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DCGAN 教程

译者:片刻小哥哥

项目地址:https://pytorch.apachecn.org/2.0/tutorials/beginner/dcgan_faces_tutorial

原始地址:https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html

作者 : Nathan Inkawhich

简介

本教程将通过示例介绍 DCGAN。我们将训练生成对抗网络 (GAN),在向其展示许多真实名人的照片后生成新的名人。这里的大部分代码来自 pytorch/examples 中的 DCGAN 实现,并且这个文档将给出实现和棚的彻底解释深入了解该模型如何以及为何发挥作用。但不用’担心,不需要事先 了解 GAN,但可能需要初学者花 一些时间来推理幕后实际发生的情况。 此外,为了节省时间拥有一个或两个 GPU 会有所帮助。让 从头开始吧。

生成对抗网络

什么是 GAN?

GAN 是一个框架,用于教授深度学习模型以捕获训练数据分布,以便我们可以从同一分布生成新数据。 GAN 由 Ian Goodfellow 于 2014 年发明,并首先 在论文 生成对抗网络 中进行了描述。\它们由两个不同的模型组成: a generator 和 a discriminator 。生成器的工作是生成看起来像训练图像的 ‘fake’ 图像。鉴别器的工作是查看图像并输出它是真实的训练图像还是来自生成器的假图像。在训练过程中,生成器不断尝试通过生成越来越好的假图像来智胜鉴别器,而鉴别器则致力于成为更好的侦探并正确分类真实图像和假图像。这个游戏的 平衡是当生成器生成完美的 赝品,看起来好像它们直接来自训练数据,并且 鉴别器总是以50%的置信度猜测 生成器输出是真的还是假的。

现在,让我们定义一些在整个教程中使用的符号,从鉴别器开始。令

\(x\)

为表示图像的数据。

\(D(x)\)

是输出(标量)的判别器网络

\(x\)

来自训练数据而不是生成器的概率。在这里,由于我们处理的是图像,

\(D(x)\)

的输入是 CHW 大小为 3x64x64 的图像。直观地说,当

\(x\)

来自训练数据时,

\(D(x)\)

应该为高,而当

时为低\(x\)

来自生成器。

\(D(x)\)

也可以被视为 传统的二元分类器。

对于生成器’s 表示法,令

\(z\)

是从标准正态分布采样 的潜在空间向量。

\(G (z)\)

表示将潜在向量

\(z\)

映射到数据空间的 生成器函数。 目标

\ (G)

是估计训练数据来自 (

\(p_{data}\)

) 的分布,以便生成假样本来自 估计的分布 (

\(p_g\)

)。

因此,

\(D(G(z))\)

是 生成器的输出

\(G\)\ 的概率(标量) n 是真实图像。如 Goodfellow’s paper 中所述,

\(D \)

\(G\)

玩一个极小极大游戏,其中

\(D\)

尝试 最大化概率它正确地分类了真品和假品 (

\(logD(x)\)

),并且

\(G\)

尝试最小化概率

\(D\)

将预测其输出是假的 (

\(log(1-D(G(z)))\)

). 论文中,GAN 损失函数为

[\underset{G}{\text{min}} \underset{D}{\text{max}}V(D,G) = \mathbb{E}{ x\sim p{data}(x)}\big[logD(x)\big] + \mathbb{E}{z\sim p{z}( z)}\big[log(1-D(G(z)))\big]

]

理论上,这个极小极大游戏的解是 \ n \(p_g = p_{data}\)

,判别器随机猜测输入是真的还是假的。然而,GAN 的收敛理论仍在积极研究中,而现实模型并不总是 训练到这一点。

什么是 DCGAN?

DCGAN 是上述 GAN 的直接扩展,不同之处在于它分别在判别器和生成器中显式使用卷积层和卷积转置层。它首先由 Radford 等人描述。论文中的 al.  使用深度卷积生成对抗网络进行无监督表示学习 。判别器由 strided 卷积 层、 batch norm 层,和 LeakyReLU 激活。输入是 3x64x64 输入图像,输出是输入来自真实数据分布的标量概率。生成器由 卷积转置 层、批标准化层和 ReLU 激活。输入是一个潜在向量,

\(z\)

,它是从标准正态分布中提取的,输出是 3x64x64 RGB 图像。跨步卷积转置层允许将潜在向量转换为与图像形状相同的体积。在本文中,作者还提供了一些有关如何设置优化器、如何计算损失函数以及如何初始化模型权重的提示,所有这些都将在接下来的部分中进行解释。

#%matplotlib inline
import argparse
import os
import random
import torch
import torch.nn as nn
import torch.nn.parallel
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 seed 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)
torch.use_deterministic_algorithms(True) # Needed for reproducible results
Random Seed:  999

输入

让’s 定义运行的一些输入:

  • dataroot
  • 数据集文件夹根目录的路径。我们将在下一节中详细讨论数据集。
  • workers
  • 用于使用 DataLoader 加载数据的工作线程数。
  • batch_size
  • 训练中使用的批量大小。 DCGAN 论文\使用的批量大小为 128。
  • image_size
  • 用于训练的图像的空间大小。 此实现默认为 64x64。如果需要其他尺寸,则必须更改 D 和 G 的结构。请参阅 此处 了解更多 详细信息。
  • nc
  • 输入图像中的颜色通道数。对于彩色图像,此值为 3。
  • nz
  • 潜在向量的长度。
  • ngf
  • 与通过生成器 生成的特征图的深度相关。
  • ndf
  • 设置通过鉴别器传播的特征图的深度。
  • num_epochs
  • 要运行的训练周期数。训练时间越长可能会得到更好的结果,但也会花费更长的时间。
  • lr
  • 训练的学习率。正如 DCGAN 论文中所述, 这个数字应该是 0.0002。
  • beta1
  • Adam 优化器的 beta1 超参数。如 论文中所述,该数字应为 0.5。
  • ngpu
  • 可用 GPU 的数量。如果为 0,代码将在 CPU 模式下运行。如果此数字大于 0,它将在该数量的 GPU 上运行。
# Root directory for dataset
dataroot = "data/celeba"

# 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 = 5

# Learning rate for optimizers
lr = 0.0002

# Beta1 hyperparameter for Adam optimizers
beta1 = 0.5

# Number of GPUs available. Use 0 for CPU mode.
ngpu = 1

数据

在本教程中,我们将使用 Celeb-A Faces 数据集 ,可以在链接处下载 站点,或在 Google Drive 中。 数据集将下载为名为 img_align_celeba 的文件.zip .下载后,创建一个名为“celeba”的目录,并将 zip 文件解压到该目录中。然后,将此笔记本的 dataroot 输入设置为 您刚刚创建的 celeba 目录。生成的目录 结构应为:

/path/to/celeba
    -> img_align_celeba
        -> 188242.jpg
        -> 173822.jpg
        -> 284702.jpg
        -> 537394.jpg
           ...

这是重要的一步,因为我们将使用 ImageFolder 数据集类,它要求 数据集根文件夹中有子目录。现在,我们可以创建数据集, 创建数据加载器,设置要运行的设备,最后 可视化一些训练数据。

# We can use an image folder dataset the way we have it setup.
# Create the dataset
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
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")
plt.imshow(np.transpose(vutils.make_grid(real_batch[0].to(device)[:64], padding=2, normalize=True).cpu(),(1,2,0)))

训练图像

<matplotlib.image.AxesImage object at 0x7f52efaa4550>

实现

设置好输入参数并准备好数据集后,我们现在就可以开始实施了。我们将从权重初始化 策略开始,然后详细讨论生成器、判别器、损失函数 和训练循环。

权重初始化

在 DCGAN 论文中,作者指定所有模型权重均应从正态分布随机初始化, mean=0stdev=0.02weights_init 函数将初始化的模型作为输入,并重新初始化所有卷积层、卷积转置层和批归一化层以满足此标准。此函数在初始化后 立即应用于模型。

# 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)

生成器

生成器

\(G\)

旨在将潜在空间向量 (

\(z\)

) 映射到数据-空间。由于我们的数据是图像,因此将

\(z\)

转换为数据空间意味着最终创建一个与训练图像大小相同的 RGB 图像(即  3x64x64)。在实践中,这是通过一系列跨步二维卷积转置层来实现的,每个转置层都与二维批归一化层和 relu 激活配对。生成器的输出通过 tanh 函数返回到输入数据范围

\([-1,1]\)

。值得注意的是,在卷积转置层之后存在批标准化函数,因为这是 DCGAN 论文的关键贡献。这些层有助于训练期间梯度的流动。下面显示了 DCGAN 论文中的生成器图像。

dcgan_generator

请注意,我们在输入部分中设置的输入( nzngfnc )如何影响代码中的生成器架构。 nz\ n 是 z 输入向量的长度, ngf 与通过生成器传播的 特征映射的大小有关, nc 是 通道数输出图像(对于 RGB 图像设置为 3)。下面是生成器的 代码。

# 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
            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)

现在,我们可以实例化生成器并应用 weights_init 函数。查看打印的模型以了解生成器对象的 结构。

# 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=0.02``.
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=True)
    (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=True)
    (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=True)
    (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=True)
    (12): ConvTranspose2d(64, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
    (13): Tanh()
  )
)

鉴别器

如前所述,判别器

\(D\)

是一个二元分类 网络,它将图像作为输入并输出输入图像为真实图像的标量概率 (如反对假货)。这里,

\(D\)

采用 3x64x64 输入图像,通过一系列 Conv2d、 BatchNorm2d 和 LeakyReLU 层对其进行处理,并通过 Sigmoid 激活函数 输出最终概率。如果问题需要,可以使用更多层扩展此架构,但使用跨步卷积、BatchNorm 和 LeakyReLU 具有重要意义。 DCGAN 论文提到,使用跨步卷积 而不是池化来下采样是一种很好的做法,因为它可以让网络学习其 自己的池化函数。批归一化和泄漏 ReLU 函数还促进 健康的梯度流,这对于

\(G\)

\(D\) 的学习过程至关重要 .

鉴别器代码

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)

现在,与生成器一样,我们可以创建鉴别器,应用 weights_init 函数,并打印模型’s 结构。

# 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
# like this: ``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=True)
    (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=True)
    (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=True)
    (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=True)
    (11): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), bias=False)
    (12): Sigmoid()
  )
)

损失函数和优化器

通过

\(D\)

\(G\)

设置,我们可以指定它们如何通过损失函数和优化器学习。我们将使用二元交叉 熵损失 ( BCELoss )\ nfunction 在 PyTorch 中定义为:

[\ell(x, y) = L = {l_1,\dots,l_N}^\top, \quad l_n = - \ \left[ y_n \cdot \log x_n + (1 - y_n) \cdot \log (1 - x_n) \right]

\ ]

请注意此函数如何提供目标函数中两个对数分量的计算(即

\(log(D(x))\)

和 \ n \(log(1-D(G(z)))\)

)。我们可以指定 BCE 方程的哪一部分与

\(y\)

输入一起使用。这是在即将到来的训练循环中完成的,但重要的是要了解我们如何通过更改

\(y\)

(即 GT 标签)。

接下来,我们将真实标签定义为 1,假标签定义为 0。这些 标签将在计算

\(D\)

\ 的损失时使用\(G\)

,这也是原始 GAN 论文中使用的约定。最后,我们设置了两个单独的优化器,一个用于

\(D\)

,一个用于

\(G\)

。正如 DCGAN 论文中所指定的,两者都是 Adam 优化器,学习率为 0.0002,Beta1 = 0.5。为了跟踪生成器 xe2x80x99 的学习进度,我们将生成一批固定的潜在向量,这些向量是从高斯分布(即 xc2xa0fixed_noise)中提取的。在训练循环中,我们会定期将这个固定的噪声输入

\(G\)

,并且在迭代过程中,我们将看到 图像是由噪声形成的。

# Initialize the ``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))

训练

最后,现在我们已经定义了 GAN 框架的所有部分, 我们可以训练它了。请注意,训练 GAN 在某种程度上是一种艺术 形式,因为不正确的超参数设置会导致模式崩溃,而 对问题所在几乎没有解释。在这里,我们将密切关注 Goodfellow’s论文 中的算法1 ,\在遵守ganhacks中显示的一些最佳实践的同时。 也就是说,我们将“构建不同的小批量,用于真实和fake” 图像,并调整 G’s 目标函数以最大化

\(log(D(G(z)))\)

。培训分为两个主要部分。第 1 部分 更新鉴别器,第 2 部分更新生成器。

第 1 部分 - 训练鉴别器

回想一下,训练鉴别器的目标是最大化将给定输入正确分类为真或假的概率。按照 Goodfellow 的说法,我们希望通过升序随机梯度 xe2x80x9d 来更新判别器。实际上,我们希望最大化

\(log(D(x)) + log(1-D(G(z)))\)

。由于来自 ganhacks 的单独小批量建议,我们将分两步进行计算。首先,我们从训练集中构建一批真实样本,向前传递

\(D\)

,计算损失(

\(log(D (x))\)

),然后 计算向后传递中的梯度。其次,我们将使用当前生成器构造 一批假样本,将这 批样本前向传递

\(D\)

,计算损失 (

\(log (1-D(G(z)))\)

), 和 累积 通过向后传递的梯度。现在,利用从全真批次和全假批次累积的 梯度,我们 调用判别器’s 优化器的一个步骤。

第 2 部分 - 训练生成器

正如原始论文中所述,我们希望通过 最小化

\(log(1-D(G(z)))\)

来训练生成器,以努力生成更好的赝品。 如前所述,Goodfellow 表明这不能提供足够 的梯度,尤其是在学习过程的早期。作为解决方案,我们希望最大化

\(log(D(G(z)))\)

。在代码中,我们通过以下方式完成 :使用判别器对第 1 部分的生成器输出进行分类,计算 G\xe2\x80\x99s 损失 使用真实标签作为 GT ,计算 G\xe2\x80\ x99s 向后传递梯度,最后使用优化器步骤更新 G\xe2\x80\x99s 参数 。使用真实标签作为损失函数的 GT 标签似乎违反直觉,但这允许我们使用

\(log(x)\)

部分 BCELoss (而不是

\(log(1-x)\)

部分)这正是我们想要的。

最后,我们将做一些统计报告,在每个时期结束时,我们将通过生成器推送固定的噪声批次,以直观地跟踪 G’s 训练的进度。报告的训练统计数据 为:

  • Loss_D
  • 判别器损失计算为 所有真实批次和所有假批次的损失总和 (

\(log(D(x)) + log(1 - D (G(z)))\)

). * Loss_G - 发电机损失计算为

\(log(D(G(z)) )\) * D(x) - 所有真实批次的判别器的平均输出(跨批次)。这应该开始接近 1,然后理论上当 G 变得更好时收敛到 0.5。想想为什么会这样。 * D(G(z)) - 所有假批次的平均鉴别器输出。 第一个数字在 D 更新之前,第二个数字在 D 之后 已更新。这些数字应该从 0 附近开始,并随着 G 变得更好而收敛到 0.5。想一想这是为什么。

注意: 此步骤可能需要一段时间,具体取决于您运行的轮数 以及是否从数据集中删除了一些数据。

# 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, dtype=torch.float, 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, accumulated (summed) with previous gradients
        errD_fake.backward()
        D_G_z1 = output.mean().item()
        # Compute error of D as sum over the fake and the real 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())

        # 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
Starting Training Loop...
[0/5][0/1583]   Loss_D: 1.4640  Loss_G: 6.9360  D(x): 0.7143    D(G(z)): 0.5877 / 0.0017
[0/5][50/1583]  Loss_D: 0.0174  Loss_G: 23.7368 D(x): 0.9881    D(G(z)): 0.0000 / 0.0000
[0/5][100/1583] Loss_D: 0.5983  Loss_G: 9.9471  D(x): 0.9715    D(G(z)): 0.3122 / 0.0003
[0/5][150/1583] Loss_D: 0.4940  Loss_G: 5.6772  D(x): 0.7028    D(G(z)): 0.0241 / 0.0091
[0/5][200/1583] Loss_D: 0.5931  Loss_G: 7.1186  D(x): 0.9423    D(G(z)): 0.3016 / 0.0018
[0/5][250/1583] Loss_D: 0.3846  Loss_G: 3.2697  D(x): 0.7663    D(G(z)): 0.0573 / 0.0739
[0/5][300/1583] Loss_D: 1.3306  Loss_G: 8.3204  D(x): 0.8768    D(G(z)): 0.6353 / 0.0009
[0/5][350/1583] Loss_D: 0.6451  Loss_G: 6.0499  D(x): 0.9025    D(G(z)): 0.3673 / 0.0060
[0/5][400/1583] Loss_D: 0.4211  Loss_G: 3.7316  D(x): 0.8407    D(G(z)): 0.1586 / 0.0392
[0/5][450/1583] Loss_D: 0.6569  Loss_G: 2.4818  D(x): 0.6437    D(G(z)): 0.0858 / 0.1129
[0/5][500/1583] Loss_D: 1.2208  Loss_G: 2.9943  D(x): 0.4179    D(G(z)): 0.0109 / 0.1133
[0/5][550/1583] Loss_D: 0.3400  Loss_G: 4.7669  D(x): 0.9135    D(G(z)): 0.1922 / 0.0145
[0/5][600/1583] Loss_D: 0.5756  Loss_G: 4.8500  D(x): 0.9189    D(G(z)): 0.3193 / 0.0187
[0/5][650/1583] Loss_D: 0.2470  Loss_G: 4.1606  D(x): 0.9460    D(G(z)): 0.1545 / 0.0250
[0/5][700/1583] Loss_D: 0.3887  Loss_G: 4.1884  D(x): 0.8518    D(G(z)): 0.1562 / 0.0297
[0/5][750/1583] Loss_D: 0.5353  Loss_G: 4.1742  D(x): 0.8034    D(G(z)): 0.1958 / 0.0302
[0/5][800/1583] Loss_D: 0.3213  Loss_G: 5.8919  D(x): 0.9076    D(G(z)): 0.1572 / 0.0065
[0/5][850/1583] Loss_D: 0.8850  Loss_G: 7.4333  D(x): 0.9258    D(G(z)): 0.4449 / 0.0017
[0/5][900/1583] Loss_D: 1.2624  Loss_G: 10.0392 D(x): 0.9896    D(G(z)): 0.6361 / 0.0002
[0/5][950/1583] Loss_D: 0.8802  Loss_G: 6.9221  D(x): 0.5527    D(G(z)): 0.0039 / 0.0045
[0/5][1000/1583]        Loss_D: 0.5799  Loss_G: 3.1800  D(x): 0.7062    D(G(z)): 0.0762 / 0.0884
[0/5][1050/1583]        Loss_D: 0.9647  Loss_G: 6.6894  D(x): 0.9429    D(G(z)): 0.5270 / 0.0035
[0/5][1100/1583]        Loss_D: 0.5624  Loss_G: 3.6715  D(x): 0.7944    D(G(z)): 0.2069 / 0.0445
[0/5][1150/1583]        Loss_D: 0.6205  Loss_G: 4.8995  D(x): 0.8634    D(G(z)): 0.3046 / 0.0169
[0/5][1200/1583]        Loss_D: 0.2569  Loss_G: 4.2945  D(x): 0.9455    D(G(z)): 0.1528 / 0.0255
[0/5][1250/1583]        Loss_D: 0.4921  Loss_G: 3.2500  D(x): 0.8152    D(G(z)): 0.1892 / 0.0753
[0/5][1300/1583]        Loss_D: 0.4068  Loss_G: 3.7702  D(x): 0.8153    D(G(z)): 0.1335 / 0.0472
[0/5][1350/1583]        Loss_D: 1.1704  Loss_G: 7.3408  D(x): 0.9443    D(G(z)): 0.5863 / 0.0022
[0/5][1400/1583]        Loss_D: 0.6111  Loss_G: 2.2676  D(x): 0.6714    D(G(z)): 0.0793 / 0.1510
[0/5][1450/1583]        Loss_D: 0.7817  Loss_G: 4.0744  D(x): 0.7915    D(G(z)): 0.3573 / 0.0242
[0/5][1500/1583]        Loss_D: 0.7177  Loss_G: 1.9253  D(x): 0.5770    D(G(z)): 0.0257 / 0.1909
[0/5][1550/1583]        Loss_D: 0.4518  Loss_G: 2.8314  D(x): 0.7991    D(G(z)): 0.1479 / 0.0885
[1/5][0/1583]   Loss_D: 0.4267  Loss_G: 4.5150  D(x): 0.8976    D(G(z)): 0.2401 / 0.0196
[1/5][50/1583]  Loss_D: 0.5106  Loss_G: 2.7800  D(x): 0.7073    D(G(z)): 0.0663 / 0.0932
[1/5][100/1583] Loss_D: 0.6300  Loss_G: 1.8648  D(x): 0.6557    D(G(z)): 0.0756 / 0.2118
[1/5][150/1583] Loss_D: 1.1727  Loss_G: 5.1536  D(x): 0.8397    D(G(z)): 0.5261 / 0.0125
[1/5][200/1583] Loss_D: 0.4675  Loss_G: 2.9615  D(x): 0.7645    D(G(z)): 0.1400 / 0.0780
[1/5][250/1583] Loss_D: 0.7938  Loss_G: 3.1614  D(x): 0.6958    D(G(z)): 0.2248 / 0.0678
[1/5][300/1583] Loss_D: 0.9869  Loss_G: 5.9243  D(x): 0.9619    D(G(z)): 0.5349 / 0.0063
[1/5][350/1583] Loss_D: 0.5178  Loss_G: 3.0236  D(x): 0.7795    D(G(z)): 0.1769 / 0.0700
[1/5][400/1583] Loss_D: 1.4509  Loss_G: 2.7187  D(x): 0.3278    D(G(z)): 0.0133 / 0.1273
[1/5][450/1583] Loss_D: 0.5530  Loss_G: 4.8110  D(x): 0.9151    D(G(z)): 0.3237 / 0.0160
[1/5][500/1583] Loss_D: 0.4621  Loss_G: 4.1158  D(x): 0.8720    D(G(z)): 0.2278 / 0.0293
[1/5][550/1583] Loss_D: 0.4987  Loss_G: 4.0199  D(x): 0.8533    D(G(z)): 0.2367 / 0.0287
[1/5][600/1583] Loss_D: 1.0630  Loss_G: 4.6502  D(x): 0.9145    D(G(z)): 0.5018 / 0.0218
[1/5][650/1583] Loss_D: 0.6081  Loss_G: 4.3172  D(x): 0.8670    D(G(z)): 0.3312 / 0.0221
[1/5][700/1583] Loss_D: 0.4703  Loss_G: 2.4900  D(x): 0.7538    D(G(z)): 0.1245 / 0.1188
[1/5][750/1583] Loss_D: 0.4827  Loss_G: 2.2941  D(x): 0.7372    D(G(z)): 0.1105 / 0.1300
[1/5][800/1583] Loss_D: 0.4013  Loss_G: 3.8850  D(x): 0.8895    D(G(z)): 0.2179 / 0.0324
[1/5][850/1583] Loss_D: 0.7245  Loss_G: 1.9088  D(x): 0.6100    D(G(z)): 0.0950 / 0.1898
[1/5][900/1583] Loss_D: 0.8372  Loss_G: 1.2346  D(x): 0.5232    D(G(z)): 0.0332 / 0.3633
[1/5][950/1583] Loss_D: 0.5561  Loss_G: 3.2048  D(x): 0.7660    D(G(z)): 0.2035 / 0.0594
[1/5][1000/1583]        Loss_D: 0.6859  Loss_G: 1.6347  D(x): 0.5764    D(G(z)): 0.0435 / 0.2540
[1/5][1050/1583]        Loss_D: 0.6785  Loss_G: 4.3244  D(x): 0.9066    D(G(z)): 0.3835 / 0.0203
[1/5][1100/1583]        Loss_D: 0.4835  Loss_G: 2.4080  D(x): 0.7428    D(G(z)): 0.1073 / 0.1147
[1/5][1150/1583]        Loss_D: 0.5507  Loss_G: 2.5400  D(x): 0.7857    D(G(z)): 0.2182 / 0.1092
[1/5][1200/1583]        Loss_D: 0.6054  Loss_G: 3.4802  D(x): 0.8263    D(G(z)): 0.2934 / 0.0441
[1/5][1250/1583]        Loss_D: 0.4788  Loss_G: 2.3533  D(x): 0.7872    D(G(z)): 0.1698 / 0.1327
[1/5][1300/1583]        Loss_D: 0.5314  Loss_G: 2.7018  D(x): 0.8273    D(G(z)): 0.2423 / 0.0921
[1/5][1350/1583]        Loss_D: 0.8579  Loss_G: 4.6214  D(x): 0.9623    D(G(z)): 0.5089 / 0.0159
[1/5][1400/1583]        Loss_D: 0.4919  Loss_G: 2.7656  D(x): 0.8122    D(G(z)): 0.2147 / 0.0864
[1/5][1450/1583]        Loss_D: 0.4461  Loss_G: 3.0576  D(x): 0.8042    D(G(z)): 0.1798 / 0.0619
[1/5][1500/1583]        Loss_D: 0.7182  Loss_G: 3.7270  D(x): 0.8553    D(G(z)): 0.3713 / 0.0382
[1/5][1550/1583]        Loss_D: 0.6378  Loss_G: 3.7489  D(x): 0.8757    D(G(z)): 0.3523 / 0.0317
[2/5][0/1583]   Loss_D: 0.3965  Loss_G: 2.6262  D(x): 0.7941    D(G(z)): 0.1247 / 0.0963
[2/5][50/1583]  Loss_D: 0.6504  Loss_G: 3.9890  D(x): 0.9267    D(G(z)): 0.3865 / 0.0275
[2/5][100/1583] Loss_D: 0.6523  Loss_G: 3.8724  D(x): 0.8707    D(G(z)): 0.3613 / 0.0299
[2/5][150/1583] Loss_D: 0.7685  Loss_G: 3.9059  D(x): 0.9361    D(G(z)): 0.4534 / 0.0278
[2/5][200/1583] Loss_D: 0.6587  Loss_G: 1.9218  D(x): 0.6469    D(G(z)): 0.1291 / 0.1888
[2/5][250/1583] Loss_D: 0.6971  Loss_G: 2.2256  D(x): 0.6208    D(G(z)): 0.1226 / 0.1465
[2/5][300/1583] Loss_D: 0.5797  Loss_G: 2.4846  D(x): 0.7762    D(G(z)): 0.2434 / 0.1098
[2/5][350/1583] Loss_D: 0.4674  Loss_G: 1.8800  D(x): 0.8045    D(G(z)): 0.1903 / 0.1877
[2/5][400/1583] Loss_D: 0.6462  Loss_G: 1.9510  D(x): 0.7018    D(G(z)): 0.1935 / 0.1792
[2/5][450/1583] Loss_D: 0.9817  Loss_G: 4.2519  D(x): 0.9421    D(G(z)): 0.5381 / 0.0233
[2/5][500/1583] Loss_D: 0.7721  Loss_G: 1.0928  D(x): 0.5402    D(G(z)): 0.0316 / 0.3927
[2/5][550/1583] Loss_D: 0.6037  Loss_G: 2.6914  D(x): 0.7719    D(G(z)): 0.2504 / 0.0896
[2/5][600/1583] Loss_D: 1.4213  Loss_G: 5.4727  D(x): 0.9408    D(G(z)): 0.6792 / 0.0064
[2/5][650/1583] Loss_D: 0.7246  Loss_G: 1.7030  D(x): 0.6716    D(G(z)): 0.2184 / 0.2246
[2/5][700/1583] Loss_D: 0.6642  Loss_G: 3.3809  D(x): 0.8554    D(G(z)): 0.3438 / 0.0591
[2/5][750/1583] Loss_D: 0.6649  Loss_G: 2.0197  D(x): 0.7169    D(G(z)): 0.2333 / 0.1565
[2/5][800/1583] Loss_D: 0.4594  Loss_G: 2.6623  D(x): 0.8150    D(G(z)): 0.1930 / 0.0944
[2/5][850/1583] Loss_D: 1.1957  Loss_G: 3.1871  D(x): 0.7790    D(G(z)): 0.5576 / 0.0568
[2/5][900/1583] Loss_D: 0.6657  Loss_G: 1.5311  D(x): 0.7092    D(G(z)): 0.2122 / 0.2558
[2/5][950/1583] Loss_D: 0.6795  Loss_G: 1.4149  D(x): 0.6134    D(G(z)): 0.1195 / 0.2937
[2/5][1000/1583]        Loss_D: 0.5995  Loss_G: 2.1744  D(x): 0.7325    D(G(z)): 0.2054 / 0.1484
[2/5][1050/1583]        Loss_D: 0.6706  Loss_G: 1.6705  D(x): 0.6425    D(G(z)): 0.1414 / 0.2310
[2/5][1100/1583]        Loss_D: 1.2840  Loss_G: 4.4620  D(x): 0.9736    D(G(z)): 0.6601 / 0.0225
[2/5][1150/1583]        Loss_D: 0.7568  Loss_G: 3.1238  D(x): 0.8153    D(G(z)): 0.3717 / 0.0581
[2/5][1200/1583]        Loss_D: 0.6331  Loss_G: 1.9048  D(x): 0.6799    D(G(z)): 0.1604 / 0.1814
[2/5][1250/1583]        Loss_D: 0.5802  Loss_G: 2.4358  D(x): 0.7561    D(G(z)): 0.2194 / 0.1095
[2/5][1300/1583]        Loss_D: 0.9613  Loss_G: 2.3290  D(x): 0.7463    D(G(z)): 0.3952 / 0.1349
[2/5][1350/1583]        Loss_D: 0.5367  Loss_G: 1.7398  D(x): 0.7580    D(G(z)): 0.1898 / 0.2216
[2/5][1400/1583]        Loss_D: 0.7762  Loss_G: 3.6246  D(x): 0.9006    D(G(z)): 0.4378 / 0.0364
[2/5][1450/1583]        Loss_D: 0.7183  Loss_G: 4.0442  D(x): 0.8602    D(G(z)): 0.3857 / 0.0254
[2/5][1500/1583]        Loss_D: 0.5416  Loss_G: 2.0642  D(x): 0.7393    D(G(z)): 0.1758 / 0.1532
[2/5][1550/1583]        Loss_D: 0.5295  Loss_G: 1.7855  D(x): 0.6768    D(G(z)): 0.0886 / 0.2154
[3/5][0/1583]   Loss_D: 0.8635  Loss_G: 1.7508  D(x): 0.4918    D(G(z)): 0.0280 / 0.2154
[3/5][50/1583]  Loss_D: 0.8697  Loss_G: 0.7859  D(x): 0.5216    D(G(z)): 0.1124 / 0.4941
[3/5][100/1583] Loss_D: 0.8607  Loss_G: 4.5255  D(x): 0.9197    D(G(z)): 0.4973 / 0.0157
[3/5][150/1583] Loss_D: 0.4805  Loss_G: 2.3071  D(x): 0.7743    D(G(z)): 0.1742 / 0.1291
[3/5][200/1583] Loss_D: 0.4925  Loss_G: 2.6018  D(x): 0.7907    D(G(z)): 0.1970 / 0.0948
[3/5][250/1583] Loss_D: 0.7870  Loss_G: 3.3529  D(x): 0.8408    D(G(z)): 0.4050 / 0.0469
[3/5][300/1583] Loss_D: 0.5479  Loss_G: 1.7376  D(x): 0.7216    D(G(z)): 0.1592 / 0.2227
[3/5][350/1583] Loss_D: 0.8117  Loss_G: 3.4145  D(x): 0.9076    D(G(z)): 0.4685 / 0.0437
[3/5][400/1583] Loss_D: 0.4210  Loss_G: 2.3880  D(x): 0.7543    D(G(z)): 0.1047 / 0.1217
[3/5][450/1583] Loss_D: 1.5745  Loss_G: 0.2366  D(x): 0.2747    D(G(z)): 0.0361 / 0.8096
[3/5][500/1583] Loss_D: 0.7196  Loss_G: 2.1319  D(x): 0.7332    D(G(z)): 0.2935 / 0.1403
[3/5][550/1583] Loss_D: 0.5697  Loss_G: 2.6649  D(x): 0.8816    D(G(z)): 0.3210 / 0.0917
[3/5][600/1583] Loss_D: 0.7779  Loss_G: 1.2727  D(x): 0.5540    D(G(z)): 0.0855 / 0.3412
[3/5][650/1583] Loss_D: 0.4090  Loss_G: 2.6893  D(x): 0.8334    D(G(z)): 0.1835 / 0.0855
[3/5][700/1583] Loss_D: 0.8108  Loss_G: 3.8991  D(x): 0.9241    D(G(z)): 0.4716 / 0.0281
[3/5][750/1583] Loss_D: 0.9907  Loss_G: 4.7885  D(x): 0.9111    D(G(z)): 0.5402 / 0.0123
[3/5][800/1583] Loss_D: 0.4725  Loss_G: 2.3347  D(x): 0.7577    D(G(z)): 0.1400 / 0.1222
[3/5][850/1583] Loss_D: 1.5580  Loss_G: 4.9586  D(x): 0.8954    D(G(z)): 0.7085 / 0.0132
[3/5][900/1583] Loss_D: 0.5785  Loss_G: 1.6395  D(x): 0.6581    D(G(z)): 0.1003 / 0.2411
[3/5][950/1583] Loss_D: 0.6592  Loss_G: 1.0890  D(x): 0.5893    D(G(z)): 0.0451 / 0.3809
[3/5][1000/1583]        Loss_D: 0.7280  Loss_G: 3.5368  D(x): 0.8898    D(G(z)): 0.4176 / 0.0409
[3/5][1050/1583]        Loss_D: 0.7088  Loss_G: 3.4301  D(x): 0.8558    D(G(z)): 0.3845 / 0.0457
[3/5][1100/1583]        Loss_D: 0.5651  Loss_G: 2.1150  D(x): 0.7602    D(G(z)): 0.2127 / 0.1532
[3/5][1150/1583]        Loss_D: 0.5412  Loss_G: 1.7790  D(x): 0.6602    D(G(z)): 0.0801 / 0.2088
[3/5][1200/1583]        Loss_D: 1.2277  Loss_G: 1.1464  D(x): 0.4864    D(G(z)): 0.2915 / 0.3665
[3/5][1250/1583]        Loss_D: 0.7148  Loss_G: 1.3957  D(x): 0.5948    D(G(z)): 0.1076 / 0.2876
[3/5][1300/1583]        Loss_D: 1.0675  Loss_G: 1.3018  D(x): 0.4056    D(G(z)): 0.0310 / 0.3355
[3/5][1350/1583]        Loss_D: 0.8064  Loss_G: 0.7482  D(x): 0.5846    D(G(z)): 0.1453 / 0.5147
[3/5][1400/1583]        Loss_D: 0.6032  Loss_G: 3.0601  D(x): 0.8474    D(G(z)): 0.3189 / 0.0590
[3/5][1450/1583]        Loss_D: 0.5329  Loss_G: 2.8172  D(x): 0.8234    D(G(z)): 0.2567 / 0.0795
[3/5][1500/1583]        Loss_D: 0.9292  Loss_G: 3.5544  D(x): 0.8686    D(G(z)): 0.4887 / 0.0410
[3/5][1550/1583]        Loss_D: 0.5929  Loss_G: 2.9118  D(x): 0.8614    D(G(z)): 0.3239 / 0.0702
[4/5][0/1583]   Loss_D: 0.5564  Loss_G: 2.7516  D(x): 0.8716    D(G(z)): 0.3145 / 0.0799
[4/5][50/1583]  Loss_D: 1.0485  Loss_G: 0.6751  D(x): 0.4332    D(G(z)): 0.0675 / 0.5568
[4/5][100/1583] Loss_D: 0.6753  Loss_G: 1.4046  D(x): 0.6028    D(G(z)): 0.0882 / 0.2901
[4/5][150/1583] Loss_D: 0.5946  Loss_G: 1.7618  D(x): 0.6862    D(G(z)): 0.1488 / 0.2016
[4/5][200/1583] Loss_D: 0.4866  Loss_G: 2.2638  D(x): 0.7628    D(G(z)): 0.1633 / 0.1321
[4/5][250/1583] Loss_D: 0.7493  Loss_G: 1.0999  D(x): 0.5541    D(G(z)): 0.0659 / 0.3787
[4/5][300/1583] Loss_D: 1.0886  Loss_G: 4.6532  D(x): 0.9370    D(G(z)): 0.5811 / 0.0149
[4/5][350/1583] Loss_D: 0.6106  Loss_G: 1.9212  D(x): 0.6594    D(G(z)): 0.1322 / 0.1825
[4/5][400/1583] Loss_D: 0.5226  Loss_G: 2.9611  D(x): 0.8178    D(G(z)): 0.2378 / 0.0731
[4/5][450/1583] Loss_D: 1.0068  Loss_G: 1.3267  D(x): 0.4310    D(G(z)): 0.0375 / 0.3179
[4/5][500/1583] Loss_D: 3.1088  Loss_G: 0.1269  D(x): 0.0706    D(G(z)): 0.0061 / 0.8897
[4/5][550/1583] Loss_D: 1.7889  Loss_G: 0.4800  D(x): 0.2175    D(G(z)): 0.0143 / 0.6479
[4/5][600/1583] Loss_D: 0.6732  Loss_G: 3.5685  D(x): 0.8775    D(G(z)): 0.3879 / 0.0362
[4/5][650/1583] Loss_D: 0.5169  Loss_G: 2.1943  D(x): 0.7222    D(G(z)): 0.1349 / 0.1416
[4/5][700/1583] Loss_D: 0.4567  Loss_G: 2.4442  D(x): 0.7666    D(G(z)): 0.1410 / 0.1204
[4/5][750/1583] Loss_D: 0.5972  Loss_G: 2.2992  D(x): 0.6286    D(G(z)): 0.0670 / 0.1283
[4/5][800/1583] Loss_D: 0.5461  Loss_G: 1.9777  D(x): 0.7013    D(G(z)): 0.1318 / 0.1795
[4/5][850/1583] Loss_D: 0.6317  Loss_G: 2.2345  D(x): 0.6962    D(G(z)): 0.1854 / 0.1385
[4/5][900/1583] Loss_D: 0.6034  Loss_G: 3.2300  D(x): 0.8781    D(G(z)): 0.3448 / 0.0517
[4/5][950/1583] Loss_D: 0.6371  Loss_G: 2.7755  D(x): 0.8595    D(G(z)): 0.3357 / 0.0826
[4/5][1000/1583]        Loss_D: 0.6077  Loss_G: 3.3958  D(x): 0.9026    D(G(z)): 0.3604 / 0.0458
[4/5][1050/1583]        Loss_D: 0.5057  Loss_G: 3.2545  D(x): 0.8705    D(G(z)): 0.2691 / 0.0546
[4/5][1100/1583]        Loss_D: 0.4552  Loss_G: 2.0632  D(x): 0.7887    D(G(z)): 0.1704 / 0.1524
[4/5][1150/1583]        Loss_D: 0.9933  Loss_G: 1.0264  D(x): 0.4507    D(G(z)): 0.0636 / 0.4182
[4/5][1200/1583]        Loss_D: 0.5037  Loss_G: 1.9940  D(x): 0.6967    D(G(z)): 0.0959 / 0.1698
[4/5][1250/1583]        Loss_D: 0.4760  Loss_G: 2.5973  D(x): 0.8192    D(G(z)): 0.2164 / 0.0945
[4/5][1300/1583]        Loss_D: 1.0137  Loss_G: 3.8782  D(x): 0.9330    D(G(z)): 0.5405 / 0.0309
[4/5][1350/1583]        Loss_D: 0.9084  Loss_G: 3.1406  D(x): 0.7540    D(G(z)): 0.3980 / 0.0648
[4/5][1400/1583]        Loss_D: 0.6724  Loss_G: 4.1269  D(x): 0.9536    D(G(z)): 0.4234 / 0.0236
[4/5][1450/1583]        Loss_D: 0.6452  Loss_G: 3.5163  D(x): 0.8730    D(G(z)): 0.3555 / 0.0412
[4/5][1500/1583]        Loss_D: 0.8843  Loss_G: 1.4950  D(x): 0.5314    D(G(z)): 0.1035 / 0.2835
[4/5][1550/1583]        Loss_D: 2.3345  Loss_G: 1.0675  D(x): 0.1448    D(G(z)): 0.0228 / 0.4177

结果

最后,让我们看看我们做得如何。在这里,我们将查看三个不同的结果。首先,我们将看到 D 和 G’s 损失在训练期间如何变化。其次,我们将可视化每个时期固定 _noise batch 上的 G’s 输出。第三,我们将查看一批真实数据 以及来自 G 的一批假数据。

损失与训练迭代

下面是 D & G’s 损失与训练迭代的关系图。

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()

训练期间生成器和鉴别器损失

G’s 进展的可视化

请记住我们如何在每个训练周期之后将生成器’s 的输出保存在固定_noise 批次 上。现在,我们可以用动画可视化 G 的训练进度。按播放按钮开始 动画。

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())

dcgan 脸部教程

function isInternetExplorer() { ua = navigator.userAgent; / MSIE 用于检测旧浏览器,Trident 用于检测新浏览器/ return ua.indexOf("MSIE ") > - 1 || ua.indexOf("Trident/") > -1; }

/ 定义动画类 / function Animation(frames, img_id, slider_id, Interval ,loop_select_id){ this.img_id = img_id; this.slider_id = slider_id; this.loop_select_id =循环_select_id; this.interval = 间隔; this.current_frame = 0; this.direction = 0; this.timer = null; this.frames = new Array(frames.length);

for (var i=0; i<frames.length; i++) { this.frames[i] = new Image(); this.frames[i ].src =frames[i]; } var slider = document.getElementById(this.slider_id); slider.max = this.frames.length - 1; if (isInternetExplorer() ) { //从 oninput 切换到 onchange,因为 IE <= 11 不符合 //W3C 规范。它忽略 oninput,而 onchange 的行为 //与 oninput 类似。相比之下,Microsoft Edge 行为正确。 slider.setAttribute('onchange', slider.getAttribute('oninput')); slider.setAttribute('oninput', null); } this.set_frame(this.current_frame); }

Animation.prototype.get_loop_state = function(){ var button_group = document[ this.loop_select_id].state; for (var i = 0; i < 按钮_group.length; i++) { var button = 按钮_group[i]; if (button.checked) { return button.value; } } return undefined; }

Animation.prototype.set_frame = function(frame){ this.current _frame =frame; document.getElementById(this.img_id).src = this.frames[this.current_frame].src; document.getElementById(this.slider_id) ).value = this.current_frame; }

Animation.prototype.next_frame = function() { this.set_frame(Math.min(this.frames. length - 1, this.current_frame + 1)); }

Animation.prototype.previous_frame = function() { this.set_frame(Math.max( 0, this.current_frame - 1)); }

Animation.prototype.first_frame = function() { this.set_frame(0); }

Animation.prototype.last_frame = function() { this.set_frame(this.frames.length - 1); }

Animation.prototype.slower = function() { this.interval /= 0.7; if(this.direction > 0){this.play_animation();} else if(this.direction < 0){this. verse_animation();} }

Animation.prototype.faster = function() { this.interval *= 0.7; if(this.direction > 0){this.play_animation();} else if(this.direction < 0){this.reverse_animation();} }

Animation.prototype.anim_step_forward = function() { this.current_frame += 1; if(this.current_frame < this.frames.length){ this.set_frame(this.current\ _frame); }else{ var Loop_state = this.get_loop_state(); if(loop_state == "loop"){ this.first\ _frame(); }else if(loop_state == "reflect"){ this.last_frame(); this.reverse_animation(); }else{ this.pause_animation(); this.last_frame(); } } }

Animation.prototype.anim_step_reverse = function() { this.current_frame -= 1; if(this.current_frame >= 0){ this.set_frame(this.current_frame); }else{ var Loop_state = this.get_loop_state(); if(loop_state == "loop"){ this.last_frame(); }else if(loop_state == "reflect"){ this.first_frame(); this.play_animation(); }else{ this.pause_animation() ; this.first_frame(); } } }

Animation.prototype.pause_animation = function() { this.direction = 0; if (this.timer){ clearInterval(this.timer); this.timer = null; } }

Animation.prototype.play_animation = function() {\ n this.pause_animation(); this.direction = 1; var t = this; if (!this.timer) this.timer = setInterval(function() { t.anim\ _step_forward(); }, this.interval); }

Animation.prototype.reverse_animation = function() { this.pause_animation(); this.direction = -1; var t = this; if (!this.timer) this.timer = setInterval(function() { t.anim_step_reverse();\ n }, this.interval); }

.animation { 显示:内联块; 文本对齐:中心; } input[type=range].anim-slider { 宽度: 374px; 左边距: 自动; 右边距: 自动; } .anim-buttons { 边距: 8px 0px; } .anim-buttons 按钮{ 内边距: 0; 宽度: 36px; } .anim-state 标签 { 右边距: 8px; } .anim-state 输入 { margin: 0;\ n 垂直对齐: 中间; }

一次

循环

反映

/ Instantiate the Animation class. / / The IDs given should match those used in the template above. / (function() { var img_id = "_anim_imgf3d9a3e6c6dc4159bf77d1add7713e04"; var slider_id = "_anim_sliderf3d9a3e6c6dc4159bf77d1add7713e04"; var loop_select_id = "_anim_loop_selectf3d9a3e6c6dc4159bf77d1add7713e04"; var frames = new Array(17);

frames[0] = "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAyAAAAMgCAYAAADbcAZoAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\ bGliIHZlcnNpb24zLjguMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/SrBM8AAAACXBIWXMAAA9h\ AAAPYQGoP6dpAAEAAElEQVR4nOz92Y9lSWKYeX529u3ui+8e7rFmZOReWZm1kSySokRKLVISGlrQ\ LTQGmIcezLzO82D+jAHmpQX19EiCpFGLEik2WUWyWGtm5Z6RsXuE7379rufesy82D1ms0gDzSLkK\ Tfs9BwIXhmvX7Tt2jh0hpZQoiqIoiqIoiqJcAe2/9gdQFEVRFEVRFOWvDxUgiqIoiqIoiqJcGRUg\ iqIoiqIoiqJcGRUgiqIoiqIoiqJcGRUgiqIoiqIoiqJcGRUgiqIoiqIoiqJcGRUgiqIoiqIoiqJc\ GRUgiqIoiqIoiqJcGRUgiqIoiqIoiqJcGRUgiqIoiqIoiqJcGRUgiqIoiqIoiqJcGRUgiqIoiqIo\ iqJcGRUgiqIoiqIoiqJcGRUgiqIoiqIoiqJcGRUgiqIoiqIoiqJcGRUgiqIoiqIoiqJcGRUgiqIo\ iqIoiqJcGRUgiqIoiqIoiqJcGRUgiqIoiqIoiqJcGRUgiqIoiqIoiqJcGRUgiqIoiqIoiqJcGRUg\ iqIoiqIoiqJcGRUgiqIoiqIoiqJcGRUgiqIoiqIoiqJcGRUgiqIoiqIoiqJcGRUgiqIoiqIoiqJc\ GRUgiqIoiqIoiqJcGRUgiqIoiqIoiqJcGRUgiqIoiqIoiqJcGRUgiqIoiqIoiqJcGRUgiqIoiqIo\ iqJcGRUgiqIoiqIoiqJcGRUgiqIoiqIoiqJcGRUgiqIoiqIoiqJcGRUgiqIoiqIoiqJcGRUgiqIo\ 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