DCGAN tutorial - Deepest-Project/Greedy-Survey GitHub Wiki

from __future__ import print_function
#%matplotlib inline
import argparse
import os
import random
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torchvision.utils as vutils
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from IPython.display import HTML

# Set random seem for reproducibility
manualSeed = 999
#manualSeed = random.randint(1, 10000) # use if you want new results
print("Random Seed: ", manualSeed)
random.seed(manualSeed)
torch.manual_seed(manualSeed)
Random Seed:  999





<torch._C.Generator at 0x1f3cfb49a30>
  • dataroot - 데이터셋 루트 폴더
  • workers - DataLoader를 통해서 Data를 불러올 threads 숫자
  • batch_size - 훈련에 쓸 배치 사이즈, DGGAN paper에서는 128을 씀.
  • image_size - 훈련을 위해서 쓰인 이미지 사이즈. 이 코드에서는 default가 64x64이다. 바꾸려면 D, G 구조가를 바뀌야 한다.
  • nc - input image의 채널 수
  • nz - latent vector의 길이
  • ngf - Generator의 feature map의 depth
  • ndf - Discriminator의 feature map의 depth
  • num_epochs - epochs 수
  • lr - learning rate, DCGAN에서는 0.0002
  • beta1 - Adam optimizer의 beta1, 0.5
  • ngpu - GPUs 숫자, 0이면 cpu에서 돌아감.
# Root directory for dataset
dataroot = "../data/celeba"
outf = "./results"

# Number of workers for dataloader
workers = 2

# Batch size during training
batch_size = 128

# Spatial size of training images. All images will be resized to this
#   size using a transformer.
image_size = 64

# Number of channels in the training images. For color images this is 3
nc = 3

# Size of z latent vector (i.e. size of generator input)
nz = 100

# Size of feature maps in generator
ngf = 64

# Size of feature maps in discriminator
ndf = 64

# Number of training epochs
num_epochs = 20

# Learning rate for optimizers
lr = 0.0002

# Beta1 hyperparam for Adam optimizers
beta1 = 0.5

# Number of GPUs available. Use 0 for CPU mode.
ngpu = 1
# We can use an image folder dataset the way we have it setup.
# Create the dataset


'''

torchvision.datasets.ImageFolder(root, transform=None, target_transform=None, loader=<function default_loader>)

torchvision.transforms.ToTensor
    Convert a PIL Image or numpy.ndarray to tensor.
    Converts a PIL Image or numpy.ndarray (H x W x C) in the range [0, 255]
    to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0]
    if the PIL Image belongs to one of the modes
    (L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK, 1) or if the numpy.ndarray has dtype = np.uint8
    In the other cases, tensors are returned without scaling.

torchvision.transforms.functional.normalize(tensor, mean, std, inplace=False)
    Normalize a tensor image with mean and standard deviation.
    Given mean: (M1,...,Mn) and std: (S1,..,Sn) for n channels,
    this transform will normalize each channel of the input torch.*Tensor
    i.e. input[channel] = (input[channel] - mean[channel])
'''
dataset = dset.ImageFolder(root=dataroot,
                           transform=transforms.Compose([
                               transforms.Resize(image_size),
                               transforms.CenterCrop(image_size),
                               transforms.ToTensor(),
                               transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
                           ]))
# Create the dataloader
'''
torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False,
                            sampler=None, batch_sampler=None,
                            num_workers=0, collate_fn=<function default_collate>,
                            pin_memory=False, drop_last=False, timeout=0,
                            worker_init_fn=None)

shuffle: data를 각 epoch마다 다시 shuffle 할 것인지?
sampler (Sampler, optional) – defines the strategy to draw samples from the dataset. If specified, shuffle must be False.
    torch.utils.data.SequentialSampler(data_source)
    torch.utils.data.RandomSampler(data_source, replacement=False, num_samples=None)
    torch.utils.data.SubsetRandomSampler(indices)
    torch.utils.data.WeightedRandomSampler(weights, num_samples, replacement=True)
    torch.utils.data.BatchSampler(sampler, batch_size, drop_last)
    torch.utils.data.distributed.DistributedSampler(dataset, num_replicas=None, rank=None)
'''

dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
                                         shuffle=True, num_workers=workers)

# Decide which device we want to run on
device = torch.device("cuda:0" if (torch.cuda.is_available() and ngpu > 0) else "cpu")

# Plot some training images
real_batch = next(iter(dataloader))
plt.figure(figsize=(8,8))
plt.axis("off")
plt.title("Training Images")

'''
torchvision.utils.make_grid(tensor, nrow=8, padding=2, normalize=False,
                            range=None, scale_each=False, pad_value=0)


'''
plt.imshow(np.transpose(vutils.make_grid(real_batch[0].to(device)[:64], padding=2, normalize=True).cpu(),(1,2,0)))
<matplotlib.image.AxesImage at 0x1f3d2565518>

png

  • Paper에서는 mean = 0.0, stdev=0.2의 Normal distribution을 사용했다고 함.
# custom weights initialization called on netG and netD
def weights_init(m):
    classname = m.__class__.__name__
    if classname.find('Conv') != -1:
        nn.init.normal_(m.weight.data, 0.0, 0.02)
    elif classname.find('BatchNorm') != -1:
        nn.init.normal_(m.weight.data, 1.0, 0.02)
        nn.init.constant_(m.bias.data, 0)

이미지

  • Generator는 latent vector(z)를 data-space 하도록 디자인 된다. 우리의 데이터는 images 이기 때문에 z를 data-space로 만든다는 것은 결국 training images(i.e. 3x64x64)를 만들어낸다는 것이다.

  • strided 2D convolutional transpose layers - 2d batch norm layer - relu activation - tanh fuction [-1, 1].

  • batch norm을 사용한 것이 DCGAN paper의 중요한 기여이다.

  • torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True)

    The parameters kernel_size, stride, padding, dilation can either be - a single int - a tuple of two ints - height x width

  • torch.nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride=1, padding=0, output_padding=0, groups=1, bias=True, dilation=1)

    The parameters kernel_size, stride, padding, dilation can either be - a single int - a tuple of two ints - height x width

# Generator Code

class Generator(nn.Module):
    def __init__(self, ngpu):
        super(Generator, self).__init__()
        self.ngpu = ngpu
        self.main = nn.Sequential(
            # input is Z, going into a convolution
            # nz = 100, ngf = 64
            # out_channel = 512
            nn.ConvTranspose2d( nz, ngf * 8, 4, 1, 0, bias=False),
            nn.BatchNorm2d(ngf * 8),
            nn.ReLU(True),
            # state size. (ngf*8) x 4 x 4
            nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf * 4),
            nn.ReLU(True),
            # state size. (ngf*4) x 8 x 8
            nn.ConvTranspose2d( ngf * 4, ngf * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf * 2),
            nn.ReLU(True),
            # state size. (ngf*2) x 16 x 16
            nn.ConvTranspose2d( ngf * 2, ngf, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf),
            nn.ReLU(True),
            # state size. (ngf) x 32 x 32
            nn.ConvTranspose2d( ngf, nc, 4, 2, 1, bias=False),
            nn.Tanh()
            # state size. (nc) x 64 x 64
        )

    def forward(self, input):
        return self.main(input)

출처: https://zzsza.github.io/data/2018/06/25/upsampling-with-transposed-convolution/

# Create the generator
netG = Generator(ngpu).to(device)

# Handle multi-gpu if desired
if (device.type == 'cuda') and (ngpu > 1):
    netG = nn.DataParallel(netG, list(range(ngpu)))

# Apply the weights_init function to randomly initialize all weights
#  to mean=0, stdev=0.2.
netG.apply(weights_init)

# Print the model
print(netG)
Generator(
  (main): Sequential(
    (0): ConvTranspose2d(100, 512, kernel_size=(4, 4), stride=(1, 1), bias=False)
    (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU(inplace)
    (3): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (5): ReLU(inplace)
    (6): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (7): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (8): ReLU(inplace)
    (9): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (10): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (11): ReLU(inplace)
    (12): ConvTranspose2d(64, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (13): Tanh()
  )
)
  • The DCGAN paper에서 pooling보다 strided convolution이 network가 own pooling function을 배우기 때문에 더 좋다고 말했다.
class Discriminator(nn.Module):
    def __init__(self, ngpu):
        super(Discriminator, self).__init__()
        self.ngpu = ngpu
        self.main = nn.Sequential(
            # input is (nc) x 64 x 64
            nn.Conv2d(nc, ndf, 4, 2, 1, bias=False),
            nn.LeakyReLU(0.2, inplace=True),
            # state size. (ndf) x 32 x 32
            nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf * 2),
            nn.LeakyReLU(0.2, inplace=True),
            # state size. (ndf*2) x 16 x 16
            nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf * 4),
            nn.LeakyReLU(0.2, inplace=True),
            # state size. (ndf*4) x 8 x 8
            nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf * 8),
            nn.LeakyReLU(0.2, inplace=True),
            # state size. (ndf*8) x 4 x 4
            nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False),
            nn.Sigmoid()
        )

    def forward(self, input):
        return self.main(input)
# Create the Discriminator
netD = Discriminator(ngpu).to(device)

# Handle multi-gpu if desired
if (device.type == 'cuda') and (ngpu > 1):
    netD = nn.DataParallel(netD, list(range(ngpu)))

# Apply the weights_init function to randomly initialize all weights
#  to mean=0, stdev=0.2.
netD.apply(weights_init)

# Print the model
print(netD)
Discriminator(
  (main): Sequential(
    (0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (1): LeakyReLU(negative_slope=0.2, inplace)
    (2): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (4): LeakyReLU(negative_slope=0.2, inplace)
    (5): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (6): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (7): LeakyReLU(negative_slope=0.2, inplace)
    (8): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (9): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (10): LeakyReLU(negative_slope=0.2, inplace)
    (11): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), bias=False)
    (12): Sigmoid()
  )
)
  • real label: 1
  • fake lable: 0
  • set two separate optimizers, D, G.
# Initialize BCELoss function
criterion = nn.BCELoss()

# Create batch of latent vectors that we will use to visualize
#  the progression of the generator
fixed_noise = torch.randn(64, nz, 1, 1, device=device)

# Establish convention for real and fake labels during training
real_label = 1
fake_label = 0

# Setup Adam optimizers for both G and D
optimizerD = optim.Adam(netD.parameters(), lr=lr, betas=(beta1, 0.999))
optimizerG = optim.Adam(netG.parameters(), lr=lr, betas=(beta1, 0.999))

  • we will “construct different mini-batches for real and fake” images, and also adjust G’s objective function to maximize logD(G(z))

  • 2 main parts, 1 updates the Discriminator and 2 updates the generator.

Part 1 - Train the Discriminator

  • input이 real 인지 fake인지 분류하는 확률을 높이는 것이 목적이다.

  • ascending its stochastic gradient로 discriminator를 update를 하길 원한다.

  • Max log(D(x))+log(1−D(G(z)))

  • 먼저, 실제 샘플을 훈련세트에서 만들고 D를 통해 forward를 한 후, loss를 계산한다. 그리고 backward pass의 gradient를 계산한다.

  • 그리고 나서, fake samples을 생성하고 D를 통해 forward를 한 후, loss를 계산한다. 그 후, gradient를 accumulate한다.

  • 마지막으로 acculmulated된 gradients를 가지고 D의 optimizer의 step를 실시한다.

Part 2 - Train the Generator

  • log(1−D(G(z))) 가 초기 단계에 gradient가 잘 안나옴.
  • 그래서 maximize log(D(G(z)))를 하려고 함.
  • How?
    • G의 output을 Part 1의 D를 통해서 classify하고, real labels을 GT로 해서 Generator's loss를 계산하고, backward pass의 gradients를 계산하고, optimizer step으로 업데이트한다.
# Training Loop

# Lists to keep track of progress
img_list = []
G_losses = []
D_losses = []
iters = 0

print("Starting Training Loop...")
# For each epoch
for epoch in range(num_epochs):
    # For each batch in the dataloader
    for i, data in enumerate(dataloader, 0):

        ############################
        # (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))
        ###########################
        ## Train with all-real batch
        netD.zero_grad()
        # Format batch
        real_cpu = data[0].to(device)
        b_size = real_cpu.size(0)
        label = torch.full((b_size,), real_label, device=device)
        # Forward pass real batch through D
        output = netD(real_cpu).view(-1)
        # Calculate loss on all-real batch
        errD_real = criterion(output, label)
        # Calculate gradients for D in backward pass
        errD_real.backward()
        D_x = output.mean().item()

        ## Train with all-fake batch
        # Generate batch of latent vectors
        noise = torch.randn(b_size, nz, 1, 1, device=device)
        # Generate fake image batch with G
        fake = netG(noise)
        label.fill_(fake_label)
        # Classify all fake batch with D
        output = netD(fake.detach()).view(-1)
        # Calculate D's loss on the all-fake batch
        errD_fake = criterion(output, label)
        # Calculate the gradients for this batch
        errD_fake.backward()
        D_G_z1 = output.mean().item()
        # Add the gradients from the all-real and all-fake batches
        errD = errD_real + errD_fake
        # Update D
        optimizerD.step()

        ############################
        # (2) Update G network: maximize log(D(G(z)))
        ###########################
        netG.zero_grad()
        label.fill_(real_label)  # fake labels are real for generator cost
        # Since we just updated D, perform another forward pass of all-fake batch through D
        output = netD(fake).view(-1)
        # Calculate G's loss based on this output
        errG = criterion(output, label)
        # Calculate gradients for G
        errG.backward()
        D_G_z2 = output.mean().item()
        # Update G
        optimizerG.step()

        # Output training stats
        if i % 50 == 0:
            print('[%d/%d][%d/%d]\tLoss_D: %.4f\tLoss_G: %.4f\tD(x): %.4f\tD(G(z)): %.4f / %.4f'
                  % (epoch, num_epochs, i, len(dataloader),
                     errD.item(), errG.item(), D_x, D_G_z1, D_G_z2))

        # Save Losses for plotting later
        G_losses.append(errG.item())
        D_losses.append(errD.item())

        if i % 100 == 0:
            vutils.save_image(real_cpu,
                    '%s/real_samples.png' % outf,
                    normalize=True)
            fake = netG(fixed_noise)
            vutils.save_image(fake.detach(),
                    '%s/fake_samples_epoch_%03d.png' % (outf, epoch),
                    normalize=True)

        # Check how the generator is doing by saving G's output on fixed_noise
        if (iters % 500 == 0) or ((epoch == num_epochs-1) and (i == len(dataloader)-1)):
            with torch.no_grad():
                fake = netG(fixed_noise).detach().cpu()
            img_list.append(vutils.make_grid(fake, padding=2, normalize=True))

        iters += 1

    # do checkpointing
    torch.save(netG.state_dict(), '%s/netG_epoch_%d.pth' % (outf, epoch))
    torch.save(netD.state_dict(), '%s/netD_epoch_%d.pth' % (outf, epoch))
[0/20][0/1583] Loss_D: 1.7410 Loss_G: 4.7765 D(x): 0.5343 D(G(z)): 0.5771 / 0.0136
...
[19/20][1550/1583] Loss_D: 0.0771 Loss_G: 4.4849 D(x): 0.9462 D(G(z)): 0.0188 / 0.0183
Starting Training Loop...
[0/20][0/1583] Loss_D: 1.7410 Loss_G: 4.7765 D(x): 0.5343 D(G(z)): 0.5771 / 0.0136
[0/20][50/1583] Loss_D: 0.0449 Loss_G: 19.0586 D(x): 0.9719 D(G(z)): 0.0000 / 0.0000
[0/20][100/1583] Loss_D: 0.0672 Loss_G: 8.9777 D(x): 0.9594 D(G(z)): 0.0147 / 0.0004
[0/20][150/1583] Loss_D: 0.5382 Loss_G: 4.3727 D(x): 0.7767 D(G(z)): 0.1481 / 0.0210
[0/20][200/1583] Loss_D: 0.3375 Loss_G: 3.3296 D(x): 0.8734 D(G(z)): 0.1447 / 0.0512
[0/20][250/1583] Loss_D: 0.4448 Loss_G: 4.5643 D(x): 0.8256 D(G(z)): 0.1510 / 0.0214
[0/20][300/1583] Loss_D: 0.4503 Loss_G: 4.5324 D(x): 0.8277 D(G(z)): 0.1886 / 0.0190
[0/20][350/1583] Loss_D: 0.8060 Loss_G: 6.4533 D(x): 0.8264 D(G(z)): 0.3890 / 0.0032
[0/20][400/1583] Loss_D: 0.3180 Loss_G: 6.1294 D(x): 0.8139 D(G(z)): 0.0258 / 0.0059
[0/20][450/1583] Loss_D: 0.3987 Loss_G: 4.7085 D(x): 0.9028 D(G(z)): 0.2162 / 0.0174
[0/20][500/1583] Loss_D: 1.7161 Loss_G: 3.4000 D(x): 0.3963 D(G(z)): 0.0449 / 0.0692
[0/20][550/1583] Loss_D: 0.4825 Loss_G: 3.6144 D(x): 0.8382 D(G(z)): 0.2082 / 0.0410
[0/20][600/1583] Loss_D: 0.2545 Loss_G: 5.3959 D(x): 0.8620 D(G(z)): 0.0592 / 0.0087
[0/20][650/1583] Loss_D: 0.3354 Loss_G: 4.8481 D(x): 0.9222 D(G(z)): 0.1839 / 0.0137
[0/20][700/1583] Loss_D: 0.5186 Loss_G: 4.2392 D(x): 0.8203 D(G(z)): 0.2167 / 0.0238
[0/20][750/1583] Loss_D: 0.5599 Loss_G: 6.3192 D(x): 0.8593 D(G(z)): 0.2695 / 0.0034
[0/20][800/1583] Loss_D: 0.4188 Loss_G: 4.9431 D(x): 0.8882 D(G(z)): 0.2113 / 0.0144
[0/20][850/1583] Loss_D: 0.6197 Loss_G: 4.4217 D(x): 0.8696 D(G(z)): 0.3028 / 0.0209
[0/20][900/1583] Loss_D: 0.4814 Loss_G: 5.1496 D(x): 0.9114 D(G(z)): 0.2720 / 0.0113
[0/20][950/1583] Loss_D: 0.6159 Loss_G: 5.2550 D(x): 0.8648 D(G(z)): 0.2966 / 0.0095
[0/20][1000/1583] Loss_D: 2.1783 Loss_G: 4.7717 D(x): 0.2019 D(G(z)): 0.0007 / 0.0210
[0/20][1050/1583] Loss_D: 0.7515 Loss_G: 5.6444 D(x): 0.8597 D(G(z)): 0.3802 / 0.0078
[0/20][1100/1583] Loss_D: 1.5281 Loss_G: 9.7716 D(x): 0.9456 D(G(z)): 0.6747 / 0.0003
[0/20][1150/1583] Loss_D: 0.8123 Loss_G: 7.3049 D(x): 0.8826 D(G(z)): 0.4314 / 0.0018
[0/20][1200/1583] Loss_D: 0.8014 Loss_G: 1.9396 D(x): 0.5848 D(G(z)): 0.0779 / 0.1988
[0/20][1250/1583] Loss_D: 0.8756 Loss_G: 5.9078 D(x): 0.9042 D(G(z)): 0.4615 / 0.0059
[0/20][1300/1583] Loss_D: 0.5593 Loss_G: 2.9927 D(x): 0.6873 D(G(z)): 0.0803 / 0.0852
[0/20][1350/1583] Loss_D: 0.3988 Loss_G: 3.8558 D(x): 0.7763 D(G(z)): 0.0747 / 0.0365
[0/20][1400/1583] Loss_D: 0.3396 Loss_G: 4.0001 D(x): 0.8580 D(G(z)): 0.1349 / 0.0295
[0/20][1450/1583] Loss_D: 2.3297 Loss_G: 5.9231 D(x): 0.9679 D(G(z)): 0.7938 / 0.0094
[0/20][1500/1583] Loss_D: 0.2046 Loss_G: 4.1294 D(x): 0.8830 D(G(z)): 0.0545 / 0.0258
[0/20][1550/1583] Loss_D: 0.3662 Loss_G: 5.0539 D(x): 0.9373 D(G(z)): 0.2283 / 0.0126
[1/20][0/1583] Loss_D: 0.3495 Loss_G: 3.0891 D(x): 0.8007 D(G(z)): 0.0768 / 0.0711
[1/20][50/1583] Loss_D: 0.4281 Loss_G: 4.7255 D(x): 0.9287 D(G(z)): 0.2718 / 0.0132
[1/20][100/1583] Loss_D: 0.6284 Loss_G: 1.7680 D(x): 0.6295 D(G(z)): 0.0378 / 0.2508
[1/20][150/1583] Loss_D: 0.4197 Loss_G: 3.1495 D(x): 0.7910 D(G(z)): 0.1052 / 0.0629
[1/20][200/1583] Loss_D: 0.7653 Loss_G: 4.9047 D(x): 0.8667 D(G(z)): 0.3928 / 0.0137
[1/20][250/1583] Loss_D: 0.9562 Loss_G: 2.5036 D(x): 0.5037 D(G(z)): 0.0166 / 0.1667
[1/20][300/1583] Loss_D: 0.4809 Loss_G: 3.3706 D(x): 0.8228 D(G(z)): 0.1935 / 0.0586
[1/20][350/1583] Loss_D: 0.6087 Loss_G: 1.8009 D(x): 0.6726 D(G(z)): 0.1011 / 0.2359
[1/20][400/1583] Loss_D: 0.4401 Loss_G: 3.6131 D(x): 0.8195 D(G(z)): 0.1787 / 0.0393
[1/20][450/1583] Loss_D: 0.5013 Loss_G: 2.5153 D(x): 0.7831 D(G(z)): 0.1805 / 0.1068
[1/20][500/1583] Loss_D: 0.8559 Loss_G: 2.9979 D(x): 0.6304 D(G(z)): 0.1721 / 0.0934
[1/20][550/1583] Loss_D: 0.4231 Loss_G: 3.3755 D(x): 0.8317 D(G(z)): 0.1810 / 0.0516
[1/20][600/1583] Loss_D: 0.3380 Loss_G: 3.9220 D(x): 0.8639 D(G(z)): 0.1467 / 0.0317
[1/20][650/1583] Loss_D: 0.5970 Loss_G: 2.8443 D(x): 0.6796 D(G(z)): 0.1008 / 0.0839
[1/20][700/1583] Loss_D: 0.5490 Loss_G: 4.4962 D(x): 0.9450 D(G(z)): 0.3408 / 0.0224
[1/20][750/1583] Loss_D: 0.7478 Loss_G: 4.6962 D(x): 0.9188 D(G(z)): 0.4250 / 0.0150
[1/20][800/1583] Loss_D: 0.9041 Loss_G: 4.8681 D(x): 0.9307 D(G(z)): 0.4542 / 0.0205
[1/20][850/1583] Loss_D: 0.5160 Loss_G: 2.6778 D(x): 0.7839 D(G(z)): 0.1914 / 0.0959
[1/20][900/1583] Loss_D: 0.4130 Loss_G: 2.6591 D(x): 0.7604 D(G(z)): 0.0793 / 0.1079
[1/20][950/1583] Loss_D: 0.7728 Loss_G: 4.5145 D(x): 0.8560 D(G(z)): 0.3996 / 0.0224
[1/20][1000/1583] Loss_D: 0.6709 Loss_G: 2.4271 D(x): 0.6159 D(G(z)): 0.0616 / 0.1313
[1/20][1050/1583] Loss_D: 0.6210 Loss_G: 1.9101 D(x): 0.6557 D(G(z)): 0.1032 / 0.1963
[1/20][1100/1583] Loss_D: 0.6920 Loss_G: 4.6043 D(x): 0.8611 D(G(z)): 0.3601 / 0.0176
[1/20][1150/1583] Loss_D: 0.6916 Loss_G: 1.8056 D(x): 0.6420 D(G(z)): 0.1577 / 0.2154
[1/20][1200/1583] Loss_D: 0.5857 Loss_G: 3.0779 D(x): 0.9126 D(G(z)): 0.3339 / 0.0785
[1/20][1250/1583] Loss_D: 0.8223 Loss_G: 1.8331 D(x): 0.5239 D(G(z)): 0.0438 / 0.2263
[1/20][1300/1583] Loss_D: 2.0921 Loss_G: 7.0854 D(x): 0.9861 D(G(z)): 0.8167 / 0.0015
[1/20][1350/1583] Loss_D: 0.5447 Loss_G: 2.3173 D(x): 0.7137 D(G(z)): 0.1482 / 0.1267
[1/20][1400/1583] Loss_D: 1.5583 Loss_G: 7.0886 D(x): 0.9757 D(G(z)): 0.7073 / 0.0018
[1/20][1450/1583] Loss_D: 0.5013 Loss_G: 3.0252 D(x): 0.8100 D(G(z)): 0.2229 / 0.0701
[1/20][1500/1583] Loss_D: 1.2264 Loss_G: 3.5587 D(x): 0.8921 D(G(z)): 0.6020 / 0.0627
[1/20][1550/1583] Loss_D: 0.5227 Loss_G: 2.5838 D(x): 0.7990 D(G(z)): 0.2174 / 0.0988
[2/20][0/1583] Loss_D: 0.4165 Loss_G: 2.4796 D(x): 0.7733 D(G(z)): 0.1202 / 0.1085
[2/20][50/1583] Loss_D: 0.8466 Loss_G: 1.9751 D(x): 0.5038 D(G(z)): 0.0321 / 0.1874
[2/20][100/1583] Loss_D: 0.4960 Loss_G: 2.0882 D(x): 0.6730 D(G(z)): 0.0633 / 0.1603
[2/20][150/1583] Loss_D: 0.7157 Loss_G: 1.6555 D(x): 0.7571 D(G(z)): 0.2876 / 0.2416
[2/20][200/1583] Loss_D: 0.5810 Loss_G: 1.8568 D(x): 0.6774 D(G(z)): 0.1262 / 0.1943
[2/20][250/1583] Loss_D: 0.8586 Loss_G: 1.9327 D(x): 0.5397 D(G(z)): 0.1100 / 0.2021
[2/20][300/1583] Loss_D: 1.1256 Loss_G: 4.0812 D(x): 0.9422 D(G(z)): 0.5831 / 0.0394
[2/20][350/1583] Loss_D: 1.1133 Loss_G: 0.6896 D(x): 0.4193 D(G(z)): 0.0297 / 0.5650
[2/20][400/1583] Loss_D: 0.7344 Loss_G: 2.5044 D(x): 0.5987 D(G(z)): 0.0796 / 0.1437
[2/20][450/1583] Loss_D: 0.8426 Loss_G: 3.7049 D(x): 0.9143 D(G(z)): 0.4675 / 0.0368
[2/20][500/1583] Loss_D: 0.8461 Loss_G: 4.4628 D(x): 0.9014 D(G(z)): 0.4809 / 0.0165
[2/20][550/1583] Loss_D: 0.7044 Loss_G: 0.9616 D(x): 0.5811 D(G(z)): 0.0680 / 0.4335
[2/20][600/1583] Loss_D: 0.4981 Loss_G: 2.5887 D(x): 0.7610 D(G(z)): 0.1710 / 0.0997
[2/20][650/1583] Loss_D: 0.5940 Loss_G: 2.0112 D(x): 0.6949 D(G(z)): 0.1572 / 0.1693
[2/20][700/1583] Loss_D: 1.0516 Loss_G: 0.9519 D(x): 0.4300 D(G(z)): 0.0271 / 0.4620
[2/20][750/1583] Loss_D: 0.6836 Loss_G: 3.1258 D(x): 0.8643 D(G(z)): 0.3775 / 0.0577
[2/20][800/1583] Loss_D: 0.7599 Loss_G: 3.7076 D(x): 0.9092 D(G(z)): 0.4526 / 0.0341
[2/20][850/1583] Loss_D: 0.5043 Loss_G: 3.1779 D(x): 0.8870 D(G(z)): 0.2876 / 0.0588
[2/20][900/1583] Loss_D: 0.7738 Loss_G: 3.0952 D(x): 0.8357 D(G(z)): 0.4029 / 0.0594
[2/20][950/1583] Loss_D: 0.4681 Loss_G: 2.7019 D(x): 0.7963 D(G(z)): 0.1882 / 0.0807
[2/20][1000/1583] Loss_D: 0.6668 Loss_G: 2.4374 D(x): 0.7022 D(G(z)): 0.2223 / 0.1057
[2/20][1050/1583] Loss_D: 0.5939 Loss_G: 2.1903 D(x): 0.7709 D(G(z)): 0.2344 / 0.1404
[2/20][1100/1583] Loss_D: 0.4734 Loss_G: 3.1788 D(x): 0.8460 D(G(z)): 0.2437 / 0.0518
[2/20][1150/1583] Loss_D: 0.6478 Loss_G: 3.2170 D(x): 0.8803 D(G(z)): 0.3519 / 0.0618
[2/20][1200/1583] Loss_D: 0.7725 Loss_G: 1.6971 D(x): 0.5410 D(G(z)): 0.0644 / 0.2365
[2/20][1250/1583] Loss_D: 0.8769 Loss_G: 3.8022 D(x): 0.9590 D(G(z)): 0.5094 / 0.0359
[2/20][1300/1583] Loss_D: 0.4804 Loss_G: 2.4144 D(x): 0.7237 D(G(z)): 0.1067 / 0.1161
[2/20][1350/1583] Loss_D: 0.7312 Loss_G: 3.4400 D(x): 0.8520 D(G(z)): 0.3902 / 0.0454
[2/20][1400/1583] Loss_D: 1.3515 Loss_G: 4.9463 D(x): 0.9638 D(G(z)): 0.6742 / 0.0119
[2/20][1450/1583] Loss_D: 0.8293 Loss_G: 1.2908 D(x): 0.5222 D(G(z)): 0.0623 / 0.3216
[2/20][1500/1583] Loss_D: 0.6314 Loss_G: 2.3523 D(x): 0.7369 D(G(z)): 0.2355 / 0.1251
[2/20][1550/1583] Loss_D: 1.2016 Loss_G: 4.7539 D(x): 0.9094 D(G(z)): 0.6105 / 0.0145
[3/20][0/1583] Loss_D: 0.7443 Loss_G: 4.0368 D(x): 0.8949 D(G(z)): 0.4305 / 0.0251
[3/20][50/1583] Loss_D: 0.5733 Loss_G: 2.9640 D(x): 0.8347 D(G(z)): 0.2895 / 0.0732
[3/20][100/1583] Loss_D: 0.6174 Loss_G: 2.7364 D(x): 0.7529 D(G(z)): 0.2465 / 0.0810
[3/20][150/1583] Loss_D: 1.1024 Loss_G: 4.4745 D(x): 0.8992 D(G(z)): 0.5760 / 0.0167
[3/20][200/1583] Loss_D: 0.4080 Loss_G: 2.4299 D(x): 0.7861 D(G(z)): 0.1361 / 0.1078
[3/20][250/1583] Loss_D: 0.7814 Loss_G: 1.3794 D(x): 0.5675 D(G(z)): 0.1168 / 0.3091
[3/20][300/1583] Loss_D: 1.5277 Loss_G: 1.0074 D(x): 0.2852 D(G(z)): 0.0417 / 0.4186
[3/20][350/1583] Loss_D: 0.5269 Loss_G: 2.1274 D(x): 0.7573 D(G(z)): 0.1816 / 0.1459
[3/20][400/1583] Loss_D: 0.5345 Loss_G: 2.4768 D(x): 0.7594 D(G(z)): 0.1945 / 0.1082
[3/20][450/1583] Loss_D: 0.6973 Loss_G: 2.7010 D(x): 0.7838 D(G(z)): 0.3241 / 0.0932
[3/20][500/1583] Loss_D: 0.9344 Loss_G: 3.8631 D(x): 0.9555 D(G(z)): 0.5286 / 0.0309
[3/20][550/1583] Loss_D: 0.7371 Loss_G: 3.9111 D(x): 0.9194 D(G(z)): 0.4313 / 0.0284
[3/20][600/1583] Loss_D: 0.7451 Loss_G: 3.1238 D(x): 0.8919 D(G(z)): 0.4267 / 0.0583
[3/20][650/1583] Loss_D: 0.7557 Loss_G: 3.9020 D(x): 0.8880 D(G(z)): 0.4247 / 0.0348
[3/20][700/1583] Loss_D: 0.5578 Loss_G: 2.9881 D(x): 0.8846 D(G(z)): 0.3224 / 0.0665
[3/20][750/1583] Loss_D: 0.6100 Loss_G: 3.1242 D(x): 0.8720 D(G(z)): 0.3423 / 0.0601
[3/20][800/1583] Loss_D: 0.5605 Loss_G: 2.7928 D(x): 0.7818 D(G(z)): 0.2393 / 0.0797
[3/20][850/1583] Loss_D: 0.6751 Loss_G: 1.9463 D(x): 0.6537 D(G(z)): 0.1695 / 0.1814
[3/20][900/1583] Loss_D: 1.0268 Loss_G: 0.7435 D(x): 0.4494 D(G(z)): 0.0827 / 0.5238
[3/20][950/1583] Loss_D: 0.9942 Loss_G: 3.3982 D(x): 0.9163 D(G(z)): 0.5413 / 0.0463
[3/20][1000/1583] Loss_D: 0.6281 Loss_G: 1.4484 D(x): 0.6802 D(G(z)): 0.1766 / 0.2706
[3/20][1050/1583] Loss_D: 1.5523 Loss_G: 4.4541 D(x): 0.9091 D(G(z)): 0.6790 / 0.0222
[3/20][1100/1583] Loss_D: 0.5266 Loss_G: 1.7666 D(x): 0.7645 D(G(z)): 0.1886 / 0.2058
[3/20][1150/1583] Loss_D: 0.6640 Loss_G: 3.9673 D(x): 0.8396 D(G(z)): 0.3543 / 0.0261
[3/20][1200/1583] Loss_D: 0.5495 Loss_G: 2.7730 D(x): 0.6856 D(G(z)): 0.1013 / 0.0908
[3/20][1250/1583] Loss_D: 0.7748 Loss_G: 3.4206 D(x): 0.8361 D(G(z)): 0.4071 / 0.0443
[3/20][1300/1583] Loss_D: 1.0531 Loss_G: 0.7425 D(x): 0.4492 D(G(z)): 0.0930 / 0.5158
[3/20][1350/1583] Loss_D: 1.2218 Loss_G: 3.6362 D(x): 0.8856 D(G(z)): 0.5945 / 0.0399
[3/20][1400/1583] Loss_D: 0.5066 Loss_G: 2.4130 D(x): 0.7957 D(G(z)): 0.2118 / 0.1101
[3/20][1450/1583] Loss_D: 1.0593 Loss_G: 1.1941 D(x): 0.4555 D(G(z)): 0.1129 / 0.3577
[3/20][1500/1583] Loss_D: 0.8114 Loss_G: 3.4493 D(x): 0.8202 D(G(z)): 0.4114 / 0.0421
[3/20][1550/1583] Loss_D: 0.6355 Loss_G: 2.7985 D(x): 0.7973 D(G(z)): 0.3010 / 0.0773
[4/20][0/1583] Loss_D: 0.6190 Loss_G: 1.4828 D(x): 0.6865 D(G(z)): 0.1701 / 0.2686
[4/20][50/1583] Loss_D: 0.7670 Loss_G: 1.5755 D(x): 0.5785 D(G(z)): 0.1064 / 0.2610
[4/20][100/1583] Loss_D: 0.9794 Loss_G: 0.8689 D(x): 0.4682 D(G(z)): 0.0713 / 0.4803
[4/20][150/1583] Loss_D: 0.4796 Loss_G: 2.5772 D(x): 0.7851 D(G(z)): 0.1844 / 0.0981
[4/20][200/1583] Loss_D: 1.0336 Loss_G: 4.0929 D(x): 0.9576 D(G(z)): 0.5638 / 0.0253
[4/20][250/1583] Loss_D: 0.7909 Loss_G: 4.3225 D(x): 0.9198 D(G(z)): 0.4664 / 0.0217
[4/20][300/1583] Loss_D: 2.3641 Loss_G: 5.2525 D(x): 0.9666 D(G(z)): 0.8407 / 0.0107
[4/20][350/1583] Loss_D: 0.3944 Loss_G: 2.9269 D(x): 0.8325 D(G(z)): 0.1671 / 0.0670
[4/20][400/1583] Loss_D: 0.6390 Loss_G: 2.0675 D(x): 0.6757 D(G(z)): 0.1704 / 0.1584
[4/20][450/1583] Loss_D: 0.7568 Loss_G: 3.3020 D(x): 0.8837 D(G(z)): 0.4247 / 0.0513
[4/20][500/1583] Loss_D: 0.5173 Loss_G: 4.1190 D(x): 0.9339 D(G(z)): 0.3338 / 0.0214
[4/20][550/1583] Loss_D: 0.7709 Loss_G: 2.0782 D(x): 0.8477 D(G(z)): 0.3988 / 0.1619
[4/20][600/1583] Loss_D: 0.6134 Loss_G: 2.2465 D(x): 0.6198 D(G(z)): 0.0796 / 0.1321
[4/20][650/1583] Loss_D: 1.1340 Loss_G: 4.1819 D(x): 0.9502 D(G(z)): 0.6112 / 0.0210
[4/20][700/1583] Loss_D: 0.3680 Loss_G: 2.5364 D(x): 0.8330 D(G(z)): 0.1506 / 0.1024
[4/20][750/1583] Loss_D: 0.7582 Loss_G: 3.5181 D(x): 0.8960 D(G(z)): 0.4361 / 0.0377
[4/20][800/1583] Loss_D: 0.5314 Loss_G: 2.8887 D(x): 0.8143 D(G(z)): 0.2457 / 0.0748
[4/20][850/1583] Loss_D: 0.7218 Loss_G: 3.3921 D(x): 0.8423 D(G(z)): 0.3714 / 0.0483
[4/20][900/1583] Loss_D: 0.7013 Loss_G: 2.3325 D(x): 0.6629 D(G(z)): 0.1797 / 0.1271
[4/20][950/1583] Loss_D: 0.4679 Loss_G: 2.7841 D(x): 0.8424 D(G(z)): 0.2300 / 0.0829
[4/20][1000/1583] Loss_D: 0.4535 Loss_G: 2.3732 D(x): 0.8321 D(G(z)): 0.2089 / 0.1128
[4/20][1050/1583] Loss_D: 0.6944 Loss_G: 3.3689 D(x): 0.8595 D(G(z)): 0.3831 / 0.0454
[4/20][1100/1583] Loss_D: 0.9457 Loss_G: 0.9144 D(x): 0.4850 D(G(z)): 0.0754 / 0.4504
[4/20][1150/1583] Loss_D: 0.6403 Loss_G: 3.7571 D(x): 0.8796 D(G(z)): 0.3643 / 0.0326
[4/20][1200/1583] Loss_D: 1.6224 Loss_G: 0.5884 D(x): 0.2613 D(G(z)): 0.0330 / 0.6027
[4/20][1250/1583] Loss_D: 0.8926 Loss_G: 2.1653 D(x): 0.6066 D(G(z)): 0.2507 / 0.1563
[4/20][1300/1583] Loss_D: 0.5672 Loss_G: 1.8014 D(x): 0.6662 D(G(z)): 0.1085 / 0.2015
[4/20][1350/1583] Loss_D: 0.6621 Loss_G: 4.0858 D(x): 0.9298 D(G(z)): 0.4096 / 0.0223
[4/20][1400/1583] Loss_D: 0.6584 Loss_G: 3.2440 D(x): 0.8878 D(G(z)): 0.3851 / 0.0501
[4/20][1450/1583] Loss_D: 0.6099 Loss_G: 3.2473 D(x): 0.9127 D(G(z)): 0.3747 / 0.0492
[4/20][1500/1583] Loss_D: 1.6107 Loss_G: 0.2481 D(x): 0.2599 D(G(z)): 0.0178 / 0.8148
[4/20][1550/1583] Loss_D: 0.4393 Loss_G: 2.5206 D(x): 0.7341 D(G(z)): 0.0924 / 0.1040
[5/20][0/1583] Loss_D: 0.7323 Loss_G: 3.4426 D(x): 0.8958 D(G(z)): 0.4190 / 0.0455
[5/20][50/1583] Loss_D: 0.4701 Loss_G: 2.9693 D(x): 0.8395 D(G(z)): 0.2326 / 0.0673
[5/20][100/1583] Loss_D: 0.6914 Loss_G: 3.9368 D(x): 0.9114 D(G(z)): 0.4031 / 0.0270
[5/20][150/1583] Loss_D: 0.5457 Loss_G: 2.3770 D(x): 0.7511 D(G(z)): 0.1965 / 0.1219
[5/20][200/1583] Loss_D: 0.5658 Loss_G: 1.5108 D(x): 0.6612 D(G(z)): 0.0848 / 0.2547
[5/20][250/1583] Loss_D: 0.6146 Loss_G: 3.9287 D(x): 0.9534 D(G(z)): 0.4005 / 0.0259
[5/20][300/1583] Loss_D: 0.9300 Loss_G: 3.3197 D(x): 0.8995 D(G(z)): 0.5042 / 0.0502
[5/20][350/1583] Loss_D: 0.5325 Loss_G: 1.6093 D(x): 0.7229 D(G(z)): 0.1440 / 0.2417
[5/20][400/1583] Loss_D: 0.9521 Loss_G: 0.4797 D(x): 0.4600 D(G(z)): 0.0518 / 0.6548
[5/20][450/1583] Loss_D: 0.7482 Loss_G: 3.2963 D(x): 0.9374 D(G(z)): 0.4515 / 0.0494
[5/20][500/1583] Loss_D: 1.2473 Loss_G: 4.8589 D(x): 0.9432 D(G(z)): 0.6380 / 0.0113
[5/20][550/1583] Loss_D: 0.5828 Loss_G: 1.3968 D(x): 0.6884 D(G(z)): 0.1459 / 0.2952
[5/20][600/1583] Loss_D: 0.4951 Loss_G: 3.5542 D(x): 0.9001 D(G(z)): 0.2957 / 0.0384
[5/20][650/1583] Loss_D: 0.4813 Loss_G: 3.2853 D(x): 0.8736 D(G(z)): 0.2686 / 0.0486
[5/20][700/1583] Loss_D: 0.5033 Loss_G: 2.6258 D(x): 0.8170 D(G(z)): 0.2208 / 0.0961
[5/20][750/1583] Loss_D: 0.6739 Loss_G: 1.9801 D(x): 0.6349 D(G(z)): 0.1067 / 0.1897
[5/20][800/1583] Loss_D: 0.4293 Loss_G: 2.6046 D(x): 0.8100 D(G(z)): 0.1678 / 0.0933
[5/20][850/1583] Loss_D: 0.5649 Loss_G: 2.2224 D(x): 0.6974 D(G(z)): 0.1357 / 0.1301
[5/20][900/1583] Loss_D: 1.1035 Loss_G: 0.8224 D(x): 0.4289 D(G(z)): 0.0525 / 0.4958
[5/20][950/1583] Loss_D: 3.2330 Loss_G: 0.0733 D(x): 0.0794 D(G(z)): 0.0310 / 0.9343
[5/20][1000/1583] Loss_D: 0.6679 Loss_G: 1.5752 D(x): 0.6013 D(G(z)): 0.0840 / 0.2559
[5/20][1050/1583] Loss_D: 1.1246 Loss_G: 4.7649 D(x): 0.9467 D(G(z)): 0.6041 / 0.0127
[5/20][1100/1583] Loss_D: 0.8038 Loss_G: 1.6691 D(x): 0.6885 D(G(z)): 0.2793 / 0.2359
[5/20][1150/1583] Loss_D: 0.6897 Loss_G: 4.0272 D(x): 0.8872 D(G(z)): 0.3930 / 0.0270
[5/20][1200/1583] Loss_D: 1.8038 Loss_G: 6.2461 D(x): 0.9885 D(G(z)): 0.7836 / 0.0040
[5/20][1250/1583] Loss_D: 0.9141 Loss_G: 1.4521 D(x): 0.4791 D(G(z)): 0.0559 / 0.2952
[5/20][1300/1583] Loss_D: 0.5217 Loss_G: 2.2163 D(x): 0.7537 D(G(z)): 0.1791 / 0.1389
[5/20][1350/1583] Loss_D: 0.4390 Loss_G: 2.2286 D(x): 0.8911 D(G(z)): 0.2451 / 0.1387
[5/20][1400/1583] Loss_D: 0.6742 Loss_G: 1.4092 D(x): 0.6256 D(G(z)): 0.1300 / 0.2963
[5/20][1450/1583] Loss_D: 0.3419 Loss_G: 2.7905 D(x): 0.8096 D(G(z)): 0.0997 / 0.0881
[5/20][1500/1583] Loss_D: 0.4997 Loss_G: 2.9426 D(x): 0.8166 D(G(z)): 0.2252 / 0.0671
[5/20][1550/1583] Loss_D: 0.3468 Loss_G: 2.8488 D(x): 0.9008 D(G(z)): 0.1978 / 0.0749
[6/20][0/1583] Loss_D: 4.3955 Loss_G: 5.5303 D(x): 0.9860 D(G(z)): 0.9622 / 0.0088
[6/20][50/1583] Loss_D: 0.7677 Loss_G: 1.8061 D(x): 0.5506 D(G(z)): 0.0690 / 0.2167
[6/20][100/1583] Loss_D: 0.7837 Loss_G: 3.6642 D(x): 0.8930 D(G(z)): 0.4401 / 0.0357
[6/20][150/1583] Loss_D: 0.3844 Loss_G: 3.2250 D(x): 0.8740 D(G(z)): 0.2014 / 0.0530
[6/20][200/1583] Loss_D: 0.3511 Loss_G: 3.5891 D(x): 0.8962 D(G(z)): 0.1963 / 0.0378
[6/20][250/1583] Loss_D: 0.5464 Loss_G: 1.9196 D(x): 0.6527 D(G(z)): 0.0583 / 0.1851
[6/20][300/1583] Loss_D: 0.6648 Loss_G: 2.0872 D(x): 0.6812 D(G(z)): 0.1896 / 0.1551
[6/20][350/1583] Loss_D: 0.5900 Loss_G: 4.0803 D(x): 0.9030 D(G(z)): 0.3360 / 0.0253
[6/20][400/1583] Loss_D: 0.4268 Loss_G: 2.8039 D(x): 0.8247 D(G(z)): 0.1793 / 0.0828
[6/20][450/1583] Loss_D: 0.4140 Loss_G: 2.9813 D(x): 0.9032 D(G(z)): 0.2454 / 0.0668
[6/20][500/1583] Loss_D: 0.3369 Loss_G: 2.3742 D(x): 0.8204 D(G(z)): 0.1106 / 0.1231
[6/20][550/1583] Loss_D: 1.0842 Loss_G: 0.3029 D(x): 0.4080 D(G(z)): 0.0300 / 0.7758
[6/20][600/1583] Loss_D: 0.6743 Loss_G: 2.1038 D(x): 0.5983 D(G(z)): 0.0673 / 0.1638
[6/20][650/1583] Loss_D: 0.5764 Loss_G: 3.9941 D(x): 0.9302 D(G(z)): 0.3584 / 0.0268
[6/20][700/1583] Loss_D: 0.3742 Loss_G: 3.2708 D(x): 0.9046 D(G(z)): 0.2213 / 0.0492
[6/20][750/1583] Loss_D: 1.1182 Loss_G: 1.0595 D(x): 0.4141 D(G(z)): 0.0312 / 0.4134
[6/20][800/1583] Loss_D: 0.5230 Loss_G: 2.1190 D(x): 0.7189 D(G(z)): 0.1305 / 0.1560
[6/20][850/1583] Loss_D: 1.0702 Loss_G: 1.6753 D(x): 0.6622 D(G(z)): 0.3463 / 0.3068
[6/20][900/1583] Loss_D: 0.5835 Loss_G: 1.5507 D(x): 0.6592 D(G(z)): 0.0960 / 0.2584
[6/20][950/1583] Loss_D: 1.3041 Loss_G: 1.0619 D(x): 0.3352 D(G(z)): 0.0179 / 0.4241
[6/20][1000/1583] Loss_D: 0.7321 Loss_G: 1.1229 D(x): 0.5704 D(G(z)): 0.0844 / 0.3779
[6/20][1050/1583] Loss_D: 0.5501 Loss_G: 2.7354 D(x): 0.8075 D(G(z)): 0.2538 / 0.0825
[6/20][1100/1583] Loss_D: 0.4789 Loss_G: 2.2263 D(x): 0.7961 D(G(z)): 0.1873 / 0.1340
[6/20][1150/1583] Loss_D: 0.6680 Loss_G: 4.6801 D(x): 0.9348 D(G(z)): 0.4143 / 0.0146
[6/20][1200/1583] Loss_D: 0.4431 Loss_G: 3.3127 D(x): 0.8719 D(G(z)): 0.2343 / 0.0534
[6/20][1250/1583] Loss_D: 0.6206 Loss_G: 4.6355 D(x): 0.9396 D(G(z)): 0.3845 / 0.0136
[6/20][1300/1583] Loss_D: 0.4057 Loss_G: 3.4378 D(x): 0.9003 D(G(z)): 0.2347 / 0.0449
[6/20][1350/1583] Loss_D: 0.6685 Loss_G: 1.7244 D(x): 0.6786 D(G(z)): 0.1996 / 0.2262
[6/20][1400/1583] Loss_D: 0.5023 Loss_G: 3.1058 D(x): 0.8602 D(G(z)): 0.2637 / 0.0586
[6/20][1450/1583] Loss_D: 0.6067 Loss_G: 2.4539 D(x): 0.7353 D(G(z)): 0.2084 / 0.1132
[6/20][1500/1583] Loss_D: 0.5930 Loss_G: 1.9310 D(x): 0.7278 D(G(z)): 0.1973 / 0.1740
[6/20][1550/1583] Loss_D: 0.3919 Loss_G: 2.8650 D(x): 0.8726 D(G(z)): 0.2034 / 0.0720
[7/20][0/1583] Loss_D: 0.9937 Loss_G: 0.8772 D(x): 0.4278 D(G(z)): 0.0203 / 0.4746
[7/20][50/1583] Loss_D: 0.5583 Loss_G: 2.2141 D(x): 0.6588 D(G(z)): 0.0663 / 0.1556
[7/20][100/1583] Loss_D: 0.4775 Loss_G: 2.8164 D(x): 0.7293 D(G(z)): 0.1121 / 0.0904
[7/20][150/1583] Loss_D: 0.3136 Loss_G: 3.4618 D(x): 0.8864 D(G(z)): 0.1531 / 0.0468
[7/20][200/1583] Loss_D: 0.3653 Loss_G: 3.7723 D(x): 0.9626 D(G(z)): 0.2593 / 0.0319
[7/20][250/1583] Loss_D: 0.3479 Loss_G: 3.3227 D(x): 0.8569 D(G(z)): 0.1571 / 0.0502
[7/20][300/1583] Loss_D: 0.7672 Loss_G: 5.0311 D(x): 0.9460 D(G(z)): 0.4491 / 0.0096
[7/20][350/1583] Loss_D: 0.5022 Loss_G: 2.7576 D(x): 0.8297 D(G(z)): 0.2316 / 0.0908
[7/20][400/1583] Loss_D: 0.4674 Loss_G: 1.9166 D(x): 0.7104 D(G(z)): 0.0751 / 0.1838
[7/20][450/1583] Loss_D: 0.4497 Loss_G: 3.4482 D(x): 0.8363 D(G(z)): 0.2027 / 0.0484
[7/20][500/1583] Loss_D: 0.4457 Loss_G: 2.0742 D(x): 0.7356 D(G(z)): 0.0974 / 0.1614
[7/20][550/1583] Loss_D: 0.3718 Loss_G: 2.7557 D(x): 0.8532 D(G(z)): 0.1646 / 0.0854
[7/20][600/1583] Loss_D: 0.3986 Loss_G: 3.1520 D(x): 0.9168 D(G(z)): 0.2433 / 0.0574
[7/20][650/1583] Loss_D: 0.4891 Loss_G: 3.9681 D(x): 0.9336 D(G(z)): 0.3129 / 0.0259
[7/20][700/1583] Loss_D: 0.8796 Loss_G: 4.7294 D(x): 0.9223 D(G(z)): 0.4910 / 0.0140
[7/20][750/1583] Loss_D: 1.3281 Loss_G: 0.7828 D(x): 0.3252 D(G(z)): 0.0342 / 0.5200
[7/20][800/1583] Loss_D: 0.3356 Loss_G: 3.2801 D(x): 0.9067 D(G(z)): 0.1936 / 0.0518
[7/20][850/1583] Loss_D: 0.6749 Loss_G: 4.3375 D(x): 0.9408 D(G(z)): 0.4078 / 0.0178
[7/20][900/1583] Loss_D: 0.6806 Loss_G: 1.4028 D(x): 0.6030 D(G(z)): 0.0648 / 0.3167
[7/20][950/1583] Loss_D: 0.5828 Loss_G: 3.9035 D(x): 0.8742 D(G(z)): 0.3122 / 0.0286
[7/20][1000/1583] Loss_D: 1.5289 Loss_G: 5.3634 D(x): 0.9872 D(G(z)): 0.7196 / 0.0085
[7/20][1050/1583] Loss_D: 0.3579 Loss_G: 3.0619 D(x): 0.8812 D(G(z)): 0.1914 / 0.0616
[7/20][1100/1583] Loss_D: 0.4081 Loss_G: 3.3457 D(x): 0.8707 D(G(z)): 0.2060 / 0.0460
[7/20][1150/1583] Loss_D: 0.3850 Loss_G: 1.8594 D(x): 0.7970 D(G(z)): 0.1164 / 0.1901
[7/20][1200/1583] Loss_D: 0.4083 Loss_G: 3.4403 D(x): 0.8714 D(G(z)): 0.2111 / 0.0443
[7/20][1250/1583] Loss_D: 0.4171 Loss_G: 3.0643 D(x): 0.7392 D(G(z)): 0.0596 / 0.0699
[7/20][1300/1583] Loss_D: 0.4492 Loss_G: 3.0841 D(x): 0.8419 D(G(z)): 0.2077 / 0.0663
[7/20][1350/1583] Loss_D: 0.3917 Loss_G: 3.9511 D(x): 0.9457 D(G(z)): 0.2551 / 0.0273
[7/20][1400/1583] Loss_D: 1.1657 Loss_G: 4.6778 D(x): 0.8444 D(G(z)): 0.5540 / 0.0164
[7/20][1450/1583] Loss_D: 0.7636 Loss_G: 4.7968 D(x): 0.9504 D(G(z)): 0.4558 / 0.0123
[7/20][1500/1583] Loss_D: 0.4004 Loss_G: 3.3350 D(x): 0.9098 D(G(z)): 0.2238 / 0.0464
[7/20][1550/1583] Loss_D: 1.4643 Loss_G: 1.1699 D(x): 0.2923 D(G(z)): 0.0120 / 0.3747
[8/20][0/1583] Loss_D: 0.3794 Loss_G: 2.8846 D(x): 0.8922 D(G(z)): 0.2056 / 0.0794
[8/20][50/1583] Loss_D: 0.3324 Loss_G: 3.7705 D(x): 0.9425 D(G(z)): 0.2165 / 0.0323
[8/20][100/1583] Loss_D: 1.4623 Loss_G: 5.1932 D(x): 0.9460 D(G(z)): 0.6943 / 0.0102
[8/20][150/1583] Loss_D: 0.2513 Loss_G: 2.8608 D(x): 0.8913 D(G(z)): 0.1138 / 0.0854
[8/20][200/1583] Loss_D: 0.4089 Loss_G: 1.9675 D(x): 0.7585 D(G(z)): 0.0976 / 0.1663
[8/20][250/1583] Loss_D: 0.5234 Loss_G: 4.9561 D(x): 0.9518 D(G(z)): 0.3438 / 0.0107
[8/20][300/1583] Loss_D: 0.4831 Loss_G: 3.2577 D(x): 0.8379 D(G(z)): 0.2316 / 0.0580
[8/20][350/1583] Loss_D: 0.6230 Loss_G: 1.5389 D(x): 0.6033 D(G(z)): 0.0506 / 0.2675
[8/20][400/1583] Loss_D: 0.4021 Loss_G: 1.8503 D(x): 0.7259 D(G(z)): 0.0341 / 0.2006
[8/20][450/1583] Loss_D: 0.3415 Loss_G: 3.3156 D(x): 0.9314 D(G(z)): 0.2180 / 0.0479
[8/20][500/1583] Loss_D: 0.7203 Loss_G: 1.0026 D(x): 0.5825 D(G(z)): 0.0615 / 0.4269
[8/20][550/1583] Loss_D: 0.4582 Loss_G: 1.6969 D(x): 0.7214 D(G(z)): 0.0910 / 0.2324
[8/20][600/1583] Loss_D: 1.4420 Loss_G: 1.1809 D(x): 0.3260 D(G(z)): 0.0394 / 0.3807
[8/20][650/1583] Loss_D: 0.2668 Loss_G: 3.3727 D(x): 0.9348 D(G(z)): 0.1705 / 0.0436
[8/20][700/1583] Loss_D: 0.7360 Loss_G: 1.0637 D(x): 0.5771 D(G(z)): 0.0725 / 0.3984
[8/20][750/1583] Loss_D: 0.4526 Loss_G: 3.5518 D(x): 0.8779 D(G(z)): 0.2426 / 0.0381
[8/20][800/1583] Loss_D: 0.2334 Loss_G: 3.1728 D(x): 0.8654 D(G(z)): 0.0729 / 0.0584
[8/20][850/1583] Loss_D: 0.3885 Loss_G: 3.6571 D(x): 0.9005 D(G(z)): 0.2243 / 0.0359
[8/20][900/1583] Loss_D: 0.4124 Loss_G: 3.9195 D(x): 0.9031 D(G(z)): 0.2408 / 0.0297
[8/20][950/1583] Loss_D: 0.5008 Loss_G: 2.7880 D(x): 0.8524 D(G(z)): 0.2472 / 0.0879
[8/20][1000/1583] Loss_D: 0.4570 Loss_G: 3.8953 D(x): 0.9260 D(G(z)): 0.2862 / 0.0278
[8/20][1050/1583] Loss_D: 3.3027 Loss_G: 0.0994 D(x): 0.0634 D(G(z)): 0.0111 / 0.9114
[8/20][1100/1583] Loss_D: 0.5205 Loss_G: 1.8326 D(x): 0.7018 D(G(z)): 0.1072 / 0.1995
[8/20][1150/1583] Loss_D: 0.4758 Loss_G: 4.3610 D(x): 0.9459 D(G(z)): 0.2994 / 0.0198
[8/20][1200/1583] Loss_D: 1.1320 Loss_G: 0.4335 D(x): 0.3994 D(G(z)): 0.0359 / 0.6783
[8/20][1250/1583] Loss_D: 0.2684 Loss_G: 3.7708 D(x): 0.9262 D(G(z)): 0.1600 / 0.0331
[8/20][1300/1583] Loss_D: 0.3112 Loss_G: 2.8636 D(x): 0.8433 D(G(z)): 0.1063 / 0.0870
[8/20][1350/1583] Loss_D: 0.3549 Loss_G: 3.3965 D(x): 0.8527 D(G(z)): 0.1540 / 0.0511
[8/20][1400/1583] Loss_D: 0.3796 Loss_G: 1.9934 D(x): 0.7987 D(G(z)): 0.1194 / 0.1735
[8/20][1450/1583] Loss_D: 0.3327 Loss_G: 2.9824 D(x): 0.9003 D(G(z)): 0.1794 / 0.0725
[8/20][1500/1583] Loss_D: 0.8892 Loss_G: 0.9814 D(x): 0.4607 D(G(z)): 0.0146 / 0.4348
[8/20][1550/1583] Loss_D: 0.4178 Loss_G: 2.1137 D(x): 0.7210 D(G(z)): 0.0524 / 0.1642
[9/20][0/1583] Loss_D: 0.3355 Loss_G: 3.4654 D(x): 0.8873 D(G(z)): 0.1723 / 0.0433
[9/20][50/1583] Loss_D: 0.3833 Loss_G: 3.7069 D(x): 0.8648 D(G(z)): 0.1832 / 0.0353
[9/20][100/1583] Loss_D: 0.4653 Loss_G: 3.7419 D(x): 0.9129 D(G(z)): 0.2668 / 0.0393
[9/20][150/1583] Loss_D: 0.3401 Loss_G: 2.4734 D(x): 0.8479 D(G(z)): 0.1401 / 0.1138
[9/20][200/1583] Loss_D: 2.3238 Loss_G: 4.4457 D(x): 0.9894 D(G(z)): 0.8347 / 0.0216
[9/20][250/1583] Loss_D: 0.5899 Loss_G: 1.2450 D(x): 0.6232 D(G(z)): 0.0364 / 0.3604
[9/20][300/1583] Loss_D: 0.6175 Loss_G: 2.7579 D(x): 0.7624 D(G(z)): 0.2380 / 0.0926
[9/20][350/1583] Loss_D: 1.1463 Loss_G: 0.6558 D(x): 0.3963 D(G(z)): 0.0196 / 0.5618
[9/20][400/1583] Loss_D: 0.3826 Loss_G: 2.1367 D(x): 0.7656 D(G(z)): 0.0702 / 0.1567
[9/20][450/1583] Loss_D: 0.8569 Loss_G: 4.9679 D(x): 0.9560 D(G(z)): 0.4928 / 0.0102
[9/20][500/1583] Loss_D: 1.0243 Loss_G: 3.8232 D(x): 0.9137 D(G(z)): 0.5489 / 0.0374
[9/20][550/1583] Loss_D: 0.4581 Loss_G: 3.4023 D(x): 0.8716 D(G(z)): 0.2388 / 0.0464
[9/20][600/1583] Loss_D: 0.2323 Loss_G: 3.0196 D(x): 0.8663 D(G(z)): 0.0747 / 0.0707
[9/20][650/1583] Loss_D: 0.4908 Loss_G: 3.6812 D(x): 0.8512 D(G(z)): 0.2212 / 0.0431
[9/20][700/1583] Loss_D: 0.7221 Loss_G: 5.3467 D(x): 0.9737 D(G(z)): 0.4437 / 0.0077
[9/20][750/1583] Loss_D: 0.3680 Loss_G: 2.6479 D(x): 0.8199 D(G(z)): 0.1334 / 0.0943
[9/20][800/1583] Loss_D: 0.3675 Loss_G: 2.0510 D(x): 0.7923 D(G(z)): 0.1031 / 0.1625
[9/20][850/1583] Loss_D: 0.3144 Loss_G: 4.3019 D(x): 0.9633 D(G(z)): 0.2227 / 0.0192
[9/20][900/1583] Loss_D: 1.6036 Loss_G: 0.8945 D(x): 0.2758 D(G(z)): 0.0344 / 0.4773
[9/20][950/1583] Loss_D: 0.4085 Loss_G: 3.2953 D(x): 0.8953 D(G(z)): 0.2265 / 0.0503
[9/20][1000/1583] Loss_D: 0.3107 Loss_G: 3.3860 D(x): 0.8849 D(G(z)): 0.1489 / 0.0550
[9/20][1050/1583] Loss_D: 0.3532 Loss_G: 2.9715 D(x): 0.8766 D(G(z)): 0.1762 / 0.0710
[9/20][1100/1583] Loss_D: 0.5160 Loss_G: 3.6577 D(x): 0.8796 D(G(z)): 0.2767 / 0.0425
[9/20][1150/1583] Loss_D: 1.3764 Loss_G: 4.0215 D(x): 0.9326 D(G(z)): 0.6596 / 0.0311
[9/20][1200/1583] Loss_D: 0.3632 Loss_G: 3.3313 D(x): 0.8521 D(G(z)): 0.1523 / 0.0535
[9/20][1250/1583] Loss_D: 0.2771 Loss_G: 3.2974 D(x): 0.8931 D(G(z)): 0.1350 / 0.0510
[9/20][1300/1583] Loss_D: 1.0717 Loss_G: 0.5304 D(x): 0.4159 D(G(z)): 0.0438 / 0.6390
[9/20][1350/1583] Loss_D: 0.2916 Loss_G: 2.1300 D(x): 0.8480 D(G(z)): 0.1016 / 0.1599
[9/20][1400/1583] Loss_D: 0.4418 Loss_G: 2.5393 D(x): 0.7389 D(G(z)): 0.0926 / 0.1136
[9/20][1450/1583] Loss_D: 0.4443 Loss_G: 2.2956 D(x): 0.7454 D(G(z)): 0.0954 / 0.1465
[9/20][1500/1583] Loss_D: 0.2820 Loss_G: 3.3872 D(x): 0.8830 D(G(z)): 0.1264 / 0.0512
[9/20][1550/1583] Loss_D: 0.2550 Loss_G: 3.5616 D(x): 0.9287 D(G(z)): 0.1501 / 0.0430
[10/20][0/1583] Loss_D: 0.4452 Loss_G: 5.2619 D(x): 0.9795 D(G(z)): 0.3109 / 0.0078
[10/20][50/1583] Loss_D: 0.3154 Loss_G: 5.1106 D(x): 0.9350 D(G(z)): 0.2016 / 0.0094
[10/20][100/1583] Loss_D: 0.8687 Loss_G: 2.1417 D(x): 0.6353 D(G(z)): 0.2446 / 0.1601
[10/20][150/1583] Loss_D: 0.3782 Loss_G: 2.2331 D(x): 0.7631 D(G(z)): 0.0646 / 0.1492
[10/20][200/1583] Loss_D: 0.3533 Loss_G: 5.1649 D(x): 0.9514 D(G(z)): 0.2351 / 0.0090
[10/20][250/1583] Loss_D: 0.3042 Loss_G: 4.2050 D(x): 0.9426 D(G(z)): 0.1991 / 0.0204
[10/20][300/1583] Loss_D: 0.4181 Loss_G: 3.1368 D(x): 0.9099 D(G(z)): 0.2471 / 0.0611
[10/20][350/1583] Loss_D: 0.3003 Loss_G: 3.2192 D(x): 0.8692 D(G(z)): 0.1221 / 0.0579
[10/20][400/1583] Loss_D: 0.1929 Loss_G: 3.8826 D(x): 0.9587 D(G(z)): 0.1306 / 0.0304
[10/20][450/1583] Loss_D: 0.3430 Loss_G: 3.6889 D(x): 0.9103 D(G(z)): 0.1918 / 0.0369
[10/20][500/1583] Loss_D: 0.1794 Loss_G: 3.4852 D(x): 0.9511 D(G(z)): 0.1145 / 0.0421
[10/20][550/1583] Loss_D: 0.4598 Loss_G: 3.7130 D(x): 0.8519 D(G(z)): 0.2169 / 0.0373
[10/20][600/1583] Loss_D: 0.8685 Loss_G: 2.0182 D(x): 0.5017 D(G(z)): 0.0311 / 0.1981
[10/20][650/1583] Loss_D: 0.3008 Loss_G: 3.7633 D(x): 0.9257 D(G(z)): 0.1768 / 0.0332
[10/20][700/1583] Loss_D: 0.4212 Loss_G: 2.5866 D(x): 0.7214 D(G(z)): 0.0412 / 0.1195
[10/20][750/1583] Loss_D: 0.4810 Loss_G: 2.3168 D(x): 0.6815 D(G(z)): 0.0369 / 0.1446
[10/20][800/1583] Loss_D: 0.3565 Loss_G: 3.1685 D(x): 0.7729 D(G(z)): 0.0687 / 0.0678
[10/20][850/1583] Loss_D: 0.2442 Loss_G: 3.2289 D(x): 0.9054 D(G(z)): 0.1184 / 0.0539
[10/20][900/1583] Loss_D: 0.5866 Loss_G: 4.6595 D(x): 0.9247 D(G(z)): 0.3470 / 0.0133
[10/20][950/1583] Loss_D: 0.2967 Loss_G: 4.1433 D(x): 0.9466 D(G(z)): 0.1993 / 0.0218
[10/20][1000/1583] Loss_D: 0.3342 Loss_G: 2.7950 D(x): 0.8801 D(G(z)): 0.1627 / 0.0827
[10/20][1050/1583] Loss_D: 0.2174 Loss_G: 4.5498 D(x): 0.9690 D(G(z)): 0.1555 / 0.0154
[10/20][1100/1583] Loss_D: 1.0542 Loss_G: 2.9927 D(x): 0.7132 D(G(z)): 0.3997 / 0.0745
[10/20][1150/1583] Loss_D: 1.7374 Loss_G: 4.8391 D(x): 0.9652 D(G(z)): 0.7398 / 0.0180
[10/20][1200/1583] Loss_D: 0.9674 Loss_G: 6.0433 D(x): 0.9941 D(G(z)): 0.5405 / 0.0033
[10/20][1250/1583] Loss_D: 1.1832 Loss_G: 4.7771 D(x): 0.9796 D(G(z)): 0.6156 / 0.0152
[10/20][1300/1583] Loss_D: 0.2012 Loss_G: 4.0343 D(x): 0.9415 D(G(z)): 0.1175 / 0.0273
[10/20][1350/1583] Loss_D: 0.2380 Loss_G: 3.4502 D(x): 0.8696 D(G(z)): 0.0748 / 0.0501
[10/20][1400/1583] Loss_D: 0.7133 Loss_G: 5.9600 D(x): 0.9878 D(G(z)): 0.4375 / 0.0051
[10/20][1450/1583] Loss_D: 0.2545 Loss_G: 3.9354 D(x): 0.9061 D(G(z)): 0.1249 / 0.0282
[10/20][1500/1583] Loss_D: 0.3176 Loss_G: 3.3065 D(x): 0.9375 D(G(z)): 0.2004 / 0.0544
[10/20][1550/1583] Loss_D: 0.2906 Loss_G: 4.1556 D(x): 0.9045 D(G(z)): 0.1512 / 0.0243
[11/20][0/1583] Loss_D: 0.4420 Loss_G: 5.3689 D(x): 0.9729 D(G(z)): 0.2932 / 0.0072
[11/20][50/1583] Loss_D: 0.2171 Loss_G: 3.1759 D(x): 0.8906 D(G(z)): 0.0813 / 0.0646
[11/20][100/1583] Loss_D: 0.4129 Loss_G: 1.5819 D(x): 0.7234 D(G(z)): 0.0346 / 0.2740
[11/20][150/1583] Loss_D: 0.2769 Loss_G: 4.0531 D(x): 0.9648 D(G(z)): 0.1988 / 0.0234
[11/20][200/1583] Loss_D: 0.1899 Loss_G: 4.2448 D(x): 0.9554 D(G(z)): 0.1256 / 0.0199
[11/20][250/1583] Loss_D: 5.5494 Loss_G: 7.5674 D(x): 0.9936 D(G(z)): 0.9849 / 0.0012
[11/20][300/1583] Loss_D: 0.5565 Loss_G: 5.1821 D(x): 0.9543 D(G(z)): 0.3435 / 0.0087
[11/20][350/1583] Loss_D: 0.3600 Loss_G: 3.7507 D(x): 0.9017 D(G(z)): 0.1938 / 0.0358
[11/20][400/1583] Loss_D: 0.2291 Loss_G: 3.8418 D(x): 0.9366 D(G(z)): 0.1390 / 0.0305
[11/20][450/1583] Loss_D: 1.1550 Loss_G: 5.3676 D(x): 0.9219 D(G(z)): 0.5864 / 0.0076
[11/20][500/1583] Loss_D: 0.3543 Loss_G: 3.3118 D(x): 0.8499 D(G(z)): 0.1438 / 0.0554
[11/20][550/1583] Loss_D: 0.1973 Loss_G: 3.6824 D(x): 0.9525 D(G(z)): 0.1272 / 0.0375
[11/20][600/1583] Loss_D: 0.1476 Loss_G: 3.9272 D(x): 0.9360 D(G(z)): 0.0712 / 0.0294
[11/20][650/1583] Loss_D: 0.7400 Loss_G: 1.1222 D(x): 0.5568 D(G(z)): 0.0079 / 0.4088
[11/20][700/1583] Loss_D: 1.0109 Loss_G: 6.3587 D(x): 0.9718 D(G(z)): 0.5298 / 0.0036
[11/20][750/1583] Loss_D: 0.2211 Loss_G: 4.0706 D(x): 0.9805 D(G(z)): 0.1694 / 0.0237
[11/20][800/1583] Loss_D: 0.5770 Loss_G: 3.7492 D(x): 0.8907 D(G(z)): 0.3026 / 0.0366
[11/20][850/1583] Loss_D: 0.5977 Loss_G: 6.1048 D(x): 0.9798 D(G(z)): 0.3782 / 0.0032
[11/20][900/1583] Loss_D: 0.1887 Loss_G: 4.0871 D(x): 0.9325 D(G(z)): 0.1003 / 0.0241
[11/20][950/1583] Loss_D: 0.5064 Loss_G: 3.1961 D(x): 0.8477 D(G(z)): 0.2475 / 0.0605
[11/20][1000/1583] Loss_D: 0.1634 Loss_G: 3.6051 D(x): 0.9100 D(G(z)): 0.0578 / 0.0415
[11/20][1050/1583] Loss_D: 0.3455 Loss_G: 4.2352 D(x): 0.9146 D(G(z)): 0.1848 / 0.0219
[11/20][1100/1583] Loss_D: 0.2474 Loss_G: 2.9496 D(x): 0.8627 D(G(z)): 0.0809 / 0.0845
[11/20][1150/1583] Loss_D: 0.2832 Loss_G: 3.0775 D(x): 0.8603 D(G(z)): 0.0916 / 0.0668
[11/20][1200/1583] Loss_D: 0.2517 Loss_G: 5.3835 D(x): 0.9611 D(G(z)): 0.1707 / 0.0072
[11/20][1250/1583] Loss_D: 0.3220 Loss_G: 2.7440 D(x): 0.7723 D(G(z)): 0.0204 / 0.0998
[11/20][1300/1583] Loss_D: 0.1644 Loss_G: 4.6127 D(x): 0.9614 D(G(z)): 0.1071 / 0.0140
[11/20][1350/1583] Loss_D: 0.8478 Loss_G: 1.5851 D(x): 0.5446 D(G(z)): 0.1176 / 0.2687
[11/20][1400/1583] Loss_D: 1.1538 Loss_G: 4.2830 D(x): 0.9453 D(G(z)): 0.5946 / 0.0220
[11/20][1450/1583] Loss_D: 2.3230 Loss_G: 0.3363 D(x): 0.1518 D(G(z)): 0.0075 / 0.7654
[11/20][1500/1583] Loss_D: 0.2925 Loss_G: 5.1475 D(x): 0.9659 D(G(z)): 0.2077 / 0.0081
[11/20][1550/1583] Loss_D: 0.9419 Loss_G: 3.6552 D(x): 0.9646 D(G(z)): 0.5320 / 0.0453
[12/20][0/1583] Loss_D: 0.2329 Loss_G: 3.5086 D(x): 0.9025 D(G(z)): 0.1089 / 0.0459
[12/20][50/1583] Loss_D: 0.2044 Loss_G: 2.7007 D(x): 0.8895 D(G(z)): 0.0736 / 0.0898
[12/20][100/1583] Loss_D: 0.1413 Loss_G: 3.5854 D(x): 0.9585 D(G(z)): 0.0867 / 0.0397
[12/20][150/1583] Loss_D: 0.4105 Loss_G: 2.9610 D(x): 0.9645 D(G(z)): 0.2726 / 0.0753
[12/20][200/1583] Loss_D: 0.1394 Loss_G: 3.6151 D(x): 0.9555 D(G(z)): 0.0807 / 0.0463
[12/20][250/1583] Loss_D: 0.3895 Loss_G: 4.1200 D(x): 0.9206 D(G(z)): 0.2326 / 0.0242
[12/20][300/1583] Loss_D: 0.1476 Loss_G: 3.8114 D(x): 0.9295 D(G(z)): 0.0670 / 0.0332
[12/20][350/1583] Loss_D: 0.2177 Loss_G: 3.9375 D(x): 0.9628 D(G(z)): 0.1479 / 0.0330
[12/20][400/1583] Loss_D: 1.7412 Loss_G: 0.6864 D(x): 0.3020 D(G(z)): 0.1364 / 0.5558
[12/20][450/1583] Loss_D: 0.5029 Loss_G: 5.4238 D(x): 0.9648 D(G(z)): 0.3219 / 0.0072
[12/20][500/1583] Loss_D: 0.1640 Loss_G: 3.6570 D(x): 0.9278 D(G(z)): 0.0772 / 0.0417
[12/20][550/1583] Loss_D: 0.1880 Loss_G: 3.7419 D(x): 0.9164 D(G(z)): 0.0822 / 0.0383
[12/20][600/1583] Loss_D: 0.5958 Loss_G: 1.7376 D(x): 0.7388 D(G(z)): 0.2005 / 0.2267
[12/20][650/1583] Loss_D: 0.1479 Loss_G: 3.5536 D(x): 0.9287 D(G(z)): 0.0621 / 0.0427
[12/20][700/1583] Loss_D: 0.4919 Loss_G: 5.9880 D(x): 0.9669 D(G(z)): 0.3191 / 0.0043
[12/20][750/1583] Loss_D: 0.1262 Loss_G: 3.7576 D(x): 0.9288 D(G(z)): 0.0464 / 0.0364
[12/20][800/1583] Loss_D: 0.1352 Loss_G: 4.5882 D(x): 0.9788 D(G(z)): 0.1020 / 0.0152
[12/20][850/1583] Loss_D: 1.1377 Loss_G: 4.6945 D(x): 0.9619 D(G(z)): 0.5560 / 0.0168
[12/20][900/1583] Loss_D: 0.3021 Loss_G: 2.8691 D(x): 0.8326 D(G(z)): 0.0899 / 0.0947
[12/20][950/1583] Loss_D: 3.6154 Loss_G: 0.1904 D(x): 0.0592 D(G(z)): 0.0439 / 0.8445
[12/20][1000/1583] Loss_D: 0.9654 Loss_G: 1.2462 D(x): 0.5309 D(G(z)): 0.1794 / 0.3414
[12/20][1050/1583] Loss_D: 0.5466 Loss_G: 3.0663 D(x): 0.8267 D(G(z)): 0.2374 / 0.0787
[12/20][1100/1583] Loss_D: 0.7550 Loss_G: 5.3458 D(x): 0.9694 D(G(z)): 0.4454 / 0.0077
[12/20][1150/1583] Loss_D: 0.7596 Loss_G: 5.1425 D(x): 0.9850 D(G(z)): 0.4588 / 0.0102
[12/20][1200/1583] Loss_D: 1.8692 Loss_G: 0.9769 D(x): 0.2173 D(G(z)): 0.0100 / 0.4478
[12/20][1250/1583] Loss_D: 0.6342 Loss_G: 2.4452 D(x): 0.6621 D(G(z)): 0.1301 / 0.1391
[12/20][1300/1583] Loss_D: 0.1406 Loss_G: 4.2106 D(x): 0.9654 D(G(z)): 0.0952 / 0.0235
[12/20][1350/1583] Loss_D: 0.2189 Loss_G: 4.2622 D(x): 0.9401 D(G(z)): 0.1320 / 0.0236
[12/20][1400/1583] Loss_D: 0.1422 Loss_G: 3.1437 D(x): 0.9681 D(G(z)): 0.0973 / 0.0636
[12/20][1450/1583] Loss_D: 0.2394 Loss_G: 3.6276 D(x): 0.8526 D(G(z)): 0.0582 / 0.0529
[12/20][1500/1583] Loss_D: 0.3643 Loss_G: 3.0499 D(x): 0.9099 D(G(z)): 0.2069 / 0.0641
[12/20][1550/1583] Loss_D: 0.1814 Loss_G: 3.2492 D(x): 0.9000 D(G(z)): 0.0637 / 0.0601
[13/20][0/1583] Loss_D: 0.1948 Loss_G: 3.0924 D(x): 0.9062 D(G(z)): 0.0800 / 0.0737
[13/20][50/1583] Loss_D: 0.2844 Loss_G: 4.5420 D(x): 0.9692 D(G(z)): 0.1984 / 0.0173
[13/20][100/1583] Loss_D: 0.2022 Loss_G: 4.4813 D(x): 0.9712 D(G(z)): 0.1458 / 0.0169
[13/20][150/1583] Loss_D: 0.2347 Loss_G: 4.6477 D(x): 0.9405 D(G(z)): 0.1446 / 0.0159
[13/20][200/1583] Loss_D: 0.5541 Loss_G: 3.5491 D(x): 0.9130 D(G(z)): 0.3298 / 0.0446
[13/20][250/1583] Loss_D: 0.1553 Loss_G: 3.9486 D(x): 0.9367 D(G(z)): 0.0775 / 0.0319
[13/20][300/1583] Loss_D: 0.2056 Loss_G: 4.4578 D(x): 0.9795 D(G(z)): 0.1509 / 0.0194
[13/20][350/1583] Loss_D: 0.1145 Loss_G: 3.9158 D(x): 0.9419 D(G(z)): 0.0489 / 0.0358
[13/20][400/1583] Loss_D: 0.1810 Loss_G: 3.9198 D(x): 0.9068 D(G(z)): 0.0628 / 0.0342
[13/20][450/1583] Loss_D: 0.1737 Loss_G: 4.3717 D(x): 0.9566 D(G(z)): 0.1110 / 0.0204
[13/20][500/1583] Loss_D: 0.1324 Loss_G: 3.6611 D(x): 0.9361 D(G(z)): 0.0589 / 0.0383
[13/20][550/1583] Loss_D: 0.1221 Loss_G: 4.1953 D(x): 0.9447 D(G(z)): 0.0588 / 0.0233
[13/20][600/1583] Loss_D: 0.4661 Loss_G: 3.5945 D(x): 0.9576 D(G(z)): 0.3072 / 0.0404
[13/20][650/1583] Loss_D: 0.3837 Loss_G: 6.8633 D(x): 0.9804 D(G(z)): 0.2731 / 0.0017
[13/20][700/1583] Loss_D: 0.7979 Loss_G: 3.4169 D(x): 0.8841 D(G(z)): 0.4090 / 0.0527
[13/20][750/1583] Loss_D: 1.2752 Loss_G: 1.2170 D(x): 0.5623 D(G(z)): 0.3042 / 0.3905
[13/20][800/1583] Loss_D: 0.8152 Loss_G: 6.7914 D(x): 0.9930 D(G(z)): 0.4845 / 0.0017
[13/20][850/1583] Loss_D: 0.2118 Loss_G: 4.2853 D(x): 0.9474 D(G(z)): 0.1337 / 0.0215
[13/20][900/1583] Loss_D: 0.2051 Loss_G: 3.4792 D(x): 0.9264 D(G(z)): 0.1058 / 0.0455
[13/20][950/1583] Loss_D: 0.2248 Loss_G: 2.8438 D(x): 0.8539 D(G(z)): 0.0430 / 0.0884
[13/20][1000/1583] Loss_D: 0.2403 Loss_G: 2.5586 D(x): 0.8343 D(G(z)): 0.0397 / 0.1121
[13/20][1050/1583] Loss_D: 0.1286 Loss_G: 3.0089 D(x): 0.9641 D(G(z)): 0.0807 / 0.0763
[13/20][1100/1583] Loss_D: 0.5571 Loss_G: 3.4412 D(x): 0.9439 D(G(z)): 0.3437 / 0.0475
[13/20][1150/1583] Loss_D: 0.2479 Loss_G: 4.0702 D(x): 0.9362 D(G(z)): 0.1472 / 0.0280
[13/20][1200/1583] Loss_D: 0.4653 Loss_G: 2.1317 D(x): 0.7067 D(G(z)): 0.0458 / 0.1941
[13/20][1250/1583] Loss_D: 0.1503 Loss_G: 4.3328 D(x): 0.9729 D(G(z)): 0.1086 / 0.0189
[13/20][1300/1583] Loss_D: 0.1317 Loss_G: 3.9708 D(x): 0.9537 D(G(z)): 0.0671 / 0.0300
[13/20][1350/1583] Loss_D: 0.1118 Loss_G: 3.9194 D(x): 0.9500 D(G(z)): 0.0534 / 0.0319
[13/20][1400/1583] Loss_D: 0.1906 Loss_G: 3.5532 D(x): 0.8952 D(G(z)): 0.0578 / 0.0519
[13/20][1450/1583] Loss_D: 0.9284 Loss_G: 1.2762 D(x): 0.5485 D(G(z)): 0.1624 / 0.3366
[13/20][1500/1583] Loss_D: 0.9241 Loss_G: 1.8346 D(x): 0.5324 D(G(z)): 0.1070 / 0.2368
[13/20][1550/1583] Loss_D: 0.2680 Loss_G: 3.7866 D(x): 0.8293 D(G(z)): 0.0453 / 0.0422
[14/20][0/1583] Loss_D: 0.1989 Loss_G: 4.7444 D(x): 0.9594 D(G(z)): 0.1353 / 0.0127
[14/20][50/1583] Loss_D: 0.1971 Loss_G: 2.5916 D(x): 0.8815 D(G(z)): 0.0576 / 0.1124
[14/20][100/1583] Loss_D: 0.1191 Loss_G: 3.8794 D(x): 0.9612 D(G(z)): 0.0724 / 0.0299
[14/20][150/1583] Loss_D: 0.1835 Loss_G: 5.2542 D(x): 0.9788 D(G(z)): 0.1383 / 0.0083
[14/20][200/1583] Loss_D: 0.3187 Loss_G: 3.5453 D(x): 0.8965 D(G(z)): 0.1660 / 0.0436
[14/20][250/1583] Loss_D: 0.1011 Loss_G: 4.4302 D(x): 0.9554 D(G(z)): 0.0486 / 0.0182
[14/20][300/1583] Loss_D: 0.1254 Loss_G: 3.6950 D(x): 0.9370 D(G(z)): 0.0537 / 0.0402
[14/20][350/1583] Loss_D: 0.3753 Loss_G: 2.9771 D(x): 0.7759 D(G(z)): 0.0636 / 0.0855
[14/20][400/1583] Loss_D: 1.8167 Loss_G: 1.0710 D(x): 0.2214 D(G(z)): 0.0314 / 0.4136
[14/20][450/1583] Loss_D: 0.5008 Loss_G: 2.8074 D(x): 0.7281 D(G(z)): 0.1087 / 0.0906
[14/20][500/1583] Loss_D: 0.2605 Loss_G: 2.9950 D(x): 0.8243 D(G(z)): 0.0443 / 0.0843
[14/20][550/1583] Loss_D: 0.1909 Loss_G: 4.0212 D(x): 0.9111 D(G(z)): 0.0764 / 0.0298
[14/20][600/1583] Loss_D: 0.2020 Loss_G: 2.3619 D(x): 0.8611 D(G(z)): 0.0350 / 0.1540
[14/20][650/1583] Loss_D: 0.2275 Loss_G: 2.9458 D(x): 0.8885 D(G(z)): 0.0928 / 0.0741
[14/20][700/1583] Loss_D: 0.0784 Loss_G: 4.4522 D(x): 0.9726 D(G(z)): 0.0462 / 0.0207
[14/20][750/1583] Loss_D: 0.0868 Loss_G: 3.5306 D(x): 0.9504 D(G(z)): 0.0325 / 0.0493
[14/20][800/1583] Loss_D: 0.1172 Loss_G: 3.4457 D(x): 0.9258 D(G(z)): 0.0311 / 0.0497
[14/20][850/1583] Loss_D: 0.1150 Loss_G: 4.7514 D(x): 0.9659 D(G(z)): 0.0681 / 0.0138
[14/20][900/1583] Loss_D: 0.6961 Loss_G: 1.0161 D(x): 0.5965 D(G(z)): 0.0307 / 0.4432
[14/20][950/1583] Loss_D: 0.5427 Loss_G: 2.5343 D(x): 0.6771 D(G(z)): 0.0792 / 0.1282
[14/20][1000/1583] Loss_D: 0.8422 Loss_G: 4.1118 D(x): 0.9256 D(G(z)): 0.4544 / 0.0273
[14/20][1050/1583] Loss_D: 1.2674 Loss_G: 5.3719 D(x): 0.9754 D(G(z)): 0.6236 / 0.0107
[14/20][1100/1583] Loss_D: 0.1675 Loss_G: 3.5696 D(x): 0.8867 D(G(z)): 0.0351 / 0.0474
[14/20][1150/1583] Loss_D: 0.1116 Loss_G: 3.9747 D(x): 0.9553 D(G(z)): 0.0592 / 0.0319
[14/20][1200/1583] Loss_D: 0.6670 Loss_G: 2.2726 D(x): 0.6947 D(G(z)): 0.1970 / 0.1443
[14/20][1250/1583] Loss_D: 0.1379 Loss_G: 4.7130 D(x): 0.9647 D(G(z)): 0.0851 / 0.0168
[14/20][1300/1583] Loss_D: 0.1290 Loss_G: 3.9698 D(x): 0.9321 D(G(z)): 0.0501 / 0.0351
[14/20][1350/1583] Loss_D: 0.1147 Loss_G: 3.5459 D(x): 0.9406 D(G(z)): 0.0484 / 0.0436
[14/20][1400/1583] Loss_D: 1.3923 Loss_G: 1.5737 D(x): 0.6575 D(G(z)): 0.4338 / 0.3123
[14/20][1450/1583] Loss_D: 0.5742 Loss_G: 2.1677 D(x): 0.6605 D(G(z)): 0.0646 / 0.1866
[14/20][1500/1583] Loss_D: 0.4496 Loss_G: 3.5803 D(x): 0.8910 D(G(z)): 0.2410 / 0.0469
[14/20][1550/1583] Loss_D: 0.2170 Loss_G: 3.1385 D(x): 0.8570 D(G(z)): 0.0334 / 0.0682
[15/20][0/1583] Loss_D: 1.2796 Loss_G: 3.3778 D(x): 0.8812 D(G(z)): 0.5999 / 0.0576
[15/20][50/1583] Loss_D: 0.1698 Loss_G: 3.5684 D(x): 0.9126 D(G(z)): 0.0629 / 0.0483
[15/20][100/1583] Loss_D: 0.2076 Loss_G: 3.1623 D(x): 0.8790 D(G(z)): 0.0629 / 0.0730
[15/20][150/1583] Loss_D: 0.1487 Loss_G: 4.5114 D(x): 0.8858 D(G(z)): 0.0096 / 0.0205
[15/20][200/1583] Loss_D: 0.1168 Loss_G: 3.9740 D(x): 0.9295 D(G(z)): 0.0341 / 0.0319
[15/20][250/1583] Loss_D: 0.1519 Loss_G: 4.3522 D(x): 0.9498 D(G(z)): 0.0826 / 0.0225
[15/20][300/1583] Loss_D: 0.1599 Loss_G: 3.2387 D(x): 0.8821 D(G(z)): 0.0215 / 0.0672
[15/20][350/1583] Loss_D: 0.1400 Loss_G: 5.3293 D(x): 0.9823 D(G(z)): 0.1030 / 0.0078
[15/20][400/1583] Loss_D: 0.1654 Loss_G: 4.2977 D(x): 0.9338 D(G(z)): 0.0799 / 0.0245
[15/20][450/1583] Loss_D: 0.1649 Loss_G: 4.2507 D(x): 0.9492 D(G(z)): 0.0932 / 0.0248
[15/20][500/1583] Loss_D: 0.3518 Loss_G: 5.5630 D(x): 0.9538 D(G(z)): 0.2218 / 0.0071
[15/20][550/1583] Loss_D: 0.2416 Loss_G: 3.6121 D(x): 0.8917 D(G(z)): 0.0970 / 0.0500
[15/20][600/1583] Loss_D: 0.9827 Loss_G: 3.2378 D(x): 0.8402 D(G(z)): 0.4914 / 0.0543
[15/20][650/1583] Loss_D: 0.7927 Loss_G: 1.4657 D(x): 0.6582 D(G(z)): 0.2021 / 0.2974
[15/20][700/1583] Loss_D: 0.2597 Loss_G: 6.1874 D(x): 0.9747 D(G(z)): 0.1770 / 0.0041
[15/20][750/1583] Loss_D: 0.6341 Loss_G: 4.5647 D(x): 0.9886 D(G(z)): 0.3928 / 0.0161
[15/20][800/1583] Loss_D: 0.1049 Loss_G: 4.0093 D(x): 0.9326 D(G(z)): 0.0309 / 0.0294
[15/20][850/1583] Loss_D: 0.2053 Loss_G: 4.5544 D(x): 0.9180 D(G(z)): 0.1014 / 0.0171
[15/20][900/1583] Loss_D: 0.3169 Loss_G: 4.2614 D(x): 0.9107 D(G(z)): 0.1746 / 0.0224
[15/20][950/1583] Loss_D: 0.1002 Loss_G: 4.4715 D(x): 0.9744 D(G(z)): 0.0682 / 0.0183
[15/20][1000/1583] Loss_D: 0.1638 Loss_G: 4.4362 D(x): 0.9780 D(G(z)): 0.1214 / 0.0191
[15/20][1050/1583] Loss_D: 0.1286 Loss_G: 4.5392 D(x): 0.9126 D(G(z)): 0.0289 / 0.0222
[15/20][1100/1583] Loss_D: 0.0945 Loss_G: 4.6884 D(x): 0.9696 D(G(z)): 0.0571 / 0.0160
[15/20][1150/1583] Loss_D: 0.1025 Loss_G: 3.6900 D(x): 0.9474 D(G(z)): 0.0434 / 0.0403
[15/20][1200/1583] Loss_D: 2.4789 Loss_G: 0.4233 D(x): 0.1553 D(G(z)): 0.0047 / 0.6973
[15/20][1250/1583] Loss_D: 0.1330 Loss_G: 4.3373 D(x): 0.9340 D(G(z)): 0.0564 / 0.0228
[15/20][1300/1583] Loss_D: 0.0977 Loss_G: 4.1846 D(x): 0.9572 D(G(z)): 0.0491 / 0.0238
[15/20][1350/1583] Loss_D: 0.4272 Loss_G: 2.1301 D(x): 0.7160 D(G(z)): 0.0252 / 0.2052
[15/20][1400/1583] Loss_D: 3.8339 Loss_G: 0.6287 D(x): 0.0447 D(G(z)): 0.0005 / 0.5831
[15/20][1450/1583] Loss_D: 0.9413 Loss_G: 3.1921 D(x): 0.6937 D(G(z)): 0.3047 / 0.1274
[15/20][1500/1583] Loss_D: 0.2445 Loss_G: 4.2717 D(x): 0.9180 D(G(z)): 0.1208 / 0.0259
[15/20][1550/1583] Loss_D: 0.1066 Loss_G: 4.0752 D(x): 0.9529 D(G(z)): 0.0521 / 0.0286
[16/20][0/1583] Loss_D: 0.1498 Loss_G: 4.7893 D(x): 0.9734 D(G(z)): 0.1037 / 0.0132
[16/20][50/1583] Loss_D: 0.1188 Loss_G: 4.0038 D(x): 0.9538 D(G(z)): 0.0629 / 0.0295
[16/20][100/1583] Loss_D: 0.1006 Loss_G: 4.1583 D(x): 0.9356 D(G(z)): 0.0263 / 0.0293
[16/20][150/1583] Loss_D: 0.7203 Loss_G: 6.8572 D(x): 0.9780 D(G(z)): 0.4173 / 0.0021
[16/20][200/1583] Loss_D: 0.0920 Loss_G: 3.9698 D(x): 0.9419 D(G(z)): 0.0282 / 0.0378
[16/20][250/1583] Loss_D: 0.6379 Loss_G: 8.9561 D(x): 0.9979 D(G(z)): 0.4009 / 0.0003
[16/20][300/1583] Loss_D: 0.6091 Loss_G: 3.3029 D(x): 0.8244 D(G(z)): 0.2925 / 0.0539
[16/20][350/1583] Loss_D: 4.3029 Loss_G: 7.3242 D(x): 0.9886 D(G(z)): 0.9378 / 0.0029
[16/20][400/1583] Loss_D: 2.0085 Loss_G: 0.9957 D(x): 0.2677 D(G(z)): 0.1036 / 0.4792
[16/20][450/1583] Loss_D: 0.6552 Loss_G: 2.6439 D(x): 0.6444 D(G(z)): 0.0637 / 0.1108
[16/20][500/1583] Loss_D: 0.3052 Loss_G: 2.6446 D(x): 0.8240 D(G(z)): 0.0669 / 0.1124
[16/20][550/1583] Loss_D: 0.1454 Loss_G: 5.1004 D(x): 0.9791 D(G(z)): 0.1087 / 0.0089
[16/20][600/1583] Loss_D: 0.2425 Loss_G: 4.7075 D(x): 0.9728 D(G(z)): 0.1730 / 0.0139
[16/20][650/1583] Loss_D: 0.1498 Loss_G: 5.8204 D(x): 0.9928 D(G(z)): 0.1251 / 0.0047
[16/20][700/1583] Loss_D: 0.1259 Loss_G: 5.5439 D(x): 0.9864 D(G(z)): 0.0981 / 0.0062
[16/20][750/1583] Loss_D: 0.0915 Loss_G: 3.7784 D(x): 0.9566 D(G(z)): 0.0431 / 0.0354
[16/20][800/1583] Loss_D: 0.2413 Loss_G: 5.6368 D(x): 0.9798 D(G(z)): 0.1697 / 0.0059
[16/20][850/1583] Loss_D: 0.2382 Loss_G: 7.1151 D(x): 0.9910 D(G(z)): 0.1791 / 0.0014
[16/20][900/1583] Loss_D: 0.6640 Loss_G: 7.8861 D(x): 0.9967 D(G(z)): 0.4225 / 0.0007
[16/20][950/1583] Loss_D: 1.8672 Loss_G: 1.0168 D(x): 0.2395 D(G(z)): 0.0556 / 0.4409
[16/20][1000/1583] Loss_D: 0.2520 Loss_G: 4.3822 D(x): 0.9286 D(G(z)): 0.1402 / 0.0238
[16/20][1050/1583] Loss_D: 0.0967 Loss_G: 4.2129 D(x): 0.9404 D(G(z)): 0.0291 / 0.0291
[16/20][1100/1583] Loss_D: 0.1344 Loss_G: 3.7313 D(x): 0.8952 D(G(z)): 0.0109 / 0.0415
[16/20][1150/1583] Loss_D: 0.1069 Loss_G: 3.4631 D(x): 0.9329 D(G(z)): 0.0310 / 0.0542
[16/20][1200/1583] Loss_D: 0.1169 Loss_G: 4.4238 D(x): 0.9630 D(G(z)): 0.0691 / 0.0211
[16/20][1250/1583] Loss_D: 0.1653 Loss_G: 3.5812 D(x): 0.9259 D(G(z)): 0.0671 / 0.0469
[16/20][1300/1583] Loss_D: 0.8881 Loss_G: 4.5451 D(x): 0.9112 D(G(z)): 0.4803 / 0.0160
[16/20][1350/1583] Loss_D: 0.1268 Loss_G: 4.0125 D(x): 0.9098 D(G(z)): 0.0249 / 0.0344
[16/20][1400/1583] Loss_D: 0.1863 Loss_G: 3.1499 D(x): 0.8899 D(G(z)): 0.0525 / 0.0709
[16/20][1450/1583] Loss_D: 2.3809 Loss_G: 3.3642 D(x): 0.9494 D(G(z)): 0.8505 / 0.0663
[16/20][1500/1583] Loss_D: 0.3161 Loss_G: 2.8555 D(x): 0.8365 D(G(z)): 0.0762 / 0.1079
[16/20][1550/1583] Loss_D: 0.4346 Loss_G: 3.4285 D(x): 0.8773 D(G(z)): 0.2172 / 0.0524
[17/20][0/1583] Loss_D: 0.2392 Loss_G: 4.2136 D(x): 0.9778 D(G(z)): 0.1636 / 0.0273
[17/20][50/1583] Loss_D: 0.1600 Loss_G: 2.9440 D(x): 0.8882 D(G(z)): 0.0296 / 0.0903
[17/20][100/1583] Loss_D: 0.1037 Loss_G: 4.1535 D(x): 0.9437 D(G(z)): 0.0375 / 0.0280
[17/20][150/1583] Loss_D: 0.0924 Loss_G: 4.2635 D(x): 0.9463 D(G(z)): 0.0328 / 0.0255
[17/20][200/1583] Loss_D: 0.0853 Loss_G: 4.4486 D(x): 0.9834 D(G(z)): 0.0580 / 0.0181
[17/20][250/1583] Loss_D: 0.1854 Loss_G: 4.5378 D(x): 0.9576 D(G(z)): 0.1196 / 0.0163
[17/20][300/1583] Loss_D: 0.0713 Loss_G: 4.6036 D(x): 0.9904 D(G(z)): 0.0570 / 0.0166
[17/20][350/1583] Loss_D: 0.0815 Loss_G: 4.6050 D(x): 0.9753 D(G(z)): 0.0480 / 0.0177
[17/20][400/1583] Loss_D: 0.0774 Loss_G: 4.6454 D(x): 0.9582 D(G(z)): 0.0324 / 0.0174
[17/20][450/1583] Loss_D: 0.0776 Loss_G: 4.7100 D(x): 0.9765 D(G(z)): 0.0498 / 0.0158
[17/20][500/1583] Loss_D: 0.0655 Loss_G: 4.3478 D(x): 0.9525 D(G(z)): 0.0143 / 0.0207
[17/20][550/1583] Loss_D: 0.7846 Loss_G: 5.5213 D(x): 0.9698 D(G(z)): 0.4275 / 0.0076
[17/20][600/1583] Loss_D: 0.3724 Loss_G: 4.9101 D(x): 0.9761 D(G(z)): 0.2327 / 0.0129
[17/20][650/1583] Loss_D: 0.1947 Loss_G: 3.8036 D(x): 0.8589 D(G(z)): 0.0167 / 0.0483
[17/20][700/1583] Loss_D: 0.1749 Loss_G: 3.6601 D(x): 0.9395 D(G(z)): 0.0960 / 0.0394
[17/20][750/1583] Loss_D: 0.0840 Loss_G: 4.9581 D(x): 0.9879 D(G(z)): 0.0641 / 0.0127
[17/20][800/1583] Loss_D: 0.0949 Loss_G: 4.9550 D(x): 0.9350 D(G(z)): 0.0222 / 0.0119
[17/20][850/1583] Loss_D: 0.7939 Loss_G: 2.4465 D(x): 0.5609 D(G(z)): 0.0214 / 0.1491
[17/20][900/1583] Loss_D: 0.2218 Loss_G: 2.8206 D(x): 0.8244 D(G(z)): 0.0073 / 0.1055
[17/20][950/1583] Loss_D: 0.6580 Loss_G: 2.1504 D(x): 0.6262 D(G(z)): 0.0473 / 0.1917
[17/20][1000/1583] Loss_D: 0.8152 Loss_G: 7.2704 D(x): 0.9978 D(G(z)): 0.4778 / 0.0014
[17/20][1050/1583] Loss_D: 0.1632 Loss_G: 5.6456 D(x): 0.9865 D(G(z)): 0.1231 / 0.0055
[17/20][1100/1583] Loss_D: 0.1255 Loss_G: 4.7188 D(x): 0.9645 D(G(z)): 0.0771 / 0.0173
[17/20][1150/1583] Loss_D: 0.1923 Loss_G: 3.0381 D(x): 0.8625 D(G(z)): 0.0231 / 0.0849
[17/20][1200/1583] Loss_D: 0.1349 Loss_G: 3.6719 D(x): 0.9055 D(G(z)): 0.0185 / 0.0535
[17/20][1250/1583] Loss_D: 0.0961 Loss_G: 5.2665 D(x): 0.9670 D(G(z)): 0.0554 / 0.0111
[17/20][1300/1583] Loss_D: 0.0753 Loss_G: 4.6870 D(x): 0.9463 D(G(z)): 0.0164 / 0.0189
[17/20][1350/1583] Loss_D: 0.1761 Loss_G: 5.7378 D(x): 0.9731 D(G(z)): 0.1212 / 0.0054
[17/20][1400/1583] Loss_D: 0.4319 Loss_G: 4.6168 D(x): 0.9011 D(G(z)): 0.2229 / 0.0188
[17/20][1450/1583] Loss_D: 1.8176 Loss_G: 1.7620 D(x): 0.2571 D(G(z)): 0.0079 / 0.2608
[17/20][1500/1583] Loss_D: 0.7519 Loss_G: 2.5534 D(x): 0.6595 D(G(z)): 0.1920 / 0.1095
[17/20][1550/1583] Loss_D: 0.3474 Loss_G: 3.1570 D(x): 0.7708 D(G(z)): 0.0265 / 0.0666
[18/20][0/1583] Loss_D: 0.4150 Loss_G: 6.7068 D(x): 0.9909 D(G(z)): 0.2803 / 0.0026
[18/20][50/1583] Loss_D: 0.0937 Loss_G: 4.4016 D(x): 0.9593 D(G(z)): 0.0464 / 0.0216
[18/20][100/1583] Loss_D: 0.1152 Loss_G: 3.8754 D(x): 0.9251 D(G(z)): 0.0278 / 0.0374
[18/20][150/1583] Loss_D: 0.0969 Loss_G: 3.9085 D(x): 0.9341 D(G(z)): 0.0251 / 0.0368
[18/20][200/1583] Loss_D: 0.0734 Loss_G: 4.3257 D(x): 0.9632 D(G(z)): 0.0328 / 0.0215
[18/20][250/1583] Loss_D: 0.0503 Loss_G: 4.2014 D(x): 0.9792 D(G(z)): 0.0278 / 0.0248
[18/20][300/1583] Loss_D: 0.0966 Loss_G: 5.3913 D(x): 0.9919 D(G(z)): 0.0794 / 0.0074
[18/20][350/1583] Loss_D: 0.0304 Loss_G: 5.3741 D(x): 0.9839 D(G(z)): 0.0138 / 0.0083
[18/20][400/1583] Loss_D: 0.0399 Loss_G: 5.1732 D(x): 0.9874 D(G(z)): 0.0258 / 0.0126
[18/20][450/1583] Loss_D: 0.2534 Loss_G: 3.4210 D(x): 0.8515 D(G(z)): 0.0577 / 0.0737
[18/20][500/1583] Loss_D: 0.5689 Loss_G: 2.7608 D(x): 0.8144 D(G(z)): 0.2366 / 0.1026
[18/20][550/1583] Loss_D: 1.6885 Loss_G: 3.8599 D(x): 0.9336 D(G(z)): 0.6982 / 0.0456
[18/20][600/1583] Loss_D: 0.1192 Loss_G: 3.8819 D(x): 0.9474 D(G(z)): 0.0560 / 0.0391
[18/20][650/1583] Loss_D: 0.1555 Loss_G: 4.7784 D(x): 0.9351 D(G(z)): 0.0686 / 0.0197
[18/20][700/1583] Loss_D: 0.0900 Loss_G: 4.8602 D(x): 0.9492 D(G(z)): 0.0317 / 0.0166
[18/20][750/1583] Loss_D: 0.8866 Loss_G: 5.2242 D(x): 0.9657 D(G(z)): 0.4731 / 0.0104
[18/20][800/1583] Loss_D: 0.1804 Loss_G: 4.2431 D(x): 0.9391 D(G(z)): 0.0942 / 0.0281
[18/20][850/1583] Loss_D: 0.0781 Loss_G: 4.6382 D(x): 0.9677 D(G(z)): 0.0384 / 0.0173
[18/20][900/1583] Loss_D: 0.1476 Loss_G: 6.2146 D(x): 0.9865 D(G(z)): 0.1143 / 0.0034
[18/20][950/1583] Loss_D: 0.0834 Loss_G: 4.4089 D(x): 0.9596 D(G(z)): 0.0362 / 0.0230
[18/20][1000/1583] Loss_D: 2.5929 Loss_G: 0.4819 D(x): 0.1635 D(G(z)): 0.0008 / 0.6707
[18/20][1050/1583] Loss_D: 0.1582 Loss_G: 3.9669 D(x): 0.8825 D(G(z)): 0.0202 / 0.0364
[18/20][1100/1583] Loss_D: 0.0735 Loss_G: 4.3646 D(x): 0.9668 D(G(z)): 0.0366 / 0.0225
[18/20][1150/1583] Loss_D: 1.3856 Loss_G: 2.4325 D(x): 0.6476 D(G(z)): 0.3604 / 0.2226
[18/20][1200/1583] Loss_D: 0.2291 Loss_G: 2.3906 D(x): 0.8539 D(G(z)): 0.0511 / 0.1526
[18/20][1250/1583] Loss_D: 1.0133 Loss_G: 1.5649 D(x): 0.6328 D(G(z)): 0.3058 / 0.2654
[18/20][1300/1583] Loss_D: 0.3146 Loss_G: 3.2187 D(x): 0.8018 D(G(z)): 0.0583 / 0.0770
[18/20][1350/1583] Loss_D: 0.8367 Loss_G: 1.1783 D(x): 0.5381 D(G(z)): 0.0149 / 0.3956
[18/20][1400/1583] Loss_D: 0.0882 Loss_G: 4.5715 D(x): 0.9458 D(G(z)): 0.0284 / 0.0173
[18/20][1450/1583] Loss_D: 0.1374 Loss_G: 4.6492 D(x): 0.9769 D(G(z)): 0.0970 / 0.0168
[18/20][1500/1583] Loss_D: 0.0925 Loss_G: 4.1705 D(x): 0.9333 D(G(z)): 0.0196 / 0.0300
[18/20][1550/1583] Loss_D: 0.1096 Loss_G: 4.9891 D(x): 0.9694 D(G(z)): 0.0579 / 0.0147
[19/20][0/1583] Loss_D: 0.0814 Loss_G: 4.2667 D(x): 0.9630 D(G(z)): 0.0395 / 0.0250
[19/20][50/1583] Loss_D: 0.1355 Loss_G: 5.3479 D(x): 0.9750 D(G(z)): 0.0964 / 0.0080
[19/20][100/1583] Loss_D: 0.0680 Loss_G: 4.8436 D(x): 0.9611 D(G(z)): 0.0249 / 0.0152
[19/20][150/1583] Loss_D: 0.1064 Loss_G: 3.1641 D(x): 0.9339 D(G(z)): 0.0288 / 0.0780
[19/20][200/1583] Loss_D: 0.0728 Loss_G: 4.7479 D(x): 0.9545 D(G(z)): 0.0235 / 0.0180
[19/20][250/1583] Loss_D: 0.5200 Loss_G: 2.5695 D(x): 0.8110 D(G(z)): 0.2162 / 0.1155
[19/20][300/1583] Loss_D: 0.1154 Loss_G: 4.3183 D(x): 0.9633 D(G(z)): 0.0698 / 0.0260
[19/20][350/1583] Loss_D: 1.1636 Loss_G: 1.8492 D(x): 0.6942 D(G(z)): 0.4510 / 0.2161
[19/20][400/1583] Loss_D: 0.5549 Loss_G: 3.0729 D(x): 0.7290 D(G(z)): 0.1001 / 0.0819
[19/20][450/1583] Loss_D: 0.2882 Loss_G: 2.2298 D(x): 0.7973 D(G(z)): 0.0201 / 0.1892
[19/20][500/1583] Loss_D: 0.1520 Loss_G: 4.7678 D(x): 0.9477 D(G(z)): 0.0812 / 0.0186
[19/20][550/1583] Loss_D: 0.0880 Loss_G: 3.8464 D(x): 0.9473 D(G(z)): 0.0289 / 0.0381
[19/20][600/1583] Loss_D: 0.0710 Loss_G: 5.4901 D(x): 0.9791 D(G(z)): 0.0462 / 0.0081
[19/20][650/1583] Loss_D: 0.2505 Loss_G: 3.1630 D(x): 0.8928 D(G(z)): 0.1099 / 0.0610
[19/20][700/1583] Loss_D: 0.5833 Loss_G: 3.9856 D(x): 0.8569 D(G(z)): 0.2875 / 0.0325
[19/20][750/1583] Loss_D: 0.5008 Loss_G: 1.7952 D(x): 0.6639 D(G(z)): 0.0035 / 0.2825
[19/20][800/1583] Loss_D: 0.0785 Loss_G: 4.4216 D(x): 0.9554 D(G(z)): 0.0277 / 0.0247
[19/20][850/1583] Loss_D: 0.2170 Loss_G: 3.2202 D(x): 0.9020 D(G(z)): 0.0731 / 0.0593
[19/20][900/1583] Loss_D: 0.0965 Loss_G: 4.5541 D(x): 0.9478 D(G(z)): 0.0339 / 0.0232
[19/20][950/1583] Loss_D: 0.7587 Loss_G: 1.8880 D(x): 0.6176 D(G(z)): 0.1392 / 0.2118
[19/20][1000/1583] Loss_D: 0.0681 Loss_G: 5.0277 D(x): 0.9803 D(G(z)): 0.0441 / 0.0124
[19/20][1050/1583] Loss_D: 0.9239 Loss_G: 1.3154 D(x): 0.5490 D(G(z)): 0.1124 / 0.3449
[19/20][1100/1583] Loss_D: 0.2015 Loss_G: 5.1364 D(x): 0.9422 D(G(z)): 0.1122 / 0.0123
[19/20][1150/1583] Loss_D: 0.1000 Loss_G: 4.3750 D(x): 0.9435 D(G(z)): 0.0337 / 0.0244
[19/20][1200/1583] Loss_D: 0.0634 Loss_G: 4.4012 D(x): 0.9620 D(G(z)): 0.0228 / 0.0245
[19/20][1250/1583] Loss_D: 0.1163 Loss_G: 6.0967 D(x): 0.9893 D(G(z)): 0.0919 / 0.0042
[19/20][1300/1583] Loss_D: 1.8999 Loss_G: 7.2711 D(x): 0.9916 D(G(z)): 0.7757 / 0.0013
[19/20][1350/1583] Loss_D: 0.4245 Loss_G: 3.6919 D(x): 0.7111 D(G(z)): 0.0166 / 0.0532
[19/20][1400/1583] Loss_D: 0.0654 Loss_G: 4.9443 D(x): 0.9873 D(G(z)): 0.0478 / 0.0151
[19/20][1450/1583] Loss_D: 0.6750 Loss_G: 1.7718 D(x): 0.6017 D(G(z)): 0.0736 / 0.2655
[19/20][1500/1583] Loss_D: 0.3889 Loss_G: 3.4791 D(x): 0.7383 D(G(z)): 0.0078 / 0.0612
[19/20][1550/1583] Loss_D: 0.0771 Loss_G: 4.4849 D(x): 0.9462 D(G(z)): 0.0188 / 0.0183 
plt.figure(figsize=(10,5))
plt.title("Generator and Discriminator Loss During Training")
plt.plot(G_losses,label="G")
plt.plot(D_losses,label="D")
plt.xlabel("iterations")
plt.ylabel("Loss")
plt.legend()
plt.show()

png

#%%capture
fig = plt.figure(figsize=(8,8))
plt.axis("off")
ims = [[plt.imshow(np.transpose(i,(1,2,0)), animated=True)] for i in img_list]
ani = animation.ArtistAnimation(fig, ims, interval=1000, repeat_delay=1000, blit=True)

HTML(ani.to_jshtml())

png png

# Grab a batch of real images from the dataloader
real_batch = next(iter(dataloader))

# Plot the real images
plt.figure(figsize=(15,15))
plt.subplot(1,2,1)
plt.axis("off")
plt.title("Real Images")
plt.imshow(np.transpose(vutils.make_grid(real_batch[0].to(device)[:64], padding=5, normalize=True).cpu(),(1,2,0)))

# Plot the fake images from the last epoch
plt.subplot(1,2,2)
plt.axis("off")
plt.title("Fake Images")
plt.imshow(np.transpose(img_list[-1],(1,2,0)))
plt.show()

png png













2019 Deepest Season 5 (2018.12.29 ~ 2019.06.15)








⚠️ **GitHub.com Fallback** ⚠️