G(z)): 0.0292 / 0.0170
[43/4000][200/600] Loss_D: 0.0427 Loss_G: 4.7969 D(x): 0.9866 D(G(z)): 0.0269 / 0.0121
[43/4000][300/600] Loss_D: 0.0813 Loss_G: 4.5771 D(x): 0.9551 D(G(z)): 0.0090 / 0.0129
[43/4000][400/600] Loss_D: 0.1272 Loss_G: 3.3563 D(x): 0.9903 D(G(z)): 0.1026 / 0.0472
[43/4000][500/600] Loss_D: 0.0087 Loss_G: 6.4952 D(x): 0.9943 D(G(z)): 0.0029 / 0.0021
[44/4000][0/600] Loss_D: 0.0997 Loss_G: 4.6448 D(x): 0.9417 D(G(z)): 0.0139 / 0.0115
[44/4000][100/600] Loss_D: 0.0420 Loss_G: 5.1208 D(x): 0.9750 D(G(z)): 0.0129 / 0.0096
[44/4000][200/600] Loss_D: 0.0694 Loss_G: 4.0479 D(x): 0.9982 D(G(z)): 0.0616 / 0.0267
[44/4000][300/600] Loss_D: 0.1015 Loss_G: 4.2735 D(x): 0.9419 D(G(z)): 0.0228 / 0.0205
[44/4000][400/600] Loss_D: 0.1619 Loss_G: 5.6287 D(x): 0.9178 D(G(z)): 0.0083 / 0.0074
[44/4000][500/600] Loss_D: 0.0398 Loss_G: 5.0423 D(x): 0.9762 D(G(z)): 0.0112 / 0.0123
[45/4000][0/600] Loss_D: 0.0255 Loss_G: 5.1073 D(x): 0.9970 D(G(z)): 0.0220 / 0.0094
[45/4000][100/600] Loss_D: 0.0307 Loss_G: 5.1965 D(x): 0.9869 D(G(z)): 0.0144 / 0.0080
[45/4000][200/600] Loss_D: 0.2302 Loss_G: 5.8453 D(x): 0.8751 D(G(z)): 0.0047 / 0.0098
[45/4000][300/600] Loss_D: 0.0363 Loss_G: 4.4610 D(x): 0.9917 D(G(z)): 0.0263 / 0.0165
[45/4000][400/600] Loss_D: 0.1060 Loss_G: 4.9406 D(x): 0.9546 D(G(z)): 0.0119 / 0.0101
[45/4000][500/600] Loss_D: 0.0294 Loss_G: 6.1611 D(x): 0.9791 D(G(z)): 0.0032 / 0.0027
[46/4000][0/600] Loss_D: 0.0635 Loss_G: 3.4131 D(x): 0.9957 D(G(z)): 0.0559 / 0.0450
[46/4000][100/600] Loss_D: 0.0460 Loss_G: 4.1045 D(x): 0.9985 D(G(z)): 0.0416 / 0.0272
[46/4000][200/600] Loss_D: 0.0356 Loss_G: 7.9512 D(x): 0.9760 D(G(z)): 0.0017 / 0.0005
[46/4000][300/600] Loss_D: 0.0639 Loss_G: 6.0574 D(x): 0.9529 D(G(z)): 0.0036 / 0.0039
[46/4000][400/600] Loss_D: 0.0247 Loss_G: 4.8915 D(x): 0.9879 D(G(z)): 0.0084 / 0.0093
[46/4000][500/600] Loss_D: 0.0755 Loss_G: 4.9247 D(x): 0.9437 D(G(z)): 0.0080 / 0.0098
[47/4000][0/600] Loss_D: 0.0389 Loss_G: 4.1013 D(x): 0.9958 D(G(z)): 0.0330 / 0.0274
[47/4000][100/600] Loss_D: 0.0172 Loss_G: 5.7586 D(x): 0.9951 D(G(z)): 0.0120 / 0.0054
[47/4000][200/600] Loss_D: 0.0543 Loss_G: 5.4202 D(x): 0.9776 D(G(z)): 0.0096 / 0.0075
[47/4000][300/600] Loss_D: 0.0811 Loss_G: 3.7703 D(x): 0.9883 D(G(z)): 0.0592 / 0.0364
[47/4000][400/600] Loss_D: 0.0364 Loss_G: 5.5982 D(x): 0.9765 D(G(z)): 0.0102 / 0.0081
[47/4000][500/600] Loss_D: 0.1097 Loss_G: 6.7903 D(x): 0.9357 D(G(z)): 0.0017 / 0.0024
[48/4000][0/600] Loss_D: 0.1174 Loss_G: 6.3698 D(x): 0.9160 D(G(z)): 0.0040 / 0.0031
[48/4000][100/600] Loss_D: 0.0345 Loss_G: 4.7988 D(x): 0.9875 D(G(z)): 0.0197 / 0.0133
[48/4000][200/600] Loss_D: 0.0250 Loss_G: 4.8016 D(x): 0.9975 D(G(z)): 0.0221 / 0.0104
[48/4000][300/600] Loss_D: 0.0442 Loss_G: 4.4848 D(x): 0.9795 D(G(z)): 0.0159 / 0.0144
[48/4000][400/600] Loss_D: 0.0999 Loss_G: 6.0857 D(x): 0.9431 D(G(z)): 0.0061 / 0.0034
[48/4000][500/600] Loss_D: 0.0493 Loss_G: 4.6668 D(x): 0.9740 D(G(z)): 0.0151 / 0.0122
[49/4000][0/600] Loss_D: 0.0881 Loss_G: 4.1870 D(x): 0.9700 D(G(z)): 0.0379 / 0.0221
[49/4000][100/600] Loss_D: 0.0571 Loss_G: 4.5915 D(x): 0.9712 D(G(z)): 0.0188 / 0.0151
[49/4000][200/600] Loss_D: 0.0109 Loss_G: 5.8679 D(x): 0.9965 D(G(z)): 0.0073 / 0.0037
[49/4000][300/600] Loss_D: 0.0635 Loss_G: 6.3964 D(x): 0.9638 D(G(z)): 0.0031 / 0.0021
[49/4000][400/600] Loss_D: 0.1893 Loss_G: 5.8633 D(x): 0.8908 D(G(z)): 0.0060 / 0.0042
[49/4000][500/600] Loss_D: 0.0577 Loss_G: 7.4644 D(x): 0.9583 D(G(z)): 0.0011 / 0.0007
[50/4000][0/600] Loss_D: 0.0335 Loss_G: 4.3722 D(x): 0.9915 D(G(z)): 0.0241 / 0.0180
[50/4000][100/600] Loss_D: 0.0592 Loss_G: 4.1266 D(x): 0.9773 D(G(z)): 0.0312 / 0.0222
[50/4000][200/600] Loss_D: 0.1450 Loss_G: 7.5133 D(x): 0.9113 D(G(z)): 0.0014 / 0.0008
[50/4000][300/600] Loss_D: 0.0441 Loss_G: 4.9267 D(x): 0.9785 D(G(z)): 0.0152 / 0.0115
[50/4000][400/600] Loss_D: 0.0584 Loss_G: 4.2687 D(x): 0.9829 D(G(z)): 0.0267 / 0.0203
[50/4000][500/600] Loss_D: 0.0845 Loss_G: 4.9608 D(x): 0.9476 D(G(z)): 0.0098 / 0.0108
[51/4000][0/600] Loss_D: 0.0582 Loss_G: 2.9596 D(x): 0.9929 D(G(z)): 0.0483 / 0.0708
[51/4000][100/600] Loss_D: 0.1034 Loss_G: 4.3409 D(x): 0.9644 D(G(z)): 0.0429 / 0.0181
[51/4000][200/600] Loss_D: 0.2785 Loss_G: 7.2224 D(x): 0.8321 D(G(z)): 0.0017 / 0.0014
[51/4000][300/600] Loss_D: 0.0381 Loss_G: 4.7525 D(x): 0.9896 D(G(z)): 0.0261 / 0.0145
[51/4000][400/600] Loss_D: 0.0567 Loss_G: 4.4424 D(x): 0.9665 D(G(z)): 0.0185 / 0.0168
[51/4000][500/600] Loss_D: 0.0674 Loss_G: 4.8393 D(x): 0.9523 D(G(z)): 0.0072 / 0.0107
[52/4000][0/600] Loss_D: 0.0141 Loss_G: 6.3135 D(x): 0.9960 D(G(z)): 0.0099 / 0.0026
[52/4000][100/600] Loss_D: 0.0742 Loss_G: 4.4437 D(x): 0.9800 D(G(z)): 0.0397 / 0.0174
[52/4000][200/600] Loss_D: 0.0881 Loss_G: 4.4894 D(x): 0.9504 D(G(z)): 0.0180 / 0.0170
[52/4000][300/600] Loss_D: 0.0200 Loss_G: 6.8474 D(x): 0.9878 D(G(z)): 0.0064 / 0.0021
[52/4000][400/600] Loss_D: 0.0425 Loss_G: 4.6783 D(x): 0.9819 D(G(z)): 0.0228 / 0.0146
[52/4000][500/600] Loss_D: 0.1120 Loss_G: 2.8360 D(x): 0.9757 D(G(z)): 0.0777 / 0.1023
[53/4000][0/600] Loss_D: 0.0345 Loss_G: 5.1028 D(x): 0.9822 D(G(z)): 0.0138 / 0.0088
[53/4000][100/600] Loss_D: 0.0258 Loss_G: 5.5293 D(x): 0.9866 D(G(z)): 0.0103 / 0.0056
[53/4000][200/600] Loss_D: 0.1263 Loss_G: 4.0230 D(x): 0.9882 D(G(z)): 0.0955 / 0.0294
[53/4000][300/600] Loss_D: 0.0949 Loss_G: 4.4906 D(x): 0.9475 D(G(z)): 0.0127 / 0.0235
[53/4000][400/600] Loss_D: 0.1504 Loss_G: 3.1553 D(x): 0.9706 D(G(z)): 0.1070 / 0.0617
[53/4000][500/600] Loss_D: 0.0296 Loss_G: 5.3400 D(x): 0.9836 D(G(z)): 0.0115 / 0.0074
[54/4000][0/600] Loss_D: 0.0838 Loss_G: 3.8117 D(x): 0.9957 D(G(z)): 0.0721 / 0.0401
[54/4000][100/600] Loss_D: 0.0636 Loss_G: 3.8068 D(x): 0.9889 D(G(z)): 0.0481 / 0.0431
[54/4000][200/600] Loss_D: 0.0255 Loss_G: 6.2156 D(x): 0.9834 D(G(z)): 0.0059 / 0.0044
[54/4000][300/600] Loss_D: 0.0746 Loss_G: 5.7871 D(x): 0.9532 D(G(z)): 0.0041 / 0.0057
[54/4000][400/600] Loss_D: 0.0368 Loss_G: 5.2535 D(x): 0.9763 D(G(z)): 0.0101 / 0.0076
[54/4000][500/600] Loss_D: 0.0607 Loss_G: 4.5068 D(x): 0.9661 D(G(z)): 0.0156 / 0.0156
[55/4000][0/600] Loss_D: 0.0446 Loss_G: 6.7231 D(x): 0.9725 D(G(z)): 0.0043 / 0.0016
[55/4000][100/600] Loss_D: 0.0600 Loss_G: 5.4967 D(x): 0.9704 D(G(z)): 0.0083 / 0.0085
[55/4000][200/600] Loss_D: 0.0294 Loss_G: 5.3717 D(x): 0.9853 D(G(z)): 0.0120 / 0.0081
[55/4000][300/600] Loss_D: 0.0809 Loss_G: 4.5917 D(x): 0.9542 D(G(z)): 0.0080 / 0.0152
[55/4000][400/600] Loss_D: 0.0210 Loss_G: 5.6150 D(x): 0.9951 D(G(z)): 0.0156 / 0.0078
[55/4000][500/600] Loss_D: 0.1076 Loss_G: 5.7107 D(x): 0.9394 D(G(z)): 0.0047 / 0.0060
[56/4000][0/600] Loss_D: 0.1032 Loss_G: 5.3333 D(x): 0.9414 D(G(z)): 0.0058 / 0.0067
[56/4000][100/600] Loss_D: 0.0621 Loss_G: 5.4814 D(x): 0.9623 D(G(z)): 0.0090 / 0.0058
[56/4000][200/600] Loss_D: 0.0931 Loss_G: 5.5189 D(x): 0.9487 D(G(z)): 0.0161 / 0.0065
[56/4000][300/600] Loss_D: 0.0343 Loss_G: 5.8384 D(x): 0.9741 D(G(z)): 0.0039 / 0.0038
[56/4000][400/600] Loss_D: 0.0225 Loss_G: 5.0236 D(x): 0.9952 D(G(z)): 0.0168 / 0.0091
[56/4000][500/600] Loss_D: 0.0629 Loss_G: 6.1544 D(x): 0.9567 D(G(z)): 0.0034 / 0.0036
[57/4000][0/600] Loss_D: 0.0302 Loss_G: 4.3713 D(x): 0.9916 D(G(z)): 0.0213 / 0.0160
[57/4000][100/600] Loss_D: 0.0286 Loss_G: 5.3148 D(x): 0.9859 D(G(z)): 0.0110 / 0.0065
[57/4000][200/600] Loss_D: 0.0268 Loss_G: 4.6221 D(x): 0.9993 D(G(z)): 0.0255 / 0.0134
[57/4000][300/600] Loss_D: 0.0194 Loss_G: 5.1755 D(x): 0.9947 D(G(z)): 0.0137 / 0.0111
[57/4000][400/600] Loss_D: 0.0646 Loss_G: 4.5038 D(x): 0.9588 D(G(z)): 0.0168 / 0.0149
[57/4000][500/600] Loss_D: 0.0182 Loss_G: 5.1807 D(x): 0.9925 D(G(z)): 0.0104 / 0.0095
[58/4000][0/600] Loss_D: 0.1326 Loss_G: 9.9539 D(x): 0.9300 D(G(z)): 0.0003 / 0.0001
[58/4000][100/600] Loss_D: 0.0193 Loss_G: 5.4067 D(x): 0.9921 D(G(z)): 0.0093 / 0.0093
[58/4000][200/600] Loss_D: 0.1221 Loss_G: 6.3810 D(x): 0.9437 D(G(z)): 0.0042 / 0.0025
[58/4000][300/600] Loss_D: 0.0134 Loss_G: 5.5555 D(x): 0.9934 D(G(z)): 0.0066 / 0.0053
[58/4000][400/600] Loss_D: 0.0210 Loss_G: 5.7650 D(x): 0.9918 D(G(z)): 0.0121 / 0.0054
[58/4000][500/600] Loss_D: 0.0313 Loss_G: 4.6934 D(x): 0.9820 D(G(z)): 0.0107 / 0.0123
[59/4000][0/600] Loss_D: 0.0179 Loss_G: 5.2895 D(x): 0.9910 D(G(z)): 0.0074 / 0.0075
[59/4000][100/600] Loss_D: 0.0145 Loss_G: 5.9681 D(x): 0.9890 D(G(z)): 0.0029 / 0.0036
[59/4000][200/600] Loss_D: 0.0228 Loss_G: 5.6461 D(x): 0.9895 D(G(z)): 0.0111 / 0.0071
[59/4000][300/600] Loss_D: 0.0652 Loss_G: 7.0754 D(x): 0.9525 D(G(z)): 0.0015 / 0.0028
[59/4000][400/600] Loss_D: 0.0318 Loss_G: 6.6199 D(x): 0.9762 D(G(z)): 0.0030 / 0.0020
[59/4000][500/600] Loss_D: 0.0624 Loss_G: 3.6466 D(x): 0.9779 D(G(z)): 0.0320 / 0.0389
[60/4000][0/600] Loss_D: 0.0140 Loss_G: 6.4851 D(x): 0.9916 D(G(z)): 0.0045 / 0.0022
[60/4000][100/600] Loss_D: 0.0195 Loss_G: 5.4892 D(x): 0.9961 D(G(z)): 0.0149 / 0.0091
[60/4000][200/600] Loss_D: 0.0239 Loss_G: 10.7857 D(x): 0.9815 D(G(z)): 0.0002 / 0.0000
[60/4000][300/600] Loss_D: 0.0452 Loss_G: 6.0586 D(x): 0.9696 D(G(z)): 0.0052 / 0.0043
[60/4000][400/600] Loss_D: 0.0202 Loss_G: 7.8870 D(x): 0.9854 D(G(z)): 0.0016 / 0.0007
[60/4000][500/600] Loss_D: 0.0597 Loss_G: 4.0527 D(x): 0.9919 D(G(z)): 0.0488 / 0.0280
[61/4000][0/600] Loss_D: 0.0085 Loss_G: 6.8405 D(x): 0.9959 D(G(z)): 0.0043 / 0.0021
[61/4000][100/600] Loss_D: 0.0469 Loss_G: 5.3645 D(x): 0.9897 D(G(z)): 0.0315 / 0.0091
[61/4000][200/600] Loss_D: 0.0311 Loss_G: 4.5898 D(x): 0.9909 D(G(z)): 0.0210 / 0.0148
[61/4000][300/600] Loss_D: 0.0797 Loss_G: 5.6997 D(x): 0.9635 D(G(z)): 0.0082 / 0.0064
[61/4000][400/600] Loss_D: 0.0512 Loss_G: 4.3276 D(x): 0.9876 D(G(z)): 0.0366 / 0.0191
[61/4000][500/600] Loss_D: 0.0178 Loss_G: 5.1315 D(x): 0.9904 D(G(z)): 0.0076 / 0.0101
[62/4000][0/600] Loss_D: 0.0317 Loss_G: 4.8872 D(x): 0.9823 D(G(z)): 0.0101 / 0.0121
[62/4000][100/600] Loss_D: 0.0480 Loss_G: 4.0487 D(x): 0.9891 D(G(z)): 0.0355 / 0.0206
[62/4000][200/600] Loss_D: 0.3436 Loss_G: 6.9486 D(x): 0.8521 D(G(z)): 0.0011 / 0.0014
[62/4000][300/600] Loss_D: 0.0714 Loss_G: 5.1042 D(x): 0.9634 D(G(z)): 0.0080 / 0.0085
[62/4000][400/600] Loss_D: 0.0188 Loss_G: 6.1888 D(x): 0.9868 D(G(z)): 0.0045 / 0.0029
[62/4000][500/600] Loss_D: 0.0507 Loss_G: 6.5782 D(x): 0.9637 D(G(z)): 0.0023 / 0.0026
[63/4000][0/600] Loss_D: 0.0337 Loss_G: 6.1050 D(x): 0.9771 D(G(z)): 0.0055 / 0.0028
[63/4000][100/600] Loss_D: 0.0172 Loss_G: 6.4681 D(x): 0.9884 D(G(z)): 0.0049 / 0.0023
[63/4000][200/600] Loss_D: 0.1033 Loss_G: 3.9099 D(x): 0.9958 D(G(z)): 0.0911 / 0.0283
[63/4000][300/600] Loss_D: 0.0443 Loss_G: 4.4225 D(x): 0.9757 D(G(z)): 0.0177 / 0.0230
[63/4000][400/600] Loss_D: 0.0669 Loss_G: 6.5660 D(x): 0.9518 D(G(z)): 0.0023 / 0.0020
[63/4000][500/600] Loss_D: 0.0165 Loss_G: 6.8555 D(x): 0.9863 D(G(z)): 0.0018 / 0.0018
[64/4000][0/600] Loss_D: 0.0581 Loss_G: 4.6683 D(x): 0.9705 D(G(z)): 0.0233 / 0.0126
[64/4000][100/600] Loss_D: 0.0131 Loss_G: 5.4762 D(x): 0.9974 D(G(z)): 0.0104 / 0.0058
[64/4000][200/600] Loss_D: 0.0259 Loss_G: 6.0072 D(x): 0.9839 D(G(z)): 0.0065 / 0.0038
[64/4000][300/600] Loss_D: 0.0644 Loss_G: 3.4501 D(x): 0.9873 D(G(z)): 0.0463 / 0.0522
[64/4000][400/600] Loss_D: 0.0352 Loss_G: 6.9611 D(x): 0.9728 D(G(z)): 0.0040 / 0.0021
[64/4000][500/600] Loss_D: 0.0190 Loss_G: 6.5146 D(x): 0.9851 D(G(z)): 0.0016 / 0.0025
[65/4000][0/600] Loss_D: 0.0354 Loss_G: 3.6836 D(x): 0.9959 D(G(z)): 0.0304 / 0.0339
[65/4000][100/600] Loss_D: 0.0475 Loss_G: 5.4622 D(x): 0.9833 D(G(z)): 0.0086 / 0.0077
[65/4000][200/600] Loss_D: 0.0581 Loss_G: 5.4979 D(x): 0.9685 D(G(z)): 0.0147 / 0.0075
[65/4000][300/600] Loss_D: 0.0175 Loss_G: 4.8711 D(x): 0.9965 D(G(z)): 0.0137 / 0.0113
[65/4000][400/600] Loss_D: 0.0099 Loss_G: 6.2953 D(x): 0.9937 D(G(z)): 0.0034 / 0.0023
[65/4000][500/600] Loss_D: 0.0551 Loss_G: 5.0453 D(x): 0.9641 D(G(z)): 0.0071 / 0.0093
[66/4000][0/600] Loss_D: 0.0156 Loss_G: 7.1667 D(x): 0.9891 D(G(z)): 0.0036 / 0.0013
[66/4000][100/600] Loss_D: 0.0593 Loss_G: 5.5071 D(x): 0.9803 D(G(z)): 0.0131 / 0.0078
[66/4000][200/600] Loss_D: 0.0399 Loss_G: 6.0377 D(x): 0.9765 D(G(z)): 0.0082 / 0.0036
[66/4000][300/600] Loss_D: 0.0469 Loss_G: 3.8034 D(x): 0.9908 D(G(z)): 0.0351 / 0.0317
[66/4000][400/600] Loss_D: 0.0399 Loss_G: 7.7873 D(x): 0.9667 D(G(z)): 0.0013 / 0.0007
[66/4000][500/600] Loss_D: 0.0643 Loss_G: 3.9461 D(x): 0.9882 D(G(z)): 0.0447 / 0.0346
[67/4000][0/600] Loss_D: 0.0321 Loss_G: 5.1282 D(x): 0.9862 D(G(z)): 0.0137 / 0.0081
[67/4000][100/600] Loss_D: 0.0109 Loss_G: 5.6704 D(x): 0.9979 D(G(z)): 0.0087 / 0.0062
[67/4000][200/600] Loss_D: 0.0050 Loss_G: 7.6709 D(x): 0.9984 D(G(z)): 0.0032 / 0.0009
[67/4000][300/600] Loss_D: 0.0086 Loss_G: 6.4505 D(x): 0.9958 D(G(z)): 0.0042 / 0.0036
[67/4000][400/600] Loss_D: 0.1256 Loss_G: 5.6361 D(x): 0.9393 D(G(z)): 0.0050 / 0.0081
[67/4000][500/600] Loss_D: 0.0708 Loss_G: 6.0308 D(x): 0.9525 D(G(z)): 0.0041 / 0.0040
[68/4000][0/600] Loss_D: 0.0794 Loss_G: 5.7070 D(x): 0.9566 D(G(z)): 0.0118 / 0.0049
[68/4000][100/600] Loss_D: 0.0686 Loss_G: 3.5454 D(x): 0.9956 D(G(z)): 0.0602 / 0.0404
[68/4000][200/600] Loss_D: 0.0587 Loss_G: 6.7163 D(x): 0.9667 D(G(z)): 0.0117 / 0.0035
[68/4000][300/600] Loss_D: 0.0174 Loss_G: 5.7377 D(x): 0.9900 D(G(z)): 0.0067 / 0.0052
[68/4000][400/600] Loss_D: 0.0579 Loss_G: 5.2230 D(x): 0.9709 D(G(z)): 0.0129 / 0.0091
[68/4000][500/600] Loss_D: 0.0490 Loss_G: 7.2261 D(x): 0.9607 D(G(z)): 0.0014 / 0.0011
[69/4000][0/600] Loss_D: 0.0570 Loss_G: 4.0280 D(x): 0.9958 D(G(z)): 0.0495 / 0.0309
[69/4000][100/600] Loss_D: 0.0172 Loss_G: 5.4828 D(x): 0.9925 D(G(z)): 0.0091 / 0.0070
[69/4000][200/600] Loss_D: 0.0639 Loss_G: 4.0276 D(x): 0.9983 D(G(z)): 0.0585 / 0.0259
[69/4000][300/600] Loss_D: 0.0229 Loss_G: 5.0567 D(x): 0.9913 D(G(z)): 0.0130 / 0.0105
[69/4000][400/600] Loss_D: 0.0099 Loss_G: 6.3637 D(x): 0.9955 D(G(z)): 0.0052 / 0.0030
[69/4000][500/600] Loss_D: 0.0146 Loss_G: 5.8237 D(x): 0.9933 D(G(z)): 0.0076 / 0.0047
[70/4000][0/600] Loss_D: 0.0272 Loss_G: 4.3618 D(x): 0.9980 D(G(z)): 0.0247 / 0.0178
[70/4000][100/600] Loss_D: 0.0463 Loss_G: 4.5076 D(x): 0.9861 D(G(z)): 0.0246 / 0.0209
[70/4000][200/600] Loss_D: 0.0113 Loss_G: 6.5568 D(x): 0.9967 D(G(z)): 0.0079 / 0.0019
[70/4000][300/600] Loss_D: 0.0203 Loss_G: 4.6552 D(x): 0.9957 D(G(z)): 0.0156 / 0.0160
[70/4000][400/600] Loss_D: 0.0264 Loss_G: 5.5285 D(x): 0.9917 D(G(z)): 0.0164 / 0.0106
[70/4000][500/600] Loss_D: 0.0114 Loss_G: 6.0149 D(x): 0.9934 D(G(z)): 0.0045 / 0.0033
[71/4000][0/600] Loss_D: 0.0237 Loss_G: 7.5374 D(x): 0.9839 D(G(z)): 0.0046 / 0.0012
[71/4000][100/600] Loss_D: 0.0173 Loss_G: 6.0861 D(x): 0.9929 D(G(z)): 0.0088 / 0.0049
[71/4000][200/600] Loss_D: 0.0302 Loss_G: 5.1810 D(x): 0.9999 D(G(z)): 0.0290 / 0.0112
[71/4000][300/600] Loss_D: 0.0448 Loss_G: 5.1768 D(x): 0.9803 D(G(z)): 0.0100 / 0.0077
[71/4000][400/600] Loss_D: 0.0532 Loss_G: 7.1913 D(x): 0.9648 D(G(z)): 0.0056 / 0.0029
[71/4000][500/600] Loss_D: 0.0248 Loss_G: 6.4169 D(x): 0.9826 D(G(z)): 0.0042 / 0.0027
[72/4000][0/600] Loss_D: 0.0307 Loss_G: 7.3533 D(x): 0.9838 D(G(z)): 0.0037 / 0.0014
[72/4000][100/600] Loss_D: 0.0182 Loss_G: 6.0139 D(x): 0.9898 D(G(z)): 0.0062 / 0.0061
[72/4000][200/600] Loss_D: 0.0340 Loss_G: 5.3329 D(x): 0.9881 D(G(z)): 0.0099 / 0.0066
[72/4000][300/600] Loss_D: 0.0540 Loss_G: 3.3482 D(x): 0.9926 D(G(z)): 0.0433 / 0.0529
[72/4000][400/600] Loss_D: 0.0424 Loss_G: 7.6102 D(x): 0.9693 D(G(z)): 0.0030 / 0.0007
[72/4000][500/600] Loss_D: 0.0061 Loss_G: 7.7341 D(x): 0.9954 D(G(z)): 0.0014 / 0.0008
[73/4000][0/600] Loss_D: 0.1310 Loss_G: 3.3794 D(x): 0.9984 D(G(z)): 0.1130 / 0.0636
[73/4000][100/600] Loss_D: 0.0571 Loss_G: 7.5951 D(x): 0.9608 D(G(z)): 0.0014 / 0.0019
[73/4000][200/600] Loss_D: 0.0096 Loss_G: 6.2057 D(x): 0.9995 D(G(z)): 0.0086 / 0.0056
[73/4000][300/600] Loss_D: 0.0857 Loss_G: 5.9737 D(x): 0.9606 D(G(z)): 0.0040 / 0.0042
[73/4000][400/600] Loss_D: 0.0766 Loss_G: 4.4313 D(x): 0.9825 D(G(z)): 0.0433 / 0.0212
[73/4000][500/600] Loss_D: 0.0691 Loss_G: 5.2992 D(x): 0.9577 D(G(z)): 0.0046 / 0.0081
[74/4000][0/600] Loss_D: 0.3534 Loss_G: 3.9915 D(x): 0.9999 D(G(z)): 0.2791 / 0.0261
[74/4000][100/600] Loss_D: 0.0233 Loss_G: 6.7626 D(x): 0.9840 D(G(z)): 0.0014 / 0.0018
[74/4000][200/600] Loss_D: 0.0310 Loss_G: 4.5151 D(x): 0.9994 D(G(z)): 0.0296 / 0.0156
[74/4000][300/600] Loss_D: 0.0373 Loss_G: 3.8110 D(x): 0.9937 D(G(z)): 0.0287 / 0.0318
[74/4000][400/600] Loss_D: 0.0286 Loss_G: 5.1935 D(x): 0.9849 D(G(z)): 0.0108 / 0.0084
[74/4000][500/600] Loss_D: 0.0169 Loss_G: 6.6794 D(x): 0.9877 D(G(z)): 0.0019 / 0.0018
[75/4000][0/600] Loss_D: 0.0774 Loss_G: 9.6187 D(x): 0.9547 D(G(z)): 0.0005 / 0.0002
[75/4000][100/600] Loss_D: 0.0494 Loss_G: 4.1838 D(x): 0.9987 D(G(z)): 0.0463 / 0.0200
[75/4000][200/600] Loss_D: 0.0951 Loss_G: 4.9381 D(x): 0.9675 D(G(z)): 0.0401 / 0.0111
[75/4000][300/600] Loss_D: 0.0366 Loss_G: 4.3679 D(x): 0.9834 D(G(z)): 0.0168 / 0.0221
[75/4000][400/600] Loss_D: 0.0186 Loss_G: 4.9118 D(x): 0.9993 D(G(z)): 0.0174 / 0.0143
[75/4000][500/600] Loss_D: 0.0088 Loss_G: 6.3192 D(x): 0.9948 D(G(z)): 0.0034 / 0.0027
[76/4000][0/600] Loss_D: 0.0798 Loss_G: 3.8517 D(x): 0.9998 D(G(z)): 0.0733 / 0.0342
[76/4000][100/600] Loss_D: 0.0111 Loss_G: 5.8538 D(x): 0.9952 D(G(z)): 0.0060 / 0.0047
[76/4000][200/600] Loss_D: 0.0315 Loss_G: 5.1758 D(x): 0.9886 D(G(z)): 0.0138 / 0.0105
[76/4000][300/600] Loss_D: 0.0395 Loss_G: 5.9776 D(x): 0.9781 D(G(z)): 0.0070 / 0.0055
[76/4000][400/600] Loss_D: 0.0591 Loss_G: 4.7402 D(x): 0.9663 D(G(z)): 0.0162 / 0.0108
[76/4000][500/600] Loss_D: 0.0149 Loss_G: 5.4019 D(x): 0.9914 D(G(z)): 0.0059 / 0.0063
[77/4000][0/600] Loss_D: 0.0202 Loss_G: 6.4982 D(x): 0.9848 D(G(z)): 0.0019 / 0.0020
[77/4000][100/600] Loss_D: 0.0141 Loss_G: 7.3993 D(x): 0.9909 D(G(z)): 0.0041 / 0.0014
[77/4000][200/600] Loss_D: 0.0329 Loss_G: 7.4390 D(x): 0.9770 D(G(z)): 0.0040 / 0.0009
[77/4000][300/600] Loss_D: 0.0255 Loss_G: 6.2294 D(x): 0.9889 D(G(z)): 0.0096 / 0.0068
[77/4000][400/600] Loss_D: 0.0163 Loss_G: 6.3106 D(x): 0.9950 D(G(z)): 0.0105 / 0.0028
[77/4000][500/600] Loss_D: 0.0219 Loss_G: 6.7109 D(x): 0.9864 D(G(z)): 0.0041 / 0.0025
[78/4000][0/600] Loss_D: 0.0837 Loss_G: 3.2544 D(x): 0.9927 D(G(z)): 0.0685 / 0.0643
[78/4000][100/600] Loss_D: 0.0325 Loss_G: 6.0012 D(x): 0.9791 D(G(z)): 0.0033 / 0.0036
[78/4000][200/600] Loss_D: 0.0186 Loss_G: 7.6826 D(x): 0.9883 D(G(z)): 0.0048 / 0.0008
[78/4000][300/600] Loss_D: 0.0691 Loss_G: 4.4508 D(x): 0.9754 D(G(z)): 0.0168 / 0.0160
[78/4000][400/600] Loss_D: 0.0160 Loss_G: 6.3330 D(x): 0.9909 D(G(z)): 0.0063 / 0.0028
[78/4000][500/600] Loss_D: 0.0507 Loss_G: 6.3826 D(x): 0.9824 D(G(z)): 0.0062 / 0.0032
[79/4000][0/600] Loss_D: 0.0186 Loss_G: 6.0574 D(x): 0.9896 D(G(z)): 0.0072 / 0.0037
[79/4000][100/600] Loss_D: 0.0260 Loss_G: 4.2779 D(x): 0.9945 D(G(z)): 0.0195 / 0.0201
[79/4000][200/600] Loss_D: 0.0080 Loss_G: 6.1510 D(x): 0.9980 D(G(z)): 0.0059 / 0.0032
[79/4000][300/600] Loss_D: 0.0138 Loss_G: 7.5473 D(x): 0.9891 D(G(z)): 0.0011 / 0.0011
[79/4000][400/600] Loss_D: 0.0524 Loss_G: 4.7939 D(x): 0.9791 D(G(z)): 0.0230 / 0.0139
[79/4000][500/600] Loss_D: 0.1064 Loss_G: 7.5499 D(x): 0.9388 D(G(z)): 0.0007 / 0.0008
[80/4000][0/600] Loss_D: 0.0465 Loss_G: 2.8720 D(x): 0.9991 D(G(z)): 0.0435 / 0.0917
[80/4000][100/600] Loss_D: 0.0125 Loss_G: 5.8585 D(x): 0.9975 D(G(z)): 0.0098 / 0.0040
[80/4000][200/600] Loss_D: 0.0233 Loss_G: 5.1494 D(x): 0.9998 D(G(z)): 0.0225 / 0.0103
[80/4000][300/600] Loss_D: 0.1094 Loss_G: 5.9639 D(x): 0.9437 D(G(z)): 0.0045 / 0.0040
[80/4000][400/600] Loss_D: 0.0871 Loss_G: 3.9321 D(x): 0.9928 D(G(z)): 0.0739 / 0.0309
[80/4000][500/600] Loss_D: 0.0112 Loss_G: 9.3177 D(x): 0.9898 D(G(z)): 0.0003 / 0.0002
[81/4000][0/600] Loss_D: 0.0140 Loss_G: 6.6058 D(x): 0.9924 D(G(z)): 0.0055 / 0.0023
[81/4000][100/600] Loss_D: 0.0258 Loss_G: 5.1318 D(x): 0.9966 D(G(z)): 0.0213 / 0.0116
[81/4000][200/600] Loss_D: 0.0311 Loss_G: 5.1572 D(x): 0.9983 D(G(z)): 0.0284 / 0.0121
[81/4000][300/600] Loss_D: 0.0318 Loss_G: 6.4956 D(x): 0.9822 D(G(z)): 0.0031 / 0.0032
[81/4000][400/600] Loss_D: 0.0088 Loss_G: 5.6437 D(x): 0.9987 D(G(z)): 0.0075 / 0.0051
[81/4000][500/600] Loss_D: 0.0231 Loss_G: 5.6180 D(x): 0.9849 D(G(z)): 0.0035 / 0.0056
[82/4000][0/600] Loss_D: 0.2068 Loss_G: 11.7429 D(x): 0.9023 D(G(z)): 0.0001 / 0.0000
[82/4000][100/600] Loss_D: 0.0908 Loss_G: 3.9470 D(x): 0.9994 D(G(z)): 0.0779 / 0.0668
[82/4000][200/600] Loss_D: 0.0130 Loss_G: 5.8660 D(x): 0.9967 D(G(z)): 0.0096 / 0.0035
[82/4000][300/600] Loss_D: 0.0390 Loss_G: 4.4446 D(x): 0.9890 D(G(z)): 0.0212 / 0.0194
[82/4000][400/600] Loss_D: 0.0239 Loss_G: 5.2717 D(x): 0.9946 D(G(z)): 0.0178 / 0.0070
[82/4000][500/600] Loss_D: 0.0142 Loss_G: 6.3259 D(x): 0.9908 D(G(z)): 0.0037 / 0.0023
[83/4000][0/600] Loss_D: 0.0147 Loss_G: 5.1139 D(x): 0.9968 D(G(z)): 0.0111 / 0.0115
[83/4000][100/600] Loss_D: 0.0090 Loss_G: 6.0662 D(x): 0.9974 D(G(z)): 0.0062 / 0.0042
[83/4000][200/600] Loss_D: 0.0070 Loss_G: 6.3383 D(x): 0.9960 D(G(z)): 0.0028 / 0.0030
[83/4000][300/600] Loss_D: 0.0113 Loss_G: 6.3072 D(x): 0.9940 D(G(z)): 0.0045 / 0.0031
[83/4000][400/600] Loss_D: 0.0332 Loss_G: 6.3654 D(x): 0.9784 D(G(z)): 0.0051 / 0.0029
[83/4000][500/600] Loss_D: 0.0104 Loss_G: 6.0089 D(x): 0.9923 D(G(z)): 0.0024 / 0.0034
[84/4000][0/600] Loss_D: 0.0240 Loss_G: 7.4294 D(x): 0.9820 D(G(z)): 0.0032 / 0.0009
[84/4000][100/600] Loss_D: 0.0155 Loss_G: 5.7492 D(x): 0.9914 D(G(z)): 0.0066 / 0.0055
[84/4000][200/600] Loss_D: 0.0273 Loss_G: 5.8773 D(x): 0.9968 D(G(z)): 0.0232 / 0.0038
[84/4000][300/600] Loss_D: 0.1935 Loss_G: 5.7464 D(x): 0.9163 D(G(z)): 0.0012 / 0.0047
[84/4000][400/600] Loss_D: 0.0118 Loss_G: 5.7657 D(x): 0.9949 D(G(z)): 0.0064 / 0.0042
[84/4000][500/600] Loss_D: 0.0625 Loss_G: 5.7797 D(x): 0.9672 D(G(z)): 0.0032 / 0.0046
[85/4000][0/600] Loss_D: 0.0785 Loss_G: 3.8528 D(x): 0.9930 D(G(z)): 0.0666 / 0.0321
[85/4000][100/600] Loss_D: 0.0110 Loss_G: 5.8654 D(x): 0.9963 D(G(z)): 0.0070 / 0.0049
[85/4000][200/600] Loss_D: 0.0177 Loss_G: 5.7423 D(x): 0.9985 D(G(z)): 0.0158 / 0.0071
[85/4000][300/600] Loss_D: 0.0252 Loss_G: 5.2248 D(x): 0.9931 D(G(z)): 0.0175 / 0.0104
[85/4000][400/600] Loss_D: 0.0768 Loss_G: 5.2523 D(x): 0.9635 D(G(z)): 0.0075 / 0.0087
[85/4000][500/600] Loss_D: 0.0573 Loss_G: 4.5027 D(x): 0.9813 D(G(z)): 0.0262 / 0.0194
[86/4000][0/600] Loss_D: 0.0574 Loss_G: 4.4162 D(x): 0.9963 D(G(z)): 0.0501 / 0.0194
[86/4000][100/600] Loss_D: 0.0685 Loss_G: 6.2291 D(x): 0.9626 D(G(z)): 0.0034 / 0.0059
[86/4000][200/600] Loss_D: 0.0217 Loss_G: 8.7058 D(x): 0.9854 D(G(z)): 0.0003 / 0.0002
[86/4000][300/600] Loss_D: 0.0695 Loss_G: 4.7411 D(x): 0.9597 D(G(z)): 0.0095 / 0.0132
[86/4000][400/600] Loss_D: 0.0609 Loss_G: 5.4478 D(x): 0.9643 D(G(z)): 0.0074 / 0.0076
[86/4000][500/600] Loss_D: 0.0345 Loss_G: 5.5291 D(x): 0.9784 D(G(z)): 0.0094 / 0.0080
[87/4000][0/600] Loss_D: 0.0233 Loss_G: 4.8125 D(x): 0.9936 D(G(z)): 0.0163 / 0.0153
[87/4000][100/600] Loss_D: 0.0090 Loss_G: 5.6359 D(x): 0.9959 D(G(z)): 0.0048 / 0.0048
[87/4000][200/600] Loss_D: 0.0254 Loss_G: 6.0518 D(x): 0.9878 D(G(z)): 0.0049 / 0.0043
[87/4000][300/600] Loss_D: 0.0657 Loss_G: 5.4723 D(x): 0.9649 D(G(z)): 0.0058 / 0.0067
[87/4000][400/600] Loss_D: 0.0157 Loss_G: 6.3700 D(x): 0.9907 D(G(z)): 0.0059 / 0.0022
[87/4000][500/600] Loss_D: 0.0162 Loss_G: 5.0480 D(x): 0.9949 D(G(z)): 0.0107 / 0.0093
[88/4000][0/600] Loss_D: 0.0940 Loss_G: 8.1145 D(x): 0.9358 D(G(z)): 0.0017 / 0.0005
[88/4000][100/600] Loss_D: 0.2523 Loss_G: 11.0974 D(x): 0.8961 D(G(z)): 0.0001 / 0.0000
[88/4000][200/600] Loss_D: 0.0364 Loss_G: 5.5070 D(x): 0.9948 D(G(z)): 0.0295 / 0.0076
[88/4000][300/600] Loss_D: 0.0222 Loss_G: 7.8858 D(x): 0.9825 D(G(z)): 0.0009 / 0.0007
[88/4000][400/600] Loss_D: 0.0594 Loss_G: 5.4400 D(x): 0.9708 D(G(z)): 0.0151 / 0.0079
[88/4000][500/600] Loss_D: 0.0639 Loss_G: 3.9783 D(x): 0.9809 D(G(z)): 0.0359 / 0.0354
[89/4000][0/600] Loss_D: 0.0712 Loss_G: 4.2183 D(x): 0.9984 D(G(z)): 0.0644 / 0.0229
[89/4000][100/600] Loss_D: 0.0510 Loss_G: 5.8407 D(x): 0.9648 D(G(z)): 0.0040 / 0.0055
[89/4000][200/600] Loss_D: 0.0144 Loss_G: 4.9478 D(x): 0.9988 D(G(z)): 0.0130 / 0.0113
[89/4000][300/600] Loss_D: 0.0951 Loss_G: 6.0169 D(x): 0.9478 D(G(z)): 0.0042 / 0.0041
[89/4000][400/600] Loss_D: 0.0500 Loss_G: 5.2406 D(x): 0.9784 D(G(z)): 0.0127 / 0.0086
[89/4000][500/600] Loss_D: 0.0906 Loss_G: 5.0283 D(x): 0.9686 D(G(z)): 0.0115 / 0.0113
[90/4000][0/600] Loss_D: 0.0364 Loss_G: 7.2007 D(x): 0.9837 D(G(z)): 0.0053 / 0.0017
[90/4000][100/600] Loss_D: 0.0218 Loss_G: 5.0574 D(x): 0.9960 D(G(z)): 0.0167 / 0.0164
[90/4000][200/600] Loss_D: 0.0721 Loss_G: 7.4805 D(x): 0.9646 D(G(z)): 0.0027 / 0.0009
[90/4000][300/600] Loss_D: 0.0114 Loss_G: 6.3896 D(x): 0.9956 D(G(z)): 0.0068 / 0.0029
[90/4000][400/600] Loss_D: 0.0985 Loss_G: 7.1734 D(x): 0.9397 D(G(z)): 0.0037 / 0.0020
[90/4000][500/600] Loss_D: 0.0284 Loss_G: 4.8324 D(x): 0.9906 D(G(z)): 0.0164 / 0.0157
[91/4000][0/600] Loss_D: 0.0147 Loss_G: 6.0541 D(x): 0.9942 D(G(z)): 0.0087 / 0.0038
[91/4000][100/600] Loss_D: 0.0271 Loss_G: 5.6182 D(x): 0.9902 D(G(z)): 0.0111 / 0.0083
[91/4000][200/600] Loss_D: 0.0172 Loss_G: 5.0920 D(x): 0.9954 D(G(z)): 0.0123 / 0.0106
[91/4000][300/600] Loss_D: 0.0331 Loss_G: 4.8196 D(x): 0.9927 D(G(z)): 0.0230 / 0.0153
[91/4000][400/600] Loss_D: 0.0237 Loss_G: 7.1809 D(x): 0.9875 D(G(z)): 0.0037 / 0.0015
[91/4000][500/600] Loss_D: 0.0392 Loss_G: 5.3551 D(x): 0.9702 D(G(z)): 0.0046 / 0.0070
[92/4000][0/600] Loss_D: 0.0116 Loss_G: 5.5869 D(x): 0.9954 D(G(z)): 0.0068 / 0.0054
[92/4000][100/600] Loss_D: 0.0136 Loss_G: 5.5625 D(x): 0.9950 D(G(z)): 0.0078 / 0.0053
[92/4000][200/600] Loss_D: 0.0123 Loss_G: 6.0353 D(x): 0.9938 D(G(z)): 0.0036 / 0.0033
[92/4000][300/600] Loss_D: 0.0144 Loss_G: 4.4787 D(x): 0.9992 D(G(z)): 0.0134 / 0.0171
[92/4000][400/600] Loss_D: 0.0168 Loss_G: 5.2140 D(x): 0.9971 D(G(z)): 0.0136 / 0.0077
[92/4000][500/600] Loss_D: 0.0664 Loss_G: 5.1415 D(x): 0.9706 D(G(z)): 0.0093 / 0.0089
[93/4000][0/600] Loss_D: 0.0300 Loss_G: 6.3588 D(x): 0.9818 D(G(z)): 0.0060 / 0.0030
[93/4000][100/600] Loss_D: 0.0157 Loss_G: 5.5486 D(x): 0.9939 D(G(z)): 0.0091 / 0.0089
[93/4000][200/600] Loss_D: 0.0262 Loss_G: 5.0458 D(x): 0.9995 D(G(z)): 0.0250 / 0.0112
[93/4000][300/600] Loss_D: 0.1272 Loss_G: 6.3623 D(x): 0.9274 D(G(z)): 0.0007 / 0.0033
[93/4000][400/600] Loss_D: 0.0594 Loss_G: 4.3573 D(x): 0.9722 D(G(z)): 0.0212 / 0.0248
[93/4000][500/600] Loss_D: 0.0270 Loss_G: 6.1542 D(x): 0.9835 D(G(z)): 0.0053 / 0.0038
[94/4000][0/600] Loss_D: 0.0185 Loss_G: 4.1406 D(x): 0.9990 D(G(z)): 0.0171 / 0.0263
[94/4000][100/600] Loss_D: 0.0434 Loss_G: 4.4047 D(x): 0.9874 D(G(z)): 0.0282 / 0.0187
[94/4000][200/600] Loss_D: 0.0119 Loss_G: 6.2143 D(x): 0.9948 D(G(z)): 0.0065 / 0.0031
[94/4000][300/600] Loss_D: 0.0290 Loss_G: 5.9852 D(x): 0.9805 D(G(z)): 0.0045 / 0.0042
[94/4000][400/600] Loss_D: 0.0185 Loss_G: 6.0606 D(x): 0.9905 D(G(z)): 0.0083 / 0.0039
[94/4000][500/600] Loss_D: 0.0268 Loss_G: 4.4209 D(x): 0.9937 D(G(z)): 0.0196 / 0.0197
[95/4000][0/600] Loss_D: 0.0067 Loss_G: 7.1059 D(x): 0.9982 D(G(z)): 0.0048 / 0.0018
[95/4000][100/600] Loss_D: 0.0342 Loss_G: 4.8496 D(x): 0.9990 D(G(z)): 0.0319 / 0.0146
[95/4000][200/600] Loss_D: 0.0306 Loss_G: 5.9187 D(x): 0.9890 D(G(z)): 0.0143 / 0.0043
[95/4000][300/600] Loss_D: 0.0116 Loss_G: 5.5698 D(x): 0.9991 D(G(z)): 0.0105 / 0.0097
[95/4000][400/600] Loss_D: 0.0103 Loss_G: 6.2059 D(x): 0.9961 D(G(z)): 0.0062 / 0.0036
[95/4000][500/600] Loss_D: 0.0045 Loss_G: 6.0662 D(x): 0.9984 D(G(z)): 0.0028 / 0.0033
[96/4000][0/600] Loss_D: 0.0059 Loss_G: 5.9425 D(x): 0.9984 D(G(z)): 0.0042 / 0.0038
[96/4000][100/600] Loss_D: 0.0466 Loss_G: 5.9024 D(x): 0.9725 D(G(z)): 0.0065 / 0.0058
[96/4000][200/600] Loss_D: 0.0318 Loss_G: 7.9483 D(x): 0.9833 D(G(z)): 0.0022 / 0.0006
[96/4000][300/600] Loss_D: 0.0112 Loss_G: 10.0428 D(x): 0.9898 D(G(z)): 0.0002 / 0.0001
[96/4000][400/600] Loss_D: 0.0401 Loss_G: 6.0794 D(x): 0.9806 D(G(z)): 0.0136 / 0.0034
[96/4000][500/600] Loss_D: 0.0129 Loss_G: 6.0008 D(x): 0.9947 D(G(z)): 0.0063 / 0.0040
[97/4000][0/600] Loss_D: 0.0068 Loss_G: 5.9932 D(x): 0.9994 D(G(z)): 0.0061 / 0.0051
[97/4000][100/600] Loss_D: 0.0117 Loss_G: 5.1045 D(x): 0.9994 D(G(z)): 0.0109 / 0.0092
[97/4000][200/600] Loss_D: 0.0143 Loss_G: 5.7177 D(x): 0.9995 D(G(z)): 0.0133 / 0.0061
[97/4000][300/600] Loss_D: 0.0965 Loss_G: 5.5103 D(x): 0.9570 D(G(z)): 0.0033 / 0.0061
[97/4000][400/600] Loss_D: 0.0885 Loss_G: 3.3838 D(x): 0.9811 D(G(z)): 0.0602 / 0.0395
[97/4000][500/600] Loss_D: 0.1144 Loss_G: 3.8144 D(x): 0.9663 D(G(z)): 0.0266 / 0.0295
[98/4000][0/600] Loss_D: 0.0107 Loss_G: 5.2687 D(x): 0.9979 D(G(z)): 0.0085 / 0.0070
[98/4000][100/600] Loss_D: 0.0098 Loss_G: 9.2806 D(x): 0.9914 D(G(z)): 0.0006 / 0.0002
[98/4000][200/600] Loss_D: 0.0138 Loss_G: 9.3767 D(x): 0.9881 D(G(z)): 0.0013 / 0.0002
[98/4000][300/600] Loss_D: 0.0146 Loss_G: 5.8655 D(x): 0.9944 D(G(z)): 0.0087 / 0.0084
[98/4000][400/600] Loss_D: 0.0174 Loss_G: 4.7542 D(x): 0.9986 D(G(z)): 0.0157 / 0.0159
[98/4000][500/600] Loss_D: 0.0220 Loss_G: 5.7759 D(x): 0.9856 D(G(z)): 0.0058 / 0.0043
[99/4000][0/600] Loss_D: 0.0484 Loss_G: 8.9349 D(x): 0.9676 D(G(z)): 0.0002 / 0.0002
[99/4000][100/600] Loss_D: 0.0556 Loss_G: 4.8826 D(x): 0.9830 D(G(z)): 0.0288 / 0.0107
[99/4000][200/600] Loss_D: 0.0179 Loss_G: 5.6804 D(x): 0.9972 D(G(z)): 0.0147 / 0.0044
[99/4000][300/600] Loss_D: 0.0238 Loss_G: 4.7208 D(x): 0.9969 D(G(z)): 0.0200 / 0.0119
[99/4000][400/600] Loss_D: 0.0479 Loss_G: 4.2749 D(x): 0.9873 D(G(z)): 0.0304 / 0.0264
[99/4000][500/600] Loss_D: 0.0094 Loss_G: 5.9392 D(x): 0.9982 D(G(z)): 0.0075 / 0.0056
[100/4000][0/600] Loss_D: 0.0258 Loss_G: 6.3243 D(x): 0.9864 D(G(z)): 0.0075 / 0.0032
[100/4000][100/600] Loss_D: 0.0249 Loss_G: 5.3388 D(x): 0.9882 D(G(z)): 0.0096 / 0.0079
[100/4000][200/600] Loss_D: 0.0518 Loss_G: 4.3115 D(x): 0.9999 D(G(z)): 0.0486 / 0.0212
[100/4000][300/600] Loss_D: 0.0293 Loss_G: 6.2319 D(x): 0.9779 D(G(z)): 0.0042 / 0.0026
[100/4000][400/600] Loss_D: 0.0484 Loss_G: 5.2291 D(x): 0.9790 D(G(z)): 0.0145 / 0.0087
[100/4000][500/600] Loss_D: 0.0424 Loss_G: 4.4553 D(x): 0.9846 D(G(z)): 0.0159 / 0.0148
[101/4000][0/600] Loss_D: 0.0172 Loss_G: 6.2510 D(x): 0.9899 D(G(z)): 0.0063 / 0.0030
[101/4000][100/600] Loss_D: 0.0114 Loss_G: 6.0162 D(x): 0.9942 D(G(z)): 0.0055 / 0.0033
[101/4000][200/600] Loss_D: 0.0335 Loss_G: 8.9405 D(x): 0.9726 D(G(z)): 0.0010 / 0.0002
[101/4000][300/600] Loss_D: 0.0225 Loss_G: 5.6299 D(x): 0.9884 D(G(z)): 0.0097 / 0.0050
[101/4000][400/600] Loss_D: 0.0166 Loss_G: 6.4816 D(x): 0.9885 D(G(z)): 0.0043 / 0.0029
[101/4000][500/600] Loss_D: 0.0269 Loss_G: 4.2495 D(x): 0.9972 D(G(z)): 0.0235 / 0.0196
[102/4000][0/600] Loss_D: 0.0093 Loss_G: 5.6286 D(x): 0.9965 D(G(z)): 0.0056 / 0.0052
[102/4000][100/600] Loss_D: 0.0274 Loss_G: 5.7548 D(x): 0.9956 D(G(z)): 0.0202 / 0.0099
[102/4000][200/600] Loss_D: 0.0138 Loss_G: 6.2278 D(x): 0.9934 D(G(z)): 0.0065 / 0.0028
[102/4000][300/600] Loss_D: 0.0110 Loss_G: 6.0323 D(x): 0.9966 D(G(z)): 0.0075 / 0.0034
[102/4000][400/600] Loss_D: 0.0922 Loss_G: 4.9933 D(x): 0.9766 D(G(z)): 0.0181 / 0.0107
[102/4000][500/600] Loss_D: 0.0203 Loss_G: 8.2900 D(x): 0.9846 D(G(z)): 0.0006 / 0.0005
[103/4000][0/600] Loss_D: 0.0070 Loss_G: 6.3482 D(x): 0.9965 D(G(z)): 0.0034 / 0.0028
[103/4000][100/600] Loss_D: 0.0279 Loss_G: 5.2230 D(x): 0.9986 D(G(z)): 0.0257 / 0.0131
[103/4000][200/600] Loss_D: 0.0411 Loss_G: 4.8738 D(x): 0.9981 D(G(z)): 0.0369 / 0.0145
[103/4000][300/600] Loss_D: 0.0203 Loss_G: 4.2419 D(x): 0.9989 D(G(z)): 0.0187 / 0.0241
[103/4000][400/600] Loss_D: 0.0982 Loss_G: 6.5540 D(x): 0.9408 D(G(z)): 0.0033 / 0.0027
[103/4000][500/600] Loss_D: 0.0249 Loss_G: 5.8928 D(x): 0.9861 D(G(z)): 0.0052 / 0.0039
[104/4000][0/600] Loss_D: 0.1213 Loss_G: 2.5168 D(x): 0.9996 D(G(z)): 0.1049 / 0.1417
[104/4000][100/600] Loss_D: 0.0107 Loss_G: 7.3029 D(x): 0.9925 D(G(z)): 0.0027 / 0.0026
[104/4000][200/600] Loss_D: 0.0081 Loss_G: 5.6789 D(x): 0.9993 D(G(z)): 0.0074 / 0.0056
[104/4000][300/600] Loss_D: 0.0650 Loss_G: 5.0095 D(x): 0.9649 D(G(z)): 0.0074 / 0.0082
[104/4000][400/600] Loss_D: 0.0436 Loss_G: 4.7850 D(x): 0.9826 D(G(z)): 0.0210 / 0.0141
[104/4000][500/600] Loss_D: 0.0152 Loss_G: 5.0897 D(x): 0.9979 D(G(z)): 0.0128 / 0.0102
[105/4000][0/600] Loss_D: 0.0434 Loss_G: 4.0893 D(x): 0.9983 D(G(z)): 0.0402 / 0.0238
[105/4000][100/600] Loss_D: 0.0107 Loss_G: 6.4639 D(x): 0.9944 D(G(z)): 0.0047 / 0.0043
[105/4000][200/600] Loss_D: 0.0242 Loss_G: 5.2817 D(x): 0.9938 D(G(z)): 0.0174 / 0.0098
[105/4000][300/600] Loss_D: 0.0172 Loss_G: 5.8718 D(x): 0.9909 D(G(z)): 0.0051 / 0.0048
[105/4000][400/600] Loss_D: 0.0379 Loss_G: 4.5974 D(x): 0.9933 D(G(z)): 0.0299 / 0.0150
[105/4000][500/600] Loss_D: 0.0279 Loss_G: 4.8662 D(x): 0.9886 D(G(z)): 0.0147 / 0.0107
[106/4000][0/600] Loss_D: 0.1050 Loss_G: 9.4208 D(x): 0.9368 D(G(z)): 0.0002 / 0.0002
[106/4000][100/600] Loss_D: 0.0381 Loss_G: 5.2959 D(x): 0.9826 D(G(z)): 0.0074 / 0.0076
[106/4000][200/600] Loss_D: 0.0334 Loss_G: 4.6999 D(x): 0.9856 D(G(z)): 0.0151 / 0.0155
[106/4000][300/600] Loss_D: 0.0128 Loss_G: 6.2805 D(x): 0.9908 D(G(z)): 0.0031 / 0.0029
[106/4000][400/600] Loss_D: 0.0192 Loss_G: 4.9422 D(x): 0.9984 D(G(z)): 0.0172 / 0.0139
[106/4000][500/600] Loss_D: 0.0160 Loss_G: 5.1852 D(x): 0.9925 D(G(z)): 0.0078 / 0.0081
[107/4000][0/600] Loss_D: 0.1360 Loss_G: 10.6186 D(x): 0.9300 D(G(z)): 0.0001 / 0.0001
[107/4000][100/600] Loss_D: 0.0200 Loss_G: 5.9243 D(x): 0.9920 D(G(z)): 0.0110 / 0.0052
[107/4000][200/600] Loss_D: 0.0129 Loss_G: 7.1352 D(x): 0.9903 D(G(z)): 0.0015 / 0.0014
[107/4000][300/600] Loss_D: 0.0634 Loss_G: 5.8193 D(x): 0.9571 D(G(z)): 0.0031 / 0.0048
[107/4000][400/600] Loss_D: 0.0139 Loss_G: 5.2395 D(x): 0.9926 D(G(z)): 0.0062 / 0.0086
[107/4000][500/600] Loss_D: 0.0492 Loss_G: 6.8845 D(x): 0.9648 D(G(z)): 0.0010 / 0.0014
[108/4000][0/600] Loss_D: 0.3708 Loss_G: 12.0550 D(x): 0.8057 D(G(z)): 0.0000 / 0.0000
[108/4000][100/600] Loss_D: 0.0548 Loss_G: 4.4697 D(x): 0.9912 D(G(z)): 0.0373 / 0.0226
[108/4000][200/600] Loss_D: 0.0254 Loss_G: 4.4543 D(x): 0.9970 D(G(z)): 0.0218 / 0.0198
[108/4000][300/600] Loss_D: 0.0816 Loss_G: 3.8452 D(x): 0.9638 D(G(z)): 0.0254 / 0.0316
[108/4000][400/600] Loss_D: 0.0264 Loss_G: 5.7544 D(x): 0.9867 D(G(z)): 0.0090 / 0.0057
[108/4000][500/600] Loss_D: 0.0186 Loss_G: 4.5680 D(x): 0.9945 D(G(z)): 0.0116 / 0.0180
[109/4000][0/600] Loss_D: 0.0572 Loss_G: 4.6743 D(x): 0.9989 D(G(z)): 0.0533 / 0.0142
[109/4000][100/600] Loss_D: 0.0258 Loss_G: 5.6626 D(x): 0.9838 D(G(z)): 0.0037 / 0.0045
[109/4000][200/600] Loss_D: 0.0025 Loss_G: 7.1250 D(x): 1.0000 D(G(z)): 0.0024 / 0.0022
[109/4000][300/600] Loss_D: 0.0041 Loss_G: 8.1162 D(x): 0.9974 D(G(z)): 0.0015 / 0.0006
[109/4000][400/600] Loss_D: 0.0393 Loss_G: 6.9964 D(x): 0.9730 D(G(z)): 0.0022 / 0.0013
[109/4000][500/600] Loss_D: 0.0142 Loss_G: 6.3517 D(x): 0.9905 D(G(z)): 0.0034 / 0.0028
[110/4000][0/600] Loss_D: 0.0137 Loss_G: 5.5542 D(x): 0.9954 D(G(z)): 0.0086 / 0.0067
[110/4000][100/600] Loss_D: 0.0198 Loss_G: 5.9869 D(x): 0.9917 D(G(z)): 0.0069 / 0.0071
[110/4000][200/600] Loss_D: 0.0602 Loss_G: 4.4377 D(x): 0.9999 D(G(z)): 0.0556 / 0.0232
[110/4000][300/600] Loss_D: 0.0048 Loss_G: 7.4880 D(x): 0.9971 D(G(z)): 0.0019 / 0.0009
[110/4000][400/600] Loss_D: 0.0302 Loss_G: 4.5230 D(x): 0.9969 D(G(z)): 0.0262 / 0.0171
[110/4000][500/600] Loss_D: 0.0082 Loss_G: 7.1025 D(x): 0.9947 D(G(z)): 0.0022 / 0.0015
[111/4000][0/600] Loss_D: 0.0069 Loss_G: 9.0722 D(x): 0.9942 D(G(z)): 0.0004 / 0.0002
[111/4000][100/600] Loss_D: 0.0064 Loss_G: 6.3419 D(x): 0.9995 D(G(z)): 0.0058 / 0.0033
[111/4000][200/600] Loss_D: 0.0168 Loss_G: 5.9142 D(x): 0.9999 D(G(z)): 0.0163 / 0.0054
[111/4000][300/600] Loss_D: 0.0095 Loss_G: 6.2018 D(x): 0.9972 D(G(z)): 0.0067 / 0.0031
[111/4000][400/600] Loss_D: 0.0448 Loss_G: 6.3646 D(x): 0.9674 D(G(z)): 0.0044 / 0.0028
[111/4000][500/600] Loss_D: 0.0558 Loss_G: 7.4167 D(x): 0.9743 D(G(z)): 0.0059 / 0.0041
[112/4000][0/600] Loss_D: 0.1838 Loss_G: 13.9284 D(x): 0.9066 D(G(z)): 0.0000 / 0.0000
[112/4000][100/600] Loss_D: 0.0308 Loss_G: 4.4620 D(x): 0.9907 D(G(z)): 0.0188 / 0.0210
[112/4000][200/600] Loss_D: 0.0435 Loss_G: 5.9615 D(x): 0.9724 D(G(z)): 0.0075 / 0.0039
[112/4000][300/600] Loss_D: 0.0082 Loss_G: 6.9072 D(x): 0.9939 D(G(z)): 0.0018 / 0.0018
[112/4000][400/600] Loss_D: 0.0857 Loss_G: 4.9127 D(x): 0.9574 D(G(z)): 0.0101 / 0.0133
[112/4000][500/600] Loss_D: 0.0208 Loss_G: 5.8020 D(x): 0.9864 D(G(z)): 0.0050 / 0.0042
[113/4000][0/600] Loss_D: 0.0396 Loss_G: 7.9266 D(x): 0.9812 D(G(z)): 0.0015 / 0.0007
[113/4000][100/600] Loss_D: 0.0145 Loss_G: 5.3031 D(x): 0.9989 D(G(z)): 0.0132 / 0.0065
[113/4000][200/600] Loss_D: 0.0290 Loss_G: 6.0687 D(x): 0.9852 D(G(z)): 0.0021 / 0.0032
[113/4000][300/600] Loss_D: 0.0210 Loss_G: 5.5922 D(x): 0.9906 D(G(z)): 0.0085 / 0.0070
[113/4000][400/600] Loss_D: 0.0341 Loss_G: 4.8368 D(x): 0.9881 D(G(z)): 0.0202 / 0.0142
[113/4000][500/600] Loss_D: 0.0077 Loss_G: 5.5637 D(x): 0.9989 D(G(z)): 0.0065 / 0.0055
[114/4000][0/600] Loss_D: 0.0138 Loss_G: 6.6886 D(x): 0.9961 D(G(z)): 0.0086 / 0.0044
[114/4000][100/600] Loss_D: 0.0536 Loss_G: 5.9556 D(x): 0.9859 D(G(z)): 0.0168 / 0.0052
[114/4000][200/600] Loss_D: 0.0112 Loss_G: 6.0862 D(x): 0.9972 D(G(z)): 0.0082 / 0.0041
[114/4000][300/600] Loss_D: 0.0097 Loss_G: 5.2874 D(x): 0.9993 D(G(z)): 0.0089 / 0.0074
[114/4000][400/600] Loss_D: 0.0244 Loss_G: 5.1328 D(x): 0.9978 D(G(z)): 0.0215 / 0.0098
[114/4000][500/600] Loss_D: 0.0153 Loss_G: 6.4439 D(x): 0.9900 D(G(z)): 0.0041 / 0.0029
[115/4000][0/600] Loss_D: 0.0166 Loss_G: 5.1101 D(x): 0.9962 D(G(z)): 0.0126 / 0.0074
[115/4000][100/600] Loss_D: 0.0319 Loss_G: 4.7173 D(x): 0.9987 D(G(z)): 0.0296 / 0.0164
[115/4000][200/600] Loss_D: 0.0036 Loss_G: 7.5097 D(x): 0.9998 D(G(z)): 0.0033 / 0.0009
[115/4000][300/600] Loss_D: 0.0121 Loss_G: 6.1149 D(x): 0.9939 D(G(z)): 0.0054 / 0.0037
[115/4000][400/600] Loss_D: 0.0391 Loss_G: 4.8112 D(x): 0.9994 D(G(z)): 0.0369 / 0.0152
[115/4000][500/600] Loss_D: 0.0090 Loss_G: 5.7154 D(x): 0.9985 D(G(z)): 0.0075 / 0.0052
[116/4000][0/600] Loss_D: 0.0276 Loss_G: 5.8334 D(x): 0.9860 D(G(z)): 0.0081 / 0.0064
[116/4000][100/600] Loss_D: 0.0048 Loss_G: 7.3192 D(x): 0.9974 D(G(z)): 0.0021 / 0.0011
[116/4000][200/600] Loss_D: 0.0106 Loss_G: 5.4561 D(x): 0.9988 D(G(z)): 0.0093 / 0.0082
[116/4000][300/600] Loss_D: 0.0141 Loss_G: 5.9709 D(x): 0.9999 D(G(z)): 0.0138 / 0.0076
[116/4000][400/600] Loss_D: 0.0155 Loss_G: 5.9228 D(x): 0.9943 D(G(z)): 0.0083 / 0.0040
[116/4000][500/600] Loss_D: 0.0471 Loss_G: 6.2754 D(x): 0.9822 D(G(z)): 0.0029 / 0.0027
[117/4000][0/600] Loss_D: 0.0057 Loss_G: 6.9664 D(x): 0.9970 D(G(z)): 0.0026 / 0.0015
[117/4000][100/600] Loss_D: 0.0042 Loss_G: 6.7744 D(x): 0.9988 D(G(z)): 0.0030 / 0.0028
[117/4000][200/600] Loss_D: 0.0058 Loss_G: 6.9366 D(x): 0.9976 D(G(z)): 0.0033 / 0.0016
[117/4000][300/600] Loss_D: 0.0072 Loss_G: 7.2163 D(x): 0.9963 D(G(z)): 0.0030 / 0.0011
[117/4000][400/600] Loss_D: 0.0194 Loss_G: 5.4542 D(x): 0.9982 D(G(z)): 0.0173 / 0.0066
[117/4000][500/600] Loss_D: 0.0129 Loss_G: 6.6851 D(x): 0.9906 D(G(z)): 0.0025 / 0.0022
[118/4000][0/600] Loss_D: 0.0121 Loss_G: 9.3434 D(x): 0.9918 D(G(z)): 0.0003 / 0.0001
[118/4000][100/600] Loss_D: 0.0146 Loss_G: 4.9553 D(x): 0.9948 D(G(z)): 0.0087 / 0.0104
[118/4000][200/600] Loss_D: 0.0052 Loss_G: 6.8403 D(x): 0.9996 D(G(z)): 0.0047 / 0.0016
[118/4000][300/600] Loss_D: 0.0241 Loss_G: 4.7693 D(x): 0.9965 D(G(z)): 0.0201 / 0.0152
[118/4000][400/600] Loss_D: 0.0607 Loss_G: 4.8911 D(x): 0.9587 D(G(z)): 0.0104 / 0.0115
[118/4000][500/600] Loss_D: 0.0109 Loss_G: 7.0426 D(x): 0.9913 D(G(z)): 0.0017 / 0.0014
[119/4000][0/600] Loss_D: 0.0153 Loss_G: 6.1536 D(x): 0.9989 D(G(z)): 0.0137 / 0.0046
[119/4000][100/600] Loss_D: 0.0243 Loss_G: 7.8840 D(x): 0.9818 D(G(z)): 0.0006 / 0.0007
[119/4000][200/600] Loss_D: 0.0051 Loss_G: 6.0567 D(x): 0.9991 D(G(z)): 0.0042 / 0.0040
[119/4000][300/600] Loss_D: 0.0186 Loss_G: 10.6132 D(x): 0.9839 D(G(z)): 0.0001 / 0.0001
[119/4000][400/600] Loss_D: 0.1897 Loss_G: 7.2248 D(x): 0.9085 D(G(z)): 0.0005 / 0.0015
[119/4000][500/600] Loss_D: 0.0275 Loss_G: 6.1164 D(x): 0.9827 D(G(z)): 0.0053 / 0.0041
[120/4000][0/600] Loss_D: 0.0152 Loss_G: 5.9098 D(x): 0.9966 D(G(z)): 0.0116 / 0.0043
[120/4000][100/600] Loss_D: 0.0110 Loss_G: 6.1689 D(x): 0.9959 D(G(z)): 0.0062 / 0.0055
[120/4000][200/600] Loss_D: 0.0425 Loss_G: 4.2534 D(x): 0.9930 D(G(z)): 0.0305 / 0.0247
[120/4000][300/600] Loss_D: 0.0254 Loss_G: 4.4932 D(x): 0.9998 D(G(z)): 0.0245 / 0.0172
[120/4000][400/600] Loss_D: 0.0268 Loss_G: 4.5935 D(x): 0.9993 D(G(z)): 0.0256 / 0.0134
[120/4000][500/600] Loss_D: 0.1093 Loss_G: 6.7108 D(x): 0.9433 D(G(z)): 0.0007 / 0.0022
[121/4000][0/600] Loss_D: 0.0129 Loss_G: 4.8092 D(x): 0.9975 D(G(z)): 0.0102 / 0.0119
[121/4000][100/600] Loss_D: 0.0073 Loss_G: 6.0256 D(x): 0.9988 D(G(z)): 0.0060 / 0.0048
[121/4000][200/600] Loss_D: 0.0089 Loss_G: 7.0662 D(x): 0.9946 D(G(z)): 0.0032 / 0.0022
[121/4000][300/600] Loss_D: 0.0179 Loss_G: 4.9280 D(x): 0.9998 D(G(z)): 0.0174 / 0.0116
[121/4000][400/600] Loss_D: 0.0292 Loss_G: 5.0155 D(x): 0.9951 D(G(z)): 0.0236 / 0.0099
[121/4000][500/600] Loss_D: 0.1024 Loss_G: 2.8155 D(x): 0.9901 D(G(z)): 0.0841 / 0.0940
[122/4000][0/600] Loss_D: 0.0060 Loss_G: 7.6053 D(x): 0.9958 D(G(z)): 0.0011 / 0.0008
[122/4000][100/600] Loss_D: 0.0266 Loss_G: 5.8198 D(x): 0.9908 D(G(z)): 0.0129 / 0.0057
[122/4000][200/600] Loss_D: 0.0762 Loss_G: 5.1310 D(x): 0.9449 D(G(z)): 0.0046 / 0.0092
[122/4000][300/600] Loss_D: 0.0347 Loss_G: 8.2439 D(x): 0.9735 D(G(z)): 0.0006 / 0.0007
[122/4000][400/600] Loss_D: 0.0223 Loss_G: 6.3804 D(x): 0.9890 D(G(z)): 0.0044 / 0.0027
[122/4000][500/600] Loss_D: 0.0033 Loss_G: 7.9465 D(x): 0.9976 D(G(z)): 0.0008 / 0.0007
[123/4000][0/600] Loss_D: 0.0082 Loss_G: 5.3540 D(x): 0.9999 D(G(z)): 0.0080 / 0.0075
[123/4000][100/600] Loss_D: 0.0282 Loss_G: 4.6891 D(x): 0.9961 D(G(z)): 0.0233 / 0.0126
[123/4000][200/600] Loss_D: 0.2100 Loss_G: 10.0530 D(x): 0.9045 D(G(z)): 0.0005 / 0.0001
[123/4000][300/600] Loss_D: 0.0269 Loss_G: 5.0629 D(x): 0.9922 D(G(z)): 0.0183 / 0.0102
[123/4000][400/600] Loss_D: 0.0717 Loss_G: 4.8123 D(x): 0.9730 D(G(z)): 0.0280 / 0.0164
[123/4000][500/600] Loss_D: 0.0197 Loss_G: 6.7354 D(x): 0.9873 D(G(z)): 0.0031 / 0.0029
[124/4000][0/600] Loss_D: 0.0676 Loss_G: 4.2953 D(x): 0.9928 D(G(z)): 0.0549 / 0.0315
[124/4000][100/600] Loss_D: 0.0288 Loss_G: 5.5708 D(x): 0.9936 D(G(z)): 0.0208 / 0.0092
[124/4000][200/600] Loss_D: 0.0285 Loss_G: 5.7548 D(x): 0.9905 D(G(z)): 0.0148 / 0.0058
[124/4000][300/600] Loss_D: 0.0519 Loss_G: 5.7487 D(x): 0.9669 D(G(z)): 0.0043 / 0.0049
[124/4000][400/600] Loss_D: 0.0326 Loss_G: 7.3913 D(x): 0.9768 D(G(z)): 0.0013 / 0.0009
[124/4000][500/600] Loss_D: 0.0167 Loss_G: 5.6859 D(x): 0.9908 D(G(z)): 0.0068 / 0.0058
[125/4000][0/600] Loss_D: 0.2443 Loss_G: 1.9519 D(x): 0.9998 D(G(z)): 0.1975 / 0.2178
[125/4000][100/600] Loss_D: 0.0109 Loss_G: 6.9882 D(x): 0.9946 D(G(z)): 0.0051 / 0.0028
[125/4000][200/600] Loss_D: 0.0342 Loss_G: 5.3307 D(x): 0.9867 D(G(z)): 0.0137 / 0.0088
[125/4000][300/600] Loss_D: 0.0787 Loss_G: 3.0084 D(x): 0.9971 D(G(z)): 0.0684 / 0.0827
[125/4000][400/600] Loss_D: 0.1018 Loss_G: 4.7236 D(x): 0.9588 D(G(z)): 0.0135 / 0.0167
[125/4000][500/600] Loss_D: 0.0271 Loss_G: 5.4433 D(x): 0.9834 D(G(z)): 0.0082 / 0.0091
[126/4000][0/600] Loss_D: 0.2156 Loss_G: 3.0119 D(x): 0.9996 D(G(z)): 0.1757 / 0.0786
[126/4000][100/600] Loss_D: 0.0248 Loss_G: 6.3426 D(x): 0.9842 D(G(z)): 0.0035 / 0.0040
[126/4000][200/600] Loss_D: 0.0403 Loss_G: 4.7559 D(x): 0.9987 D(G(z)): 0.0370 / 0.0155
[126/4000][300/600] Loss_D: 0.0183 Loss_G: 5.8866 D(x): 0.9873 D(G(z)): 0.0036 / 0.0044
[126/4000][400/600] Loss_D: 0.1243 Loss_G: 5.9009 D(x): 0.9466 D(G(z)): 0.0130 / 0.0127
[126/4000][500/600] Loss_D: 0.0101 Loss_G: 6.2085 D(x): 0.9949 D(G(z)): 0.0047 / 0.0032
[127/4000][0/600] Loss_D: 0.0073 Loss_G: 7.6415 D(x): 0.9955 D(G(z)): 0.0024 / 0.0008
[127/4000][100/600] Loss_D: 0.0107 Loss_G: 7.7017 D(x): 0.9916 D(G(z)): 0.0016 / 0.0019
[127/4000][200/600] Loss_D: 0.0204 Loss_G: 6.2806 D(x): 0.9996 D(G(z)): 0.0193 / 0.0045
[127/4000][300/600] Loss_D: 0.0145 Loss_G: 5.6637 D(x): 0.9998 D(G(z)): 0.0141 / 0.0057
[127/4000][400/600] Loss_D: 0.0201 Loss_G: 5.0533 D(x): 0.9951 D(G(z)): 0.0148 / 0.0087
[127/4000][500/600] Loss_D: 0.0227 Loss_G: 4.3197 D(x): 0.9897 D(G(z)): 0.0116 / 0.0173
[128/4000][0/600] Loss_D: 0.0278 Loss_G: 10.5306 D(x): 0.9783 D(G(z)): 0.0005 / 0.0002
[128/4000][100/600] Loss_D: 0.0265 Loss_G: 5.8184 D(x): 0.9854 D(G(z)): 0.0079 / 0.0043
[128/4000][200/600] Loss_D: 0.0716 Loss_G: 4.5634 D(x): 0.9690 D(G(z)): 0.0188 / 0.0175
[128/4000][300/600] Loss_D: 0.0489 Loss_G: 5.6330 D(x): 0.9751 D(G(z)): 0.0039 / 0.0049
[128/4000][400/600] Loss_D: 0.0276 Loss_G: 6.9744 D(x): 0.9781 D(G(z)): 0.0016 / 0.0014
[128/4000][500/600] Loss_D: 0.0200 Loss_G: 5.3758 D(x): 0.9861 D(G(z)): 0.0047 / 0.0062
[129/4000][0/600] Loss_D: 0.0085 Loss_G: 6.0365 D(x): 0.9976 D(G(z)): 0.0060 / 0.0031
[129/4000][100/600] Loss_D: 0.0309 Loss_G: 6.5152 D(x): 0.9874 D(G(z)): 0.0066 / 0.0045
[129/4000][200/600] Loss_D: 0.0058 Loss_G: 6.4276 D(x): 0.9971 D(G(z)): 0.0028 / 0.0023
[129/4000][300/600] Loss_D: 0.0067 Loss_G: 6.4020 D(x): 0.9976 D(G(z)): 0.0042 / 0.0028
[129/4000][400/600] Loss_D: 0.0436 Loss_G: 4.6206 D(x): 0.9981 D(G(z)): 0.0393 / 0.0220
[129/4000][500/600] Loss_D: 0.0076 Loss_G: 6.1370 D(x): 0.9973 D(G(z)): 0.0048 / 0.0040
[130/4000][0/600] Loss_D: 0.1522 Loss_G: 13.8136 D(x): 0.9085 D(G(z)): 0.0000 / 0.0000
[130/4000][100/600] Loss_D: 0.0569 Loss_G: 4.8681 D(x): 0.9778 D(G(z)): 0.0164 / 0.0137
[130/4000][200/600] Loss_D: 0.0117 Loss_G: 6.2998 D(x): 0.9951 D(G(z)): 0.0062 / 0.0035
[130/4000][300/600] Loss_D: 0.0368 Loss_G: 4.7624 D(x): 0.9874 D(G(z)): 0.0204 / 0.0170
[130/4000][400/600] Loss_D: 0.0431 Loss_G: 4.6366 D(x): 0.9963 D(G(z)): 0.0366 / 0.0242
[130/4000][500/600] Loss_D: 0.0107 Loss_G: 7.0177 D(x): 0.9920 D(G(z)): 0.0021 / 0.0018
[131/4000][0/600] Loss_D: 0.0272 Loss_G: 5.0225 D(x): 0.9953 D(G(z)): 0.0218 / 0.0109
[131/4000][100/600] Loss_D: 0.0114 Loss_G: 7.6320 D(x): 0.9914 D(G(z)): 0.0018 / 0.0008
[131/4000][200/600] Loss_D: 0.0178 Loss_G: 5.1538 D(x): 0.9980 D(G(z)): 0.0155 / 0.0102
[131/4000][300/600] Loss_D: 0.0199 Loss_G: 5.0208 D(x): 0.9890 D(G(z)): 0.0077 / 0.0099
[131/4000][400/600] Loss_D: 0.0354 Loss_G: 5.1592 D(x): 0.9887 D(G(z)): 0.0154 / 0.0098
[131/4000][500/600] Loss_D: 0.0106 Loss_G: 5.9569 D(x): 0.9951 D(G(z)): 0.0055 / 0.0043
[132/4000][0/600] Loss_D: 0.0216 Loss_G: 4.7990 D(x): 0.9943 D(G(z)): 0.0150 / 0.0159
[132/4000][100/600] Loss_D: 0.0139 Loss_G: 5.7656 D(x): 0.9972 D(G(z)): 0.0109 / 0.0062
[132/4000][200/600] Loss_D: 0.0097 Loss_G: 5.8126 D(x): 0.9992 D(G(z)): 0.0087 / 0.0049
[132/4000][300/600] Loss_D: 0.0185 Loss_G: 5.0804 D(x): 0.9953 D(G(z)): 0.0132 / 0.0108
[132/4000][400/600] Loss_D: 0.0217 Loss_G: 6.8273 D(x): 0.9865 D(G(z)): 0.0048 / 0.0017
[132/4000][500/600] Loss_D: 0.0459 Loss_G: 5.4517 D(x): 0.9737 D(G(z)): 0.0076 / 0.0086
[133/4000][0/600] Loss_D: 0.1615 Loss_G: 4.1042 D(x): 0.9963 D(G(z)): 0.1211 / 0.0321
[133/4000][100/600] Loss_D: 0.0076 Loss_G: 7.7352 D(x): 0.9935 D(G(z)): 0.0010 / 0.0009
[133/4000][200/600] Loss_D: 0.0175 Loss_G: 5.4649 D(x): 0.9996 D(G(z)): 0.0168 / 0.0070
[133/4000][300/600] Loss_D: 0.1093 Loss_G: 5.7628 D(x): 0.9526 D(G(z)): 0.0041 / 0.0040
[133/4000][400/600] Loss_D: 0.0482 Loss_G: 5.1433 D(x): 0.9756 D(G(z)): 0.0166 / 0.0098
[133/4000][500/600] Loss_D: 0.0116 Loss_G: 5.5569 D(x): 0.9971 D(G(z)): 0.0086 / 0.0053
[134/4000][0/600] Loss_D: 0.0218 Loss_G: 8.3353 D(x): 0.9819 D(G(z)): 0.0013 / 0.0003
[134/4000][100/600] Loss_D: 0.0218 Loss_G: 4.5398 D(x): 0.9989 D(G(z)): 0.0200 / 0.0201
[134/4000][200/600] Loss_D: 0.0444 Loss_G: 4.5679 D(x): 0.9839 D(G(z)): 0.0249 / 0.0192
[134/4000][300/600] Loss_D: 0.0286 Loss_G: 6.7198 D(x): 0.9771 D(G(z)): 0.0029 / 0.0018
[134/4000][400/600] Loss_D: 0.0323 Loss_G: 5.4792 D(x): 0.9817 D(G(z)): 0.0072 / 0.0074
[134/4000][500/600] Loss_D: 0.0249 Loss_G: 5.4787 D(x): 0.9889 D(G(z)): 0.0089 / 0.0088
[135/4000][0/600] Loss_D: 0.0361 Loss_G: 5.5764 D(x): 0.9857 D(G(z)): 0.0105 / 0.0062
[135/4000][100/600] Loss_D: 0.0172 Loss_G: 6.2909 D(x): 0.9886 D(G(z)): 0.0027 / 0.0029
[135/4000][200/600] Loss_D: 0.0176 Loss_G: 6.8411 D(x): 0.9875 D(G(z)): 0.0025 / 0.0021
[135/4000][300/600] Loss_D: 0.0394 Loss_G: 4.0456 D(x): 0.9917 D(G(z)): 0.0291 / 0.0254
[135/4000][400/600] Loss_D: 0.0397 Loss_G: 4.3076 D(x): 0.9961 D(G(z)): 0.0337 / 0.0229
[135/4000][500/600] Loss_D: 0.0049 Loss_G: 5.8307 D(x): 0.9976 D(G(z)): 0.0025 / 0.0047
[136/4000][0/600] Loss_D: 0.0099 Loss_G: 4.9919 D(x): 0.9992 D(G(z)): 0.0090 / 0.0110
[136/4000][100/600] Loss_D: 0.0045 Loss_G: 6.1199 D(x): 0.9998 D(G(z)): 0.0043 / 0.0042
[136/4000][200/600] Loss_D: 0.0472 Loss_G: 6.3436 D(x): 0.9774 D(G(z)): 0.0044 / 0.0028
[136/4000][300/600] Loss_D: 0.1121 Loss_G: 5.6234 D(x): 0.9561 D(G(z)): 0.0031 / 0.0053
[136/4000][400/600] Loss_D: 0.0264 Loss_G: 10.1014 D(x): 0.9763 D(G(z)): 0.0003 / 0.0001
[136/4000][500/600] Loss_D: 0.0153 Loss_G: 6.6431 D(x): 0.9906 D(G(z)): 0.0042 / 0.0041
[137/4000][0/600] Loss_D: 0.0240 Loss_G: 5.1459 D(x): 0.9973 D(G(z)): 0.0206 / 0.0127
[137/4000][100/600] Loss_D: 0.0101 Loss_G: 8.0981 D(x): 0.9911 D(G(z)): 0.0005 / 0.0006
[137/4000][200/600] Loss_D: 0.0438 Loss_G: 6.4494 D(x): 0.9762 D(G(z)): 0.0023 / 0.0035
[137/4000][300/600] Loss_D: 0.0072 Loss_G: 6.0331 D(x): 0.9993 D(G(z)): 0.0064 / 0.0055
[137/4000][400/600] Loss_D: 0.0906 Loss_G: 7.9187 D(x): 0.9376 D(G(z)): 0.0008 / 0.0010
[137/4000][500/600] Loss_D: 0.0158 Loss_G: 5.6069 D(x): 0.9931 D(G(z)): 0.0085 / 0.0075
[138/4000][0/600] Loss_D: 0.0119 Loss_G: 6.0146 D(x): 0.9988 D(G(z)): 0.0104 / 0.0049
[138/4000][100/600] Loss_D: 0.0118 Loss_G: 6.3519 D(x): 0.9943 D(G(z)): 0.0050 / 0.0044
[138/4000][200/600] Loss_D: 0.0247 Loss_G: 5.8790 D(x): 0.9999 D(G(z)): 0.0237 / 0.0052
[138/4000][300/600] Loss_D: 0.0095 Loss_G: 5.1359 D(x): 0.9993 D(G(z)): 0.0086 / 0.0125
[138/4000][400/600] Loss_D: 0.0094 Loss_G: 7.6251 D(x): 0.9949 D(G(z)): 0.0039 / 0.0008
[138/4000][500/600] Loss_D: 0.0205 Loss_G: 6.9626 D(x): 0.9894 D(G(z)): 0.0023 / 0.0019
[139/4000][0/600] Loss_D: 0.0342 Loss_G: 4.7339 D(x): 0.9986 D(G(z)): 0.0315 / 0.0137
[139/4000][100/600] Loss_D: 0.0149 Loss_G: 5.5693 D(x): 0.9922 D(G(z)): 0.0057 / 0.0051
[139/4000][200/600] Loss_D: 0.0106 Loss_G: 5.5929 D(x): 0.9955 D(G(z)): 0.0058 / 0.0060
[139/4000][300/600] Loss_D: 0.0061 Loss_G: 6.5960 D(x): 0.9975 D(G(z)): 0.0035 / 0.0025
[139/4000][400/600] Loss_D: 0.0090 Loss_G: 6.5512 D(x): 0.9996 D(G(z)): 0.0085 / 0.0029
[139/4000][500/600] Loss_D: 0.0343 Loss_G: 4.4598 D(x): 0.9936 D(G(z)): 0.0219 / 0.0387
[140/4000][0/600] Loss_D: 0.0039 Loss_G: 7.3004 D(x): 0.9983 D(G(z)): 0.0021 / 0.0015
[140/4000][100/600] Loss_D: 0.0058 Loss_G: 7.1100 D(x): 0.9962 D(G(z)): 0.0019 / 0.0014
[140/4000][200/600] Loss_D: 0.0289 Loss_G: 6.0716 D(x): 0.9949 D(G(z)): 0.0218 / 0.0061
[140/4000][300/600] Loss_D: 0.0389 Loss_G: 5.7929 D(x): 0.9775 D(G(z)): 0.0032 / 0.0050
[140/4000][400/600] Loss_D: 0.0644 Loss_G: 7.0227 D(x): 0.9678 D(G(z)): 0.0016 / 0.0016
[140/4000][500/600] Loss_D: 0.0227 Loss_G: 6.9640 D(x): 0.9875 D(G(z)): 0.0071 / 0.0056
[141/4000][0/600] Loss_D: 0.0338 Loss_G: 11.8575 D(x): 0.9802 D(G(z)): 0.0001 / 0.0000
[141/4000][100/600] Loss_D: 0.0104 Loss_G: 6.8389 D(x): 0.9939 D(G(z)): 0.0031 / 0.0019
[141/4000][200/600] Loss_D: 0.0205 Loss_G: 8.0190 D(x): 0.9897 D(G(z)): 0.0043 / 0.0006
[141/4000][300/600] Loss_D: 0.0123 Loss_G: 5.6908 D(x): 0.9974 D(G(z)): 0.0093 / 0.0061
[141/4000][400/600] Loss_D: 0.0153 Loss_G: 5.8628 D(x): 0.9926 D(G(z)): 0.0048 / 0.0048
[141/4000][500/600] Loss_D: 0.0340 Loss_G: 5.0269 D(x): 0.9819 D(G(z)): 0.0066 / 0.0119
[142/4000][0/600] Loss_D: 0.0085 Loss_G: 11.5673 D(x): 0.9919 D(G(z)): 0.0001 / 0.0000
[142/4000][100/600] Loss_D: 0.0397 Loss_G: 4.0267 D(x): 0.9965 D(G(z)): 0.0348 / 0.0233
[142/4000][200/600] Loss_D: 0.0613 Loss_G: 4.5401 D(x): 0.9721 D(G(z)): 0.0122 / 0.0183
[142/4000][300/600] Loss_D: 0.0188 Loss_G: 5.1746 D(x): 0.9938 D(G(z)): 0.0107 / 0.0092
[142/4000][400/600] Loss_D: 0.0054 Loss_G: 6.4890 D(x): 0.9983 D(G(z)): 0.0037 / 0.0024
[142/4000][500/600] Loss_D: 0.0383 Loss_G: 4.9180 D(x): 0.9786 D(G(z)): 0.0063 / 0.0107
[143/4000][0/600] Loss_D: 0.0094 Loss_G: 7.5428 D(x): 0.9946 D(G(z)): 0.0037 / 0.0013
[143/4000][100/600] Loss_D: 0.0194 Loss_G: 6.0826 D(x): 0.9985 D(G(z)): 0.0170 / 0.0093
[143/4000][200/600] Loss_D: 0.0140 Loss_G: 5.6264 D(x): 0.9951 D(G(z)): 0.0087 / 0.0063
[143/4000][300/600] Loss_D: 0.0182 Loss_G: 4.9479 D(x): 0.9919 D(G(z)): 0.0094 / 0.0119
[143/4000][400/600] Loss_D: 0.0174 Loss_G: 5.9416 D(x): 0.9885 D(G(z)): 0.0026 / 0.0042
[143/4000][500/600] Loss_D: 0.0114 Loss_G: 5.5360 D(x): 0.9985 D(G(z)): 0.0096 / 0.0089
[144/4000][0/600] Loss_D: 0.0277 Loss_G: 5.0499 D(x): 0.9999 D(G(z)): 0.0265 / 0.0114
[144/4000][100/600] Loss_D: 0.0329 Loss_G: 5.7226 D(x): 0.9986 D(G(z)): 0.0299 / 0.0121
[144/4000][200/600] Loss_D: 0.0186 Loss_G: 7.1935 D(x): 0.9995 D(G(z)): 0.0175 / 0.0016
[144/4000][300/600] Loss_D: 0.0353 Loss_G: 4.6866 D(x): 0.9898 D(G(z)): 0.0221 / 0.0234
[144/4000][400/600] Loss_D: 0.0032 Loss_G: 7.2333 D(x): 0.9991 D(G(z)): 0.0023 / 0.0014
[144/4000][500/600] Loss_D: 0.0113 Loss_G: 6.2084 D(x): 0.9937 D(G(z)): 0.0046 / 0.0041
[145/4000][0/600] Loss_D: 0.0518 Loss_G: 3.1433 D(x): 0.9986 D(G(z)): 0.0478 / 0.0642
[145/4000][100/600] Loss_D: 0.0155 Loss_G: 5.4756 D(x): 0.9979 D(G(z)): 0.0130 / 0.0102
[145/4000][200/600] Loss_D: 0.0228 Loss_G: 4.9227 D(x): 0.9972 D(G(z)): 0.0187 / 0.0145
[145/4000][300/600] Loss_D: 0.0101 Loss_G: 6.3743 D(x): 0.9955 D(G(z)): 0.0053 / 0.0036
[145/4000][400/600] Loss_D: 0.0591 Loss_G: 5.8058 D(x): 0.9613 D(G(z)): 0.0042 / 0.0053
[145/4000][500/600] Loss_D: 0.0131 Loss_G: 5.8895 D(x): 0.9971 D(G(z)): 0.0099 / 0.0075
[146/4000][0/600] Loss_D: 0.0182 Loss_G: 5.0087 D(x): 0.9974 D(G(z)): 0.0149 / 0.0106
[146/4000][100/600] Loss_D: 0.0134 Loss_G: 5.9686 D(x): 0.9947 D(G(z)): 0.0073 / 0.0047
[146/4000][200/600] Loss_D: 0.0063 Loss_G: 6.4191 D(x): 0.9996 D(G(z)): 0.0058 / 0.0034
[146/4000][300/600] Loss_D: 0.0105 Loss_G: 6.0354 D(x): 0.9953 D(G(z)): 0.0054 / 0.0035
[146/4000][400/600] Loss_D: 0.0185 Loss_G: 4.5779 D(x): 0.9986 D(G(z)): 0.0168 / 0.0160
[146/4000][500/600] Loss_D: 0.0272 Loss_G: 6.0718 D(x): 0.9867 D(G(z)): 0.0035 / 0.0030
[147/4000][0/600] Loss_D: 0.0458 Loss_G: 4.8736 D(x): 0.9996 D(G(z)): 0.0417 / 0.0191
[147/4000][100/600] Loss_D: 0.0242 Loss_G: 5.4968 D(x): 0.9888 D(G(z)): 0.0117 / 0.0062
[147/4000][200/600] Loss_D: 0.0155 Loss_G: 5.9355 D(x): 0.9991 D(G(z)): 0.0141 / 0.0048
[147/4000][300/600] Loss_D: 0.0061 Loss_G: 8.1483 D(x): 0.9952 D(G(z)): 0.0011 / 0.0004
[147/4000][400/600] Loss_D: 0.0708 Loss_G: 5.4206 D(x): 0.9673 D(G(z)): 0.0063 / 0.0087
[147/4000][500/600] Loss_D: 0.0366 Loss_G: 7.7552 D(x): 0.9709 D(G(z)): 0.0011 / 0.0007
[148/4000][0/600] Loss_D: 0.0630 Loss_G: 9.4768 D(x): 0.9647 D(G(z)): 0.0007 / 0.0002
[148/4000][100/600] Loss_D: 0.0128 Loss_G: 6.1042 D(x): 0.9933 D(G(z)): 0.0056 / 0.0045
[148/4000][200/600] Loss_D: 0.0161 Loss_G: 5.4232 D(x): 0.9988 D(G(z)): 0.0146 / 0.0068
[148/4000][300/600] Loss_D: 0.0498 Loss_G: 8.2150 D(x): 0.9664 D(G(z)): 0.0007 / 0.0004
[148/4000][400/600] Loss_D: 0.0532 Loss_G: 4.6833 D(x): 0.9802 D(G(z)): 0.0287 / 0.0154
[148/4000][500/600] Loss_D: 0.0182 Loss_G: 6.6466 D(x): 0.9866 D(G(z)): 0.0030 / 0.0027
[149/4000][0/600] Loss_D: 0.0532 Loss_G: 4.7696 D(x): 0.9985 D(G(z)): 0.0473 / 0.0201
[149/4000][100/600] Loss_D: 0.0212 Loss_G: 5.2430 D(x): 0.9906 D(G(z)): 0.0100 / 0.0087
[149/4000][200/600] Loss_D: 0.0066 Loss_G: 5.5655 D(x): 0.9991 D(G(z)): 0.0056 / 0.0060
[149/4000][300/600] Loss_D: 0.0216 Loss_G: 7.6108 D(x): 0.9854 D(G(z)): 0.0014 / 0.0009
[149/4000][400/600] Loss_D: 0.0242 Loss_G: 5.5491 D(x): 0.9880 D(G(z)): 0.0097 / 0.0078
[149/4000][500/600] Loss_D: 0.0256 Loss_G: 6.2210 D(x): 0.9800 D(G(z)): 0.0032 / 0.0030
[150/4000][0/600] Loss_D: 0.0137 Loss_G: 6.3040 D(x): 0.9944 D(G(z)): 0.0075 / 0.0038
[150/4000][100/600] Loss_D: 0.0314 Loss_G: 6.2354 D(x): 0.9836 D(G(z)): 0.0076 / 0.0030
[150/4000][200/600] Loss_D: 0.0262 Loss_G: 4.8704 D(x): 0.9918 D(G(z)): 0.0136 / 0.0165
[150/4000][300/600] Loss_D: 0.0158 Loss_G: 7.1361 D(x): 0.9881 D(G(z)): 0.0017 / 0.0013
[150/4000][400/600] Loss_D: 0.0853 Loss_G: 4.9445 D(x): 0.9729 D(G(z)): 0.0158 / 0.0114
[150/4000][500/600] Loss_D: 0.0126 Loss_G: 10.5356 D(x): 0.9894 D(G(z)): 0.0002 / 0.0001
[151/4000][0/600] Loss_D: 0.0209 Loss_G: 5.7399 D(x): 0.9923 D(G(z)): 0.0126 / 0.0061
[151/4000][100/600] Loss_D: 0.0142 Loss_G: 5.5533 D(x): 0.9977 D(G(z)): 0.0115 / 0.0067
[151/4000][200/600] Loss_D: 0.0893 Loss_G: 6.4738 D(x): 0.9669 D(G(z)): 0.0105 / 0.0052
[151/4000][300/600] Loss_D: 0.0559 Loss_G: 10.2411 D(x): 0.9613 D(G(z)): 0.0001 / 0.0001
[151/4000][400/600] Loss_D: 0.0873 Loss_G: 7.9763 D(x): 0.9408 D(G(z)): 0.0007 / 0.0009
[151/4000][500/600] Loss_D: 0.0328 Loss_G: 6.8931 D(x): 0.9846 D(G(z)): 0.0021 / 0.0024
[152/4000][0/600] Loss_D: 0.1628 Loss_G: 9.0938 D(x): 0.9093 D(G(z)): 0.0003 / 0.0002
[152/4000][100/600] Loss_D: 0.0161 Loss_G: 5.1731 D(x): 0.9980 D(G(z)): 0.0139 / 0.0093
[152/4000][200/600] Loss_D: 0.0202 Loss_G: 6.2212 D(x): 0.9943 D(G(z)): 0.0136 / 0.0043
[152/4000][300/600] Loss_D: 0.0298 Loss_G: 5.6929 D(x): 0.9783 D(G(z)): 0.0035 / 0.0052
[152/4000][400/600] Loss_D: 0.0234 Loss_G: 5.2934 D(x): 0.9902 D(G(z)): 0.0099 / 0.0077
[152/4000][500/600] Loss_D: 0.0245 Loss_G: 5.7863 D(x): 0.9863 D(G(z)): 0.0045 / 0.0045
[153/4000][0/600] Loss_D: 0.0325 Loss_G: 4.5546 D(x): 0.9951 D(G(z)): 0.0267 / 0.0163
[153/4000][100/600] Loss_D: 0.0233 Loss_G: 4.8952 D(x): 0.9987 D(G(z)): 0.0212 / 0.0141
[153/4000][200/600] Loss_D: 0.0234 Loss_G: 5.8707 D(x): 0.9946 D(G(z)): 0.0171 / 0.0043
[153/4000][300/600] Loss_D: 0.0151 Loss_G: 6.8283 D(x): 0.9889 D(G(z)): 0.0029 / 0.0033
[153/4000][400/600] Loss_D: 0.0897 Loss_G: 6.0048 D(x): 0.9552 D(G(z)): 0.0037 / 0.0041
[153/4000][500/600] Loss_D: 0.0311 Loss_G: 6.3451 D(x): 0.9777 D(G(z)): 0.0040 / 0.0041
[154/4000][0/600] Loss_D: 0.0185 Loss_G: 4.8008 D(x): 0.9982 D(G(z)): 0.0160 / 0.0158
[154/4000][100/600] Loss_D: 0.0734 Loss_G: 4.0105 D(x): 0.9997 D(G(z)): 0.0682 / 0.0262
[154/4000][200/600] Loss_D: 0.0089 Loss_G: 5.5465 D(x): 0.9970 D(G(z)): 0.0058 / 0.0060
[154/4000][300/600] Loss_D: 0.0378 Loss_G: 5.3256 D(x): 0.9805 D(G(z)): 0.0099 / 0.0124
[154/4000][400/600] Loss_D: 0.0451 Loss_G: 5.7099 D(x): 0.9956 D(G(z)): 0.0373 / 0.0062
[154/4000][500/600] Loss_D: 0.0355 Loss_G: 3.7947 D(x): 0.9842 D(G(z)): 0.0169 / 0.0436
[155/4000][0/600] Loss_D: 0.0268 Loss_G: 4.7697 D(x): 0.9918 D(G(z)): 0.0173 / 0.0151
[155/4000][100/600] Loss_D: 0.0250 Loss_G: 4.5350 D(x): 0.9977 D(G(z)): 0.0220 / 0.0175
[155/4000][200/600] Loss_D: 0.0218 Loss_G: 6.9550 D(x): 0.9844 D(G(z)): 0.0038 / 0.0018
[155/4000][300/600] Loss_D: 0.0108 Loss_G: 8.3249 D(x): 0.9916 D(G(z)): 0.0017 / 0.0005
[155/4000][400/600] Loss_D: 0.0172 Loss_G: 5.5049 D(x): 0.9972 D(G(z)): 0.0139 / 0.0070
[155/4000][500/600] Loss_D: 0.0179 Loss_G: 4.9783 D(x): 0.9979 D(G(z)): 0.0154 / 0.0115
[156/4000][0/600] Loss_D: 0.0227 Loss_G: 6.7181 D(x): 0.9916 D(G(z)): 0.0125 / 0.0027
[156/4000][100/600] Loss_D: 0.0106 Loss_G: 6.8559 D(x): 0.9976 D(G(z)): 0.0080 / 0.0021
[156/4000][200/600] Loss_D: 0.0687 Loss_G: 4.9591 D(x): 0.9842 D(G(z)): 0.0209 / 0.0107
[156/4000][300/600] Loss_D: 0.0263 Loss_G: 5.9491 D(x): 0.9990 D(G(z)): 0.0237 / 0.0135
[156/4000][400/600] Loss_D: 0.0239 Loss_G: 5.0502 D(x): 0.9921 D(G(z)): 0.0145 / 0.0099
[156/4000][500/600] Loss_D: 0.0384 Loss_G: 4.7397 D(x): 0.9770 D(G(z)): 0.0126 / 0.0134
[157/4000][0/600] Loss_D: 0.0169 Loss_G: 9.4412 D(x): 0.9873 D(G(z)): 0.0010 / 0.0002
[157/4000][100/600] Loss_D: 0.0242 Loss_G: 6.4129 D(x): 0.9835 D(G(z)): 0.0044 / 0.0025
[157/4000][200/600] Loss_D: 0.0402 Loss_G: 6.1309 D(x): 0.9797 D(G(z)): 0.0118 / 0.0041
[157/4000][300/600] Loss_D: 0.0234 Loss_G: 6.0488 D(x): 0.9917 D(G(z)): 0.0137 / 0.0046
[157/4000][400/600] Loss_D: 0.0124 Loss_G: 5.4757 D(x): 0.9976 D(G(z)): 0.0098 / 0.0082
[157/4000][500/600] Loss_D: 0.0152 Loss_G: 5.6168 D(x): 0.9900 D(G(z)): 0.0045 / 0.0051
[158/4000][0/600] Loss_D: 0.0325 Loss_G: 4.9181 D(x): 0.9924 D(G(z)): 0.0219 / 0.0139
[158/4000][100/600] Loss_D: 0.0195 Loss_G: 5.9961 D(x): 0.9920 D(G(z)): 0.0110 / 0.0045
[158/4000][200/600] Loss_D: 0.0143 Loss_G: 5.7493 D(x): 0.9955 D(G(z)): 0.0096 / 0.0049
[158/4000][300/600] Loss_D: 0.0139 Loss_G: 7.4042 D(x): 0.9888 D(G(z)): 0.0020 / 0.0011
[158/4000][400/600] Loss_D: 0.0503 Loss_G: 5.6597 D(x): 0.9666 D(G(z)): 0.0070 / 0.0070
[158/4000][500/600] Loss_D: 0.0554 Loss_G: 6.2117 D(x): 0.9617 D(G(z)): 0.0036 / 0.0035
[159/4000][0/600] Loss_D: 0.0023 Loss_G: 7.1288 D(x): 0.9991 D(G(z)): 0.0014 / 0.0020
[159/4000][100/600] Loss_D: 0.0198 Loss_G: 5.1193 D(x): 0.9990 D(G(z)): 0.0183 / 0.0114
[159/4000][200/600] Loss_D: 0.0460 Loss_G: 4.0124 D(x): 0.9994 D(G(z)): 0.0434 / 0.0279
[159/4000][300/600] Loss_D: 0.0124 Loss_G: 4.6961 D(x): 0.9990 D(G(z)): 0.0113 / 0.0116
[159/4000][400/600] Loss_D: 0.0451 Loss_G: 5.1139 D(x): 0.9871 D(G(z)): 0.0224 / 0.0103
[159/4000][500/600] Loss_D: 0.0228 Loss_G: 5.1533 D(x): 0.9878 D(G(z)): 0.0079 / 0.0095
[160/4000][0/600] Loss_D: 0.0167 Loss_G: 5.1639 D(x): 0.9980 D(G(z)): 0.0144 / 0.0119
[160/4000][100/600] Loss_D: 0.0159 Loss_G: 6.4604 D(x): 0.9945 D(G(z)): 0.0099 / 0.0035
[160/4000][200/600] Loss_D: 0.0117 Loss_G: 6.3299 D(x): 0.9938 D(G(z)): 0.0048 / 0.0047
[160/4000][300/600] Loss_D: 0.0164 Loss_G: 5.2374 D(x): 0.9921 D(G(z)): 0.0072 / 0.0083
[160/4000][400/600] Loss_D: 0.0170 Loss_G: 5.0914 D(x): 0.9990 D(G(z)): 0.0157 / 0.0112
[160/4000][500/600] Loss_D: 0.0195 Loss_G: 5.9594 D(x): 0.9838 D(G(z)): 0.0009 / 0.0043
[161/4000][0/600] Loss_D: 0.0161 Loss_G: 10.0137 D(x): 0.9850 D(G(z)): 0.0002 / 0.0001
[161/4000][100/600] Loss_D: 0.0465 Loss_G: 7.9421 D(x): 0.9746 D(G(z)): 0.0013 / 0.0006
[161/4000][200/600] Loss_D: 0.0078 Loss_G: 6.4884 D(x): 0.9979 D(G(z)): 0.0056 / 0.0025
[161/4000][300/600] Loss_D: 0.0244 Loss_G: 5.1404 D(x): 0.9887 D(G(z)): 0.0122 / 0.0082
[161/4000][400/600] Loss_D: 0.0460 Loss_G: 5.7427 D(x): 0.9718 D(G(z)): 0.0053 / 0.0053
[161/4000][500/600] Loss_D: 0.0381 Loss_G: 4.3642 D(x): 0.9738 D(G(z)): 0.0065 / 0.0229
[162/4000][0/600] Loss_D: 0.0384 Loss_G: 5.2230 D(x): 0.9985 D(G(z)): 0.0352 / 0.0094
[162/4000][100/600] Loss_D: 0.0621 Loss_G: 4.9320 D(x): 0.9841 D(G(z)): 0.0248 / 0.0110
[162/4000][200/600] Loss_D: 0.0298 Loss_G: 4.4433 D(x): 0.9934 D(G(z)): 0.0223 / 0.0190
[162/4000][300/600] Loss_D: 0.0184 Loss_G: 7.3162 D(x): 0.9849 D(G(z)): 0.0017 / 0.0011
[162/4000][400/600] Loss_D: 0.0131 Loss_G: 5.4399 D(x): 0.9963 D(G(z)): 0.0092 / 0.0063
[162/4000][500/600] Loss_D: 0.0195 Loss_G: 4.4039 D(x): 0.9937 D(G(z)): 0.0125 / 0.0196
[163/4000][0/600] Loss_D: 0.0514 Loss_G: 4.1065 D(x): 0.9998 D(G(z)): 0.0485 / 0.0292
[163/4000][100/600] Loss_D: 0.0177 Loss_G: 4.6555 D(x): 0.9971 D(G(z)): 0.0142 / 0.0172
[163/4000][200/600] Loss_D: 0.0463 Loss_G: 5.9644 D(x): 0.9726 D(G(z)): 0.0091 / 0.0052
[163/4000][300/600] Loss_D: 0.0196 Loss_G: 5.5772 D(x): 0.9899 D(G(z)): 0.0081 / 0.0076
[163/4000][400/600] Loss_D: 0.0153 Loss_G: 4.7270 D(x): 0.9994 D(G(z)): 0.0144 / 0.0175
[163/4000][500/600] Loss_D: 0.0422 Loss_G: 3.7469 D(x): 0.9952 D(G(z)): 0.0348 / 0.0379
[164/4000][0/600] Loss_D: 0.0056 Loss_G: 6.3223 D(x): 0.9984 D(G(z)): 0.0039 / 0.0031
[164/4000][100/600] Loss_D: 0.1118 Loss_G: 6.2430 D(x): 0.9742 D(G(z)): 0.0036 / 0.0033
[164/4000][200/600] Loss_D: 0.0690 Loss_G: 4.4425 D(x): 0.9540 D(G(z)): 0.0115 / 0.0170
[164/4000][300/600] Loss_D: 0.0487 Loss_G: 3.4780 D(x): 0.9774 D(G(z)): 0.0204 / 0.0613
[164/4000][400/600] Loss_D: 0.0190 Loss_G: 5.4307 D(x): 0.9956 D(G(z)): 0.0142 / 0.0066
[164/4000][500/600] Loss_D: 0.0288 Loss_G: 5.5192 D(x): 0.9837 D(G(z)): 0.0046 / 0.0054
---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-270-8369458edb25> in <module>()
      1 for epoch in range(niter):
----> 2     for i, data in enumerate(dataloader, 0):
      3         ############################
      4         # (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))
      5         ###########################

~/anaconda3/lib/python3.6/site-packages/torch/utils/data/dataloader.py in __next__(self)
    188                 raise StopIteration
    189             indices = self._next_indices()
--> 190             batch = self.collate_fn([self.dataset[i] for i in indices])
    191             if self.pin_memory:
    192                 batch = pin_memory_batch(batch)

~/anaconda3/lib/python3.6/site-packages/torch/utils/data/dataloader.py in <listcomp>(.0)
    188                 raise StopIteration
    189             indices = self._next_indices()
--> 190             batch = self.collate_fn([self.dataset[i] for i in indices])
    191             if self.pin_memory:
    192                 batch = pin_memory_batch(batch)

~/anaconda3/lib/python3.6/site-packages/torchvision-0.1.8-py3.6.egg/torchvision/datasets/mnist.py in __getitem__(self, index)
     53 
     54         if self.transform is not None:
---> 55             img = self.transform(img)
     56 
     57         if self.target_transform is not None:

~/anaconda3/lib/python3.6/site-packages/torchvision-0.1.8-py3.6.egg/torchvision/transforms.py in __call__(self, img)
     27     def __call__(self, img):
     28         for t in self.transforms:
---> 29             img = t(img)
     30         return img
     31 

~/anaconda3/lib/python3.6/site-packages/torchvision-0.1.8-py3.6.egg/torchvision/transforms.py in __call__(self, img)
    138             oh = self.size
    139             ow = int(self.size * w / h)
--> 140             return img.resize((ow, oh), self.interpolation)
    141 
    142 

~/anaconda3/lib/python3.6/site-packages/PIL/Image.py in resize(self, size, resample)
   1710             return self.convert('RGBa').resize(size, resample).convert('RGBA')
   1711 
-> 1712         return self._new(self.im.resize(size, resample))
   1713 
   1714     def rotate(self, angle, resample=NEAREST, expand=0, center=None,

KeyboardInterrupt: 
In [271]:
fake = net_G(fixed_noise)
vutils.save_image(fake.data[:64], '%s/fake_samples3.png' % 'results' ,normalize=True)
In [273]:
from PIL import Image
im = Image.open("results/fake_samples3.png", "r")
plt.imshow(np.array(im))
Out[273]:
<matplotlib.image.AxesImage at 0x7f15245df160>
In [ ]: