1.项目介绍

 通过用minist数据集进行训练，得到一个GAN模型，可以生成与minist数据集类似的图片。
GAN是一种生成模型，它的目的是通过学习真实数据的分布来生成新的数据。GAN由两个网络组成，一个是生成器（Generator），一个是判别器（Discriminator）。生成器的任务是从随机噪声中生成类似于真实数据的样本，判别器的任务是判断给定的样本是真实的还是生成的。GAN的训练过程可以看作是一种对抗博弈，生成器和判别器互相竞争，不断提高自己的能力，最终达到生成器生成的样本和真实数据分布一致，判别器无法区分真假的状态。通过GAN我们可以生成足以以假乱真的图像，GAN被广泛的应用在图像生成，语音生成等场景中。例如经典的换脸应用DeepFakes背后的技术便是GAN.
生成对抗网络的组成：
   生成器网络 、判别器网络
目标：
   总体目标:
     生成模型，根据已有的图片，生成与已有的图片类似的图片
   训练目标：
    判别器
     能够正确的识别真的图片
     能够正确的识别假的图片
    生成器
     能够生成的能被判别器判断为真的图片

本文是在google drive中部署的，部署过程参考博客：colab部署过程

2相关网站

参考的网站：github pytorch代码
所有的代码文件：提取码：f3vq

3具体的代码及结果

导入工具包

import os
import torch
import torchvision
import torch.nn as nn
from torchvision import transforms
from torchvision.utils import save_image
import os
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import torch
from torch import nn
import torch.optim as optim
import torchvision
#pip install torchvision
from torchvision import transforms, models, datasets
#https://pytorch.org/docs/stable/torchvision/index.html
import imageio
import time
import warnings
import random
import sys
import copy
import json
from PIL import Image

设置超参数

# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Hyper-parameters
latent_size = 64
hidden_size = 256
image_size = 784
num_epochs = 200
batch_size = 100
# latent_size：这是潜在向量的大小，用作生成器网络的输入以生成假图像。
#latent_size 的大小会影响生成图像的多样性和质量。如果 latent_size 太小，则生成器可能无法捕获数据分布的所有变化，从而导致生成图像缺乏多样性。如果 latent_size 太大，则生成器可能会过拟合训练数据，从而导致生成图像质量下降。
# hidden_size：这是隐藏层的大小，用于定义生成器和鉴别器网络中隐藏层的大小。
# image_size：这是图像的大小，表示图像的像素数。在这种情况下，图像被重塑为一维张量，因此图像大小等于图像的长度。
# num_epochs：这是训练期间整个数据集通过网络的次数。
# batch_size：每批次中图像的数量。

sample_dir = 'samples'

# Create a directory if not exists
#创建一个sample数据集用来存储数据，一个是minist中的真实的图像，以一个是我们的生成器生成的图像
if not os.path.exists(sample_dir):
    os.makedirs(sample_dir)

#transforms.ToTensor()是一个函数，它可以将PIL.Image或者numpy.ndarray格式的图像转换为torch.FloatTensor格式的张量，并且将像素值范围缩放到[0, 1]之间。
#transforms.Normalize(mean, std)是一个类，它可以对张量图像进行标准化，即减去给定的均值mean并除以给定的标准差std。这样做可以使得图像的分布接近标准正态分布，有利于模型的训练和收敛。
transform = transforms.Compose([
                transforms.ToTensor(),
                transforms.Normalize(mean=[0.5],   # 1 for greyscale channels
                                     std=[0.5])])

#在 Colab 文件系统的 /content/drive/ 目录下挂载您的 Google Drive
from google.colab import drive
drive.mount('/content/drive/')
# 指定当前的工作文件夹

import os
# 此处为google drive中的文件路径,drive为之前指定的工作根目录，要加上
os.chdir("/content/drive/MyDrive/gan/")

# MNIST dataset
mnist = torchvision.datasets.MNIST(root='./data',
                                   train=True,
                                   transform=transform,
                                   download=True)

# Data loader
data_loader = torch.utils.data.DataLoader(dataset=mnist,
                                          batch_size=batch_size,
                                          shuffle=True)

#定义判别器
# Discriminator
D = nn.Sequential(
    nn.Linear(image_size, hidden_size),
    #该函数相比于ReLU，保留了一些负轴的值，缓解了激活值过小而导致神经元参数无法更新的问题，其中α\alphaα默认0.01。
    nn.LeakyReLU(0.2),
    nn.Linear(hidden_size, hidden_size),
    nn.LeakyReLU(0.2),
    nn.Linear(hidden_size, 1),
    #将值映射到0~1
    nn.Sigmoid())

#定义生成器
# Generator
G = nn.Sequential(
    nn.Linear(latent_size, hidden_size),
    nn.ReLU(),
    nn.Linear(hidden_size, hidden_size),
    nn.ReLU(),
    nn.Linear(hidden_size, image_size),
    #将值映射到-1~1
    nn.Tanh())

# Device setting
D = D.to(device)
G = G.to(device)

定义优化器，以及损失函数

# Binary cross entropy loss and optimizer
#nn.BCELoss()函数是二分类交叉熵损失函数，用于计算二分类问题中的交叉熵损失
criterion = nn.BCELoss()
d_optimizer = torch.optim.Adam(D.parameters(), lr=0.0002)
g_optimizer = torch.optim.Adam(G.parameters(), lr=0.0002)

#定义函数将生成器生成的图像的像素值的范围转化为0~1
# denorm函数的作用是将输入张量的值从[-1,1]范围转换为[0,1]范围。
def denorm(x):
    out = (x + 1) / 2
    return out.clamp(0, 1)
# reset_grad函数的作用是将判别器和生成器的梯度清零，以便进行下一次反向传播
def reset_grad():
    d_optimizer.zero_grad()
    g_optimizer.zero_grad()
    

# Start training
total_step = len(data_loader)
for epoch in range(num_epochs):
    for i, (images, _) in enumerate(data_loader):
        images = images.reshape(batch_size, -1).to(device)

        # Create the labels which are later used as input for the BCE loss
        real_labels = torch.ones(batch_size, 1).to(device)
        fake_labels = torch.zeros(batch_size, 1).to(device)

        # ================================================================== #
        #                      Train the discriminator                       #
        # ================================================================== #
		#训练判别器，让判别器能够对真实的和虚假的图片都进行判断
        # Compute BCE_Loss using real images where BCE_Loss(x, y): - y * log(D(x)) - (1-y) * log(1 - D(x))
        # Second term of the loss is always zero since real_labels == 1
        outputs = D(images)
        #计算对于真实样本的损失
        #真实标签列表是作为BCELoss公式中的y参数，它和预测值x一起计算交叉熵。
        #具体来说，当y为1时，损失值为-log(x)，
        #当y为0时，损失值为-log(1-x)。
        #这样可以保证当预测值x和真实标签y一致时，损失值最小，当预测值x和真实标签y相反时，损失值最大。
        d_loss_real = criterion(outputs, real_labels)
        real_score = outputs

        # Compute BCELoss using fake images
        # First term of the loss is always zero since fake_labels == 0
        z = torch.randn(batch_size, latent_size).to(device)
        fake_images = G(z)
        outputs = D(fake_images)
        #计算对于虚假样本的损失
        d_loss_fake = criterion(outputs, fake_labels)
        fake_score = outputs

        # Backprop and optimize
        #总损失为判断两个判断错误的损失，既我们希望找到一个对于正确错误的样本都能进行判断正确的判别器
        d_loss = d_loss_real + d_loss_fake
        reset_grad()
        d_loss.backward()
        d_optimizer.step()

        # ================================================================== #
        #                        Train the generator                         #
        # ================================================================== #

        # Compute loss with fake images
        #随机生成一个输入，也就是虚假的图片
        z = torch.randn(batch_size, latent_size).to(device)
        #根据虚假输入，生成一个图片
        fake_images = G(z)
        #给判别器判断
        outputs = D(fake_images)

        # We train G to maximize log(D(G(z)) instead of minimizing log(1-D(G(z)))
        # For the reason, see the last paragraph of section 3. https://arxiv.org/pdf/1406.2661.pdf
        #计算生成器的损失，我们希望，生成的图像能和真实的图像类似，所以这里y取1，只考虑-ylog(p(x)),也就是判断正确为真的损失
        g_loss = criterion(outputs, real_labels)

        # Backprop and optimize
        reset_grad()
        g_loss.backward()
        g_optimizer.step()

        if (i+1) % 200 == 0:
            print('Epoch [{}/{}], Step [{}/{}], d_loss: {:.4f}, g_loss: {:.4f}, D(x): {:.2f}, D(G(z)): {:.2f}'
                  .format(epoch, num_epochs, i+1, total_step, d_loss.item(), g_loss.item(),
                          real_score.mean().item(), fake_score.mean().item()))

    # Save real images
    if (epoch+1) == 1:
        images = images.reshape(images.size(0), 1, 28, 28)
        save_image(denorm(images), os.path.join(sample_dir, 'real_images.png'))

    # Save sampled images
    #每一次迭代，我们保存我们的生成器能生成的虚假的图像，看看我们的生成器生成的图片在逐步的真实话的过程
    fake_images = fake_images.reshape(fake_images.size(0), 1, 28, 28)
    save_image(denorm(fake_images), os.path.join(sample_dir, 'fake_images-{}.png'.format(epoch+1)))

训练时的迭代过程

我们可以看到其中每次迭代的过程中
g_loss:生成器的损失在下降（说明生成器生成的图像越来越能被判别器识别为真实的图像）
D(G(z)): 判别器对于生成器生成的样本识别真样本的概率在上升（说明生成器的效果在不断提高，逐渐的可以生成真实的样本）

Epoch [0/200], Step [200/600], d_loss: 0.0252, g_loss: 5.2554, D(x): 1.00, D(G(z)): 0.02
Epoch [0/200], Step [400/600], d_loss: 0.1340, g_loss: 4.9320, D(x): 0.95, D(G(z)): 0.07
Epoch [0/200], Step [600/600], d_loss: 0.2178, g_loss: 4.7700, D(x): 0.93, D(G(z)): 0.07
Epoch [1/200], Step [200/600], d_loss: 0.3207, g_loss: 2.5174, D(x): 0.88, D(G(z)): 0.07
Epoch [1/200], Step [400/600], d_loss: 1.6857, g_loss: 3.8114, D(x): 0.64, D(G(z)): 0.30
Epoch [1/200], Step [600/600], d_loss: 1.2818, g_loss: 2.4387, D(x): 0.79, D(G(z)): 0.39
Epoch [2/200], Step [200/600], d_loss: 0.3939, g_loss: 3.4207, D(x): 0.91, D(G(z)): 0.19
Epoch [2/200], Step [400/600], d_loss: 0.2278, g_loss: 2.5563, D(x): 0.93, D(G(z)): 0.11
Epoch [2/200], Step [600/600], d_loss: 0.6092, g_loss: 4.0915, D(x): 0.83, D(G(z)): 0.19
Epoch [3/200], Step [200/600], d_loss: 0.2883, g_loss: 3.6595, D(x): 0.87, D(G(z)): 0.07
Epoch [3/200], Step [400/600], d_loss: 0.5897, g_loss: 2.6272, D(x): 0.81, D(G(z)): 0.15
Epoch [3/200], Step [600/600], d_loss: 0.8164, g_loss: 2.3492, D(x): 0.81, D(G(z)): 0.31
Epoch [4/200], Step [200/600], d_loss: 0.7031, g_loss: 2.1948, D(x): 0.84, D(G(z)): 0.25
Epoch [4/200], Step [400/600], d_loss: 0.1647, g_loss: 3.7823, D(x): 0.98, D(G(z)): 0.08
Epoch [4/200], Step [600/600], d_loss: 0.1939, g_loss: 3.5391, D(x): 0.91, D(G(z)): 0.05
Epoch [5/200], Step [200/600], d_loss: 0.1912, g_loss: 3.8443, D(x): 0.92, D(G(z)): 0.05
Epoch [5/200], Step [400/600], d_loss: 0.1662, g_loss: 3.8918, D(x): 0.97, D(G(z)): 0.11
Epoch [5/200], Step [600/600], d_loss: 0.3716, g_loss: 3.8777, D(x): 0.87, D(G(z)): 0.06
Epoch [6/200], Step [200/600], d_loss: 0.5468, g_loss: 3.8969, D(x): 0.85, D(G(z)): 0.10
Epoch [6/200], Step [400/600], d_loss: 0.1739, g_loss: 3.8602, D(x): 0.94, D(G(z)): 0.04
Epoch [6/200], Step [600/600], d_loss: 0.2472, g_loss: 4.8380, D(x): 0.91, D(G(z)): 0.04
Epoch [7/200], Step [200/600], d_loss: 0.2011, g_loss: 4.5990, D(x): 0.92, D(G(z)): 0.06
Epoch [7/200], Step [400/600], d_loss: 0.3489, g_loss: 5.6411, D(x): 0.90, D(G(z)): 0.06
Epoch [7/200], Step [600/600], d_loss: 0.3301, g_loss: 3.0097, D(x): 0.93, D(G(z)): 0.16
Epoch [8/200], Step [200/600], d_loss: 0.3328, g_loss: 3.4204, D(x): 0.92, D(G(z)): 0.13
Epoch [8/200], Step [400/600], d_loss: 0.1620, g_loss: 3.1926, D(x): 0.96, D(G(z)): 0.09
Epoch [8/200], Step [600/600], d_loss: 0.2524, g_loss: 3.4148, D(x): 0.98, D(G(z)): 0.18
Epoch [9/200], Step [200/600], d_loss: 0.1735, g_loss: 3.2235, D(x): 0.95, D(G(z)): 0.06
Epoch [9/200], Step [400/600], d_loss: 0.1905, g_loss: 4.0506, D(x): 0.94, D(G(z)): 0.07
Epoch [9/200], Step [600/600], d_loss: 0.1437, g_loss: 4.8081, D(x): 0.96, D(G(z)): 0.08
Epoch [10/200], Step [200/600], d_loss: 0.0497, g_loss: 5.0490, D(x): 0.98, D(G(z)): 0.02
Epoch [10/200], Step [400/600], d_loss: 0.0652, g_loss: 5.5561, D(x): 0.97, D(G(z)): 0.01
Epoch [10/200], Step [600/600], d_loss: 0.1745, g_loss: 4.9265, D(x): 0.93, D(G(z)): 0.03
Epoch [11/200], Step [200/600], d_loss: 0.1788, g_loss: 4.2305, D(x): 0.95, D(G(z)): 0.07
Epoch [11/200], Step [400/600], d_loss: 0.0884, g_loss: 4.9210, D(x): 0.97, D(G(z)): 0.03
Epoch [11/200], Step [600/600], d_loss: 0.4536, g_loss: 5.0761, D(x): 0.91, D(G(z)): 0.18
Epoch [12/200], Step [200/600], d_loss: 0.3983, g_loss: 5.9821, D(x): 0.93, D(G(z)): 0.13
Epoch [12/200], Step [400/600], d_loss: 0.2873, g_loss: 5.5326, D(x): 0.91, D(G(z)): 0.08
Epoch [12/200], Step [600/600], d_loss: 0.1984, g_loss: 4.5899, D(x): 0.97, D(G(z)): 0.12
Epoch [13/200], Step [200/600], d_loss: 0.1524, g_loss: 4.7841, D(x): 0.94, D(G(z)): 0.03
Epoch [13/200], Step [400/600], d_loss: 0.1075, g_loss: 4.5099, D(x): 0.98, D(G(z)): 0.07
Epoch [13/200], Step [600/600], d_loss: 0.5001, g_loss: 6.8877, D(x): 0.84, D(G(z)): 0.01
Epoch [14/200], Step [200/600], d_loss: 0.0878, g_loss: 4.6591, D(x): 0.98, D(G(z)): 0.06
Epoch [14/200], Step [400/600], d_loss: 0.2258, g_loss: 4.7830, D(x): 0.97, D(G(z)): 0.13
Epoch [14/200], Step [600/600], d_loss: 0.2287, g_loss: 4.9709, D(x): 0.93, D(G(z)): 0.07
Epoch [15/200], Step [200/600], d_loss: 0.2274, g_loss: 5.0505, D(x): 0.94, D(G(z)): 0.07
Epoch [15/200], Step [400/600], d_loss: 0.1501, g_loss: 5.0111, D(x): 0.94, D(G(z)): 0.04
Epoch [15/200], Step [600/600], d_loss: 0.0859, g_loss: 5.2517, D(x): 0.95, D(G(z)): 0.02
Epoch [16/200], Step [200/600], d_loss: 0.1900, g_loss: 4.7658, D(x): 0.95, D(G(z)): 0.04
Epoch [16/200], Step [400/600], d_loss: 0.1400, g_loss: 7.4018, D(x): 0.97, D(G(z)): 0.03
Epoch [16/200], Step [600/600], d_loss: 0.1485, g_loss: 5.5882, D(x): 0.99, D(G(z)): 0.10
Epoch [17/200], Step [200/600], d_loss: 0.2869, g_loss: 4.3017, D(x): 0.90, D(G(z)): 0.02
Epoch [17/200], Step [400/600], d_loss: 0.2603, g_loss: 5.7215, D(x): 0.98, D(G(z)): 0.17
Epoch [17/200], Step [600/600], d_loss: 0.1268, g_loss: 5.8928, D(x): 0.97, D(G(z)): 0.06
Epoch [18/200], Step [200/600], d_loss: 0.0614, g_loss: 6.3626, D(x): 0.97, D(G(z)): 0.02
Epoch [18/200], Step [400/600], d_loss: 0.3950, g_loss: 5.1696, D(x): 0.95, D(G(z)): 0.17
Epoch [18/200], Step [600/600], d_loss: 0.0887, g_loss: 4.8490, D(x): 0.97, D(G(z)): 0.03
Epoch [19/200], Step [200/600], d_loss: 0.1939, g_loss: 3.7379, D(x): 0.95, D(G(z)): 0.09
Epoch [19/200], Step [400/600], d_loss: 0.3316, g_loss: 5.7292, D(x): 0.88, D(G(z)): 0.01
Epoch [19/200], Step [600/600], d_loss: 0.1429, g_loss: 4.8458, D(x): 0.96, D(G(z)): 0.05
Epoch [20/200], Step [200/600], d_loss: 0.1976, g_loss: 7.0141, D(x): 0.93, D(G(z)): 0.04
Epoch [20/200], Step [400/600], d_loss: 0.1226, g_loss: 5.1962, D(x): 0.95, D(G(z)): 0.02
Epoch [20/200], Step [600/600], d_loss: 0.2435, g_loss: 7.1254, D(x): 0.92, D(G(z)): 0.01
Epoch [21/200], Step [200/600], d_loss: 0.3504, g_loss: 4.0122, D(x): 0.90, D(G(z)): 0.09
Epoch [21/200], Step [400/600], d_loss: 0.3238, g_loss: 5.0485, D(x): 0.94, D(G(z)): 0.14
Epoch [21/200], Step [600/600], d_loss: 0.2065, g_loss: 3.8730, D(x): 0.94, D(G(z)): 0.06
Epoch [22/200], Step [200/600], d_loss: 0.3684, g_loss: 4.4913, D(x): 0.87, D(G(z)): 0.02
Epoch [22/200], Step [400/600], d_loss: 0.1684, g_loss: 4.6138, D(x): 0.93, D(G(z)): 0.05
Epoch [22/200], Step [600/600], d_loss: 0.2458, g_loss: 5.2389, D(x): 0.93, D(G(z)): 0.08
Epoch [23/200], Step [200/600], d_loss: 0.3259, g_loss: 4.3412, D(x): 0.88, D(G(z)): 0.07
Epoch [23/200], Step [400/600], d_loss: 0.2862, g_loss: 4.0427, D(x): 0.94, D(G(z)): 0.10
Epoch [23/200], Step [600/600], d_loss: 0.4527, g_loss: 3.7884, D(x): 0.92, D(G(z)): 0.18
Epoch [24/200], Step [200/600], d_loss: 0.1750, g_loss: 4.4059, D(x): 0.93, D(G(z)): 0.04
Epoch [24/200], Step [400/600], d_loss: 0.1966, g_loss: 4.7848, D(x): 0.96, D(G(z)): 0.10
Epoch [24/200], Step [600/600], d_loss: 0.2126, g_loss: 3.6014, D(x): 0.96, D(G(z)): 0.11
Epoch [25/200], Step [200/600], d_loss: 0.4429, g_loss: 3.7987, D(x): 0.88, D(G(z)): 0.07
Epoch [25/200], Step [400/600], d_loss: 0.3222, g_loss: 4.3068, D(x): 0.94, D(G(z)): 0.16
Epoch [25/200], Step [600/600], d_loss: 0.2818, g_loss: 4.9270, D(x): 0.94, D(G(z)): 0.07
Epoch [26/200], Step [200/600], d_loss: 0.2719, g_loss: 5.2220, D(x): 0.95, D(G(z)): 0.14
Epoch [26/200], Step [400/600], d_loss: 0.4343, g_loss: 4.7186, D(x): 0.85, D(G(z)): 0.05
Epoch [26/200], Step [600/600], d_loss: 0.4143, g_loss: 4.4156, D(x): 0.86, D(G(z)): 0.03
Epoch [27/200], Step [200/600], d_loss: 0.3997, g_loss: 3.5575, D(x): 0.92, D(G(z)): 0.13
Epoch [27/200], Step [400/600], d_loss: 0.3701, g_loss: 4.4439, D(x): 0.87, D(G(z)): 0.02
Epoch [27/200], Step [600/600], d_loss: 0.0873, g_loss: 4.3754, D(x): 0.98, D(G(z)): 0.05
Epoch [28/200], Step [200/600], d_loss: 0.4607, g_loss: 3.8893, D(x): 0.95, D(G(z)): 0.19
Epoch [28/200], Step [400/600], d_loss: 0.3372, g_loss: 5.0975, D(x): 0.97, D(G(z)): 0.16
Epoch [28/200], Step [600/600], d_loss: 0.2284, g_loss: 5.4233, D(x): 0.92, D(G(z)): 0.06
Epoch [29/200], Step [200/600], d_loss: 0.3817, g_loss: 4.3107, D(x): 0.93, D(G(z)): 0.12
Epoch [29/200], Step [400/600], d_loss: 0.3336, g_loss: 3.7298, D(x): 0.97, D(G(z)): 0.20
Epoch [29/200], Step [600/600], d_loss: 0.2664, g_loss: 4.5941, D(x): 0.92, D(G(z)): 0.08
Epoch [30/200], Step [200/600], d_loss: 0.4520, g_loss: 3.2173, D(x): 0.87, D(G(z)): 0.16
Epoch [30/200], Step [400/600], d_loss: 0.4444, g_loss: 2.4830, D(x): 0.91, D(G(z)): 0.21
Epoch [30/200], Step [600/600], d_loss: 0.2679, g_loss: 4.0562, D(x): 0.92, D(G(z)): 0.08
Epoch [31/200], Step [200/600], d_loss: 0.4414, g_loss: 4.9419, D(x): 0.93, D(G(z)): 0.20
Epoch [31/200], Step [400/600], d_loss: 0.4257, g_loss: 4.2394, D(x): 0.90, D(G(z)): 0.16
Epoch [31/200], Step [600/600], d_loss: 0.1577, g_loss: 4.1475, D(x): 0.93, D(G(z)): 0.05
Epoch [32/200], Step [200/600], d_loss: 0.6468, g_loss: 3.1813, D(x): 0.79, D(G(z)): 0.09
Epoch [32/200], Step [400/600], d_loss: 0.7241, g_loss: 4.1790, D(x): 0.78, D(G(z)): 0.12
Epoch [32/200], Step [600/600], d_loss: 0.4787, g_loss: 3.5841, D(x): 0.87, D(G(z)): 0.14
Epoch [33/200], Step [200/600], d_loss: 0.2906, g_loss: 3.6536, D(x): 0.94, D(G(z)): 0.15
Epoch [33/200], Step [400/600], d_loss: 0.4640, g_loss: 3.3191, D(x): 0.93, D(G(z)): 0.18
Epoch [33/200], Step [600/600], d_loss: 0.6650, g_loss: 3.3779, D(x): 0.83, D(G(z)): 0.20
Epoch [34/200], Step [200/600], d_loss: 0.3749, g_loss: 3.5957, D(x): 0.90, D(G(z)): 0.14
Epoch [34/200], Step [400/600], d_loss: 0.3069, g_loss: 4.4897, D(x): 0.92, D(G(z)): 0.13
Epoch [34/200], Step [600/600], d_loss: 0.4221, g_loss: 3.1977, D(x): 0.86, D(G(z)): 0.11
Epoch [35/200], Step [200/600], d_loss: 0.5369, g_loss: 2.4619, D(x): 0.80, D(G(z)): 0.11
Epoch [35/200], Step [400/600], d_loss: 0.5826, g_loss: 2.3311, D(x): 0.94, D(G(z)): 0.29
Epoch [35/200], Step [600/600], d_loss: 0.4576, g_loss: 3.8068, D(x): 0.83, D(G(z)): 0.06
Epoch [36/200], Step [200/600], d_loss: 0.2154, g_loss: 3.4432, D(x): 0.93, D(G(z)): 0.08
Epoch [36/200], Step [400/600], d_loss: 0.4272, g_loss: 3.7969, D(x): 0.91, D(G(z)): 0.17
Epoch [36/200], Step [600/600], d_loss: 0.4357, g_loss: 4.6301, D(x): 0.86, D(G(z)): 0.06
Epoch [37/200], Step [200/600], d_loss: 0.3840, g_loss: 3.5209, D(x): 0.86, D(G(z)): 0.09
Epoch [37/200], Step [400/600], d_loss: 0.2520, g_loss: 4.5026, D(x): 0.93, D(G(z)): 0.09
Epoch [37/200], Step [600/600], d_loss: 0.2975, g_loss: 4.3481, D(x): 0.96, D(G(z)): 0.12
Epoch [38/200], Step [200/600], d_loss: 0.2116, g_loss: 4.2693, D(x): 0.96, D(G(z)): 0.12
Epoch [38/200], Step [400/600], d_loss: 0.2980, g_loss: 5.6267, D(x): 0.91, D(G(z)): 0.08
Epoch [38/200], Step [600/600], d_loss: 0.3867, g_loss: 3.2204, D(x): 0.85, D(G(z)): 0.09
Epoch [39/200], Step [200/600], d_loss: 0.5077, g_loss: 2.9204, D(x): 0.94, D(G(z)): 0.25
Epoch [39/200], Step [400/600], d_loss: 0.5755, g_loss: 3.8340, D(x): 0.82, D(G(z)): 0.07
Epoch [39/200], Step [600/600], d_loss: 0.3072, g_loss: 4.0534, D(x): 0.88, D(G(z)): 0.09
Epoch [40/200], Step [200/600], d_loss: 0.2913, g_loss: 3.2628, D(x): 0.94, D(G(z)): 0.11
Epoch [40/200], Step [400/600], d_loss: 0.5462, g_loss: 3.0916, D(x): 0.82, D(G(z)): 0.12
Epoch [40/200], Step [600/600], d_loss: 0.6271, g_loss: 3.8926, D(x): 0.80, D(G(z)): 0.12
Epoch [41/200], Step [200/600], d_loss: 0.4911, g_loss: 3.1461, D(x): 0.90, D(G(z)): 0.19
Epoch [41/200], Step [400/600], d_loss: 0.4260, g_loss: 3.4678, D(x): 0.87, D(G(z)): 0.11
Epoch [41/200], Step [600/600], d_loss: 0.6902, g_loss: 2.5126, D(x): 0.76, D(G(z)): 0.15
Epoch [42/200], Step [200/600], d_loss: 0.4464, g_loss: 3.1702, D(x): 0.83, D(G(z)): 0.12
Epoch [42/200], Step [400/600], d_loss: 0.3756, g_loss: 2.6393, D(x): 0.89, D(G(z)): 0.12
Epoch [42/200], Step [600/600], d_loss: 0.6360, g_loss: 3.0586, D(x): 0.79, D(G(z)): 0.18
Epoch [43/200], Step [200/600], d_loss: 0.4233, g_loss: 3.2700, D(x): 0.86, D(G(z)): 0.12
Epoch [43/200], Step [400/600], d_loss: 0.6836, g_loss: 3.4518, D(x): 0.80, D(G(z)): 0.21
Epoch [43/200], Step [600/600], d_loss: 0.7702, g_loss: 2.3113, D(x): 0.76, D(G(z)): 0.19
Epoch [44/200], Step [200/600], d_loss: 0.3849, g_loss: 2.8337, D(x): 0.87, D(G(z)): 0.11
Epoch [44/200], Step [400/600], d_loss: 0.5281, g_loss: 2.4929, D(x): 0.87, D(G(z)): 0.21
Epoch [44/200], Step [600/600], d_loss: 0.7222, g_loss: 2.9932, D(x): 0.85, D(G(z)): 0.27
Epoch [45/200], Step [200/600], d_loss: 0.5053, g_loss: 3.4436, D(x): 0.85, D(G(z)): 0.15
Epoch [45/200], Step [400/600], d_loss: 0.5699, g_loss: 3.0064, D(x): 0.80, D(G(z)): 0.15
Epoch [45/200], Step [600/600], d_loss: 0.5866, g_loss: 2.7113, D(x): 0.83, D(G(z)): 0.19
Epoch [46/200], Step [200/600], d_loss: 0.5891, g_loss: 2.5745, D(x): 0.81, D(G(z)): 0.14
Epoch [46/200], Step [400/600], d_loss: 0.4424, g_loss: 2.5302, D(x): 0.82, D(G(z)): 0.10
Epoch [46/200], Step [600/600], d_loss: 0.6589, g_loss: 2.2096, D(x): 0.76, D(G(z)): 0.16
Epoch [47/200], Step [200/600], d_loss: 0.5520, g_loss: 2.6966, D(x): 0.85, D(G(z)): 0.21
Epoch [47/200], Step [400/600], d_loss: 0.7059, g_loss: 2.7294, D(x): 0.81, D(G(z)): 0.22
Epoch [47/200], Step [600/600], d_loss: 0.2912, g_loss: 3.7787, D(x): 0.88, D(G(z)): 0.08
Epoch [48/200], Step [200/600], d_loss: 0.4149, g_loss: 2.3708, D(x): 0.90, D(G(z)): 0.19
Epoch [48/200], Step [400/600], d_loss: 0.4266, g_loss: 3.3905, D(x): 0.87, D(G(z)): 0.13
Epoch [48/200], Step [600/600], d_loss: 0.3298, g_loss: 3.2459, D(x): 0.90, D(G(z)): 0.11
Epoch [49/200], Step [200/600], d_loss: 0.3318, g_loss: 3.5093, D(x): 0.92, D(G(z)): 0.14
Epoch [49/200], Step [400/600], d_loss: 0.6507, g_loss: 3.4914, D(x): 0.78, D(G(z)): 0.11
Epoch [49/200], Step [600/600], d_loss: 0.6534, g_loss: 2.7849, D(x): 0.76, D(G(z)): 0.08
Epoch [50/200], Step [200/600], d_loss: 0.6406, g_loss: 3.6847, D(x): 0.82, D(G(z)): 0.20
Epoch [50/200], Step [400/600], d_loss: 0.6941, g_loss: 2.9424, D(x): 0.80, D(G(z)): 0.21
Epoch [50/200], Step [600/600], d_loss: 0.4733, g_loss: 3.2116, D(x): 0.86, D(G(z)): 0.15
Epoch [51/200], Step [200/600], d_loss: 0.3287, g_loss: 3.6701, D(x): 0.91, D(G(z)): 0.16
Epoch [51/200], Step [400/600], d_loss: 0.5537, g_loss: 1.9568, D(x): 0.79, D(G(z)): 0.11
Epoch [51/200], Step [600/600], d_loss: 0.6470, g_loss: 2.4228, D(x): 0.80, D(G(z)): 0.22
Epoch [52/200], Step [200/600], d_loss: 0.8183, g_loss: 3.1269, D(x): 0.73, D(G(z)): 0.13
Epoch [52/200], Step [400/600], d_loss: 0.4611, g_loss: 2.3535, D(x): 0.88, D(G(z)): 0.20
Epoch [52/200], Step [600/600], d_loss: 0.5158, g_loss: 2.3248, D(x): 0.85, D(G(z)): 0.20
Epoch [53/200], Step [200/600], d_loss: 0.5410, g_loss: 2.3491, D(x): 0.82, D(G(z)): 0.17
Epoch [53/200], Step [400/600], d_loss: 0.6135, g_loss: 2.0376, D(x): 0.79, D(G(z)): 0.19
Epoch [53/200], Step [600/600], d_loss: 0.6995, g_loss: 3.0768, D(x): 0.76, D(G(z)): 0.15
Epoch [54/200], Step [200/600], d_loss: 0.7788, g_loss: 2.3476, D(x): 0.84, D(G(z)): 0.32
Epoch [54/200], Step [400/600], d_loss: 0.6057, g_loss: 2.6538, D(x): 0.78, D(G(z)): 0.16
Epoch [54/200], Step [600/600], d_loss: 0.6524, g_loss: 1.9913, D(x): 0.83, D(G(z)): 0.26
Epoch [55/200], Step [200/600], d_loss: 0.4699, g_loss: 3.0903, D(x): 0.88, D(G(z)): 0.20
Epoch [55/200], Step [400/600], d_loss: 0.6438, g_loss: 2.4830, D(x): 0.77, D(G(z)): 0.18
Epoch [55/200], Step [600/600], d_loss: 0.8641, g_loss: 1.5713, D(x): 0.80, D(G(z)): 0.32
Epoch [56/200], Step [200/600], d_loss: 0.5685, g_loss: 1.8569, D(x): 0.78, D(G(z)): 0.17
Epoch [56/200], Step [400/600], d_loss: 0.4462, g_loss: 2.5452, D(x): 0.82, D(G(z)): 0.13
Epoch [56/200], Step [600/600], d_loss: 0.5907, g_loss: 2.4652, D(x): 0.75, D(G(z)): 0.12
Epoch [57/200], Step [200/600], d_loss: 0.4843, g_loss: 3.0264, D(x): 0.83, D(G(z)): 0.16
Epoch [57/200], Step [400/600], d_loss: 0.5594, g_loss: 2.5355, D(x): 0.82, D(G(z)): 0.20
Epoch [57/200], Step [600/600], d_loss: 0.5502, g_loss: 2.2562, D(x): 0.80, D(G(z)): 0.15
Epoch [58/200], Step [200/600], d_loss: 0.6652, g_loss: 2.0351, D(x): 0.82, D(G(z)): 0.24
Epoch [58/200], Step [400/600], d_loss: 0.4033, g_loss: 2.9153, D(x): 0.90, D(G(z)): 0.20
Epoch [58/200], Step [600/600], d_loss: 0.6348, g_loss: 2.1119, D(x): 0.88, D(G(z)): 0.30
Epoch [59/200], Step [200/600], d_loss: 0.6415, g_loss: 3.1282, D(x): 0.81, D(G(z)): 0.20
Epoch [59/200], Step [400/600], d_loss: 0.5136, g_loss: 3.3376, D(x): 0.82, D(G(z)): 0.17
Epoch [59/200], Step [600/600], d_loss: 0.6462, g_loss: 2.3404, D(x): 0.76, D(G(z)): 0.16
Epoch [60/200], Step [200/600], d_loss: 0.5160, g_loss: 2.9145, D(x): 0.80, D(G(z)): 0.14
Epoch [60/200], Step [400/600], d_loss: 0.7120, g_loss: 2.6986, D(x): 0.79, D(G(z)): 0.27
Epoch [60/200], Step [600/600], d_loss: 0.4580, g_loss: 2.8799, D(x): 0.88, D(G(z)): 0.17
Epoch [61/200], Step [200/600], d_loss: 0.5593, g_loss: 2.7334, D(x): 0.77, D(G(z)): 0.13
Epoch [61/200], Step [400/600], d_loss: 0.7277, g_loss: 2.6792, D(x): 0.90, D(G(z)): 0.30
Epoch [61/200], Step [600/600], d_loss: 0.7283, g_loss: 1.6875, D(x): 0.68, D(G(z)): 0.11
Epoch [62/200], Step [200/600], d_loss: 0.3957, g_loss: 3.4459, D(x): 0.89, D(G(z)): 0.18
Epoch [62/200], Step [400/600], d_loss: 0.5846, g_loss: 1.6489, D(x): 0.81, D(G(z)): 0.17
Epoch [62/200], Step [600/600], d_loss: 0.7358, g_loss: 2.3752, D(x): 0.82, D(G(z)): 0.28
Epoch [63/200], Step [200/600], d_loss: 0.5577, g_loss: 2.9186, D(x): 0.87, D(G(z)): 0.21
Epoch [63/200], Step [400/600], d_loss: 0.6466, g_loss: 2.4374, D(x): 0.85, D(G(z)): 0.27
Epoch [63/200], Step [600/600], d_loss: 0.6891, g_loss: 2.2632, D(x): 0.84, D(G(z)): 0.30
Epoch [64/200], Step [200/600], d_loss: 0.8519, g_loss: 2.3878, D(x): 0.65, D(G(z)): 0.12
Epoch [64/200], Step [400/600], d_loss: 0.6176, g_loss: 2.4059, D(x): 0.81, D(G(z)): 0.20
Epoch [64/200], Step [600/600], d_loss: 0.8032, g_loss: 3.1006, D(x): 0.68, D(G(z)): 0.12
Epoch [65/200], Step [200/600], d_loss: 0.5564, g_loss: 2.9973, D(x): 0.77, D(G(z)): 0.14
Epoch [65/200], Step [400/600], d_loss: 0.7254, g_loss: 2.0362, D(x): 0.81, D(G(z)): 0.24
Epoch [65/200], Step [600/600], d_loss: 0.5845, g_loss: 2.7048, D(x): 0.75, D(G(z)): 0.11
Epoch [66/200], Step [200/600], d_loss: 0.6310, g_loss: 2.9230, D(x): 0.79, D(G(z)): 0.20
Epoch [66/200], Step [400/600], d_loss: 0.5095, g_loss: 2.3467, D(x): 0.77, D(G(z)): 0.13
Epoch [66/200], Step [600/600], d_loss: 0.5869, g_loss: 2.3648, D(x): 0.74, D(G(z)): 0.13
Epoch [67/200], Step [200/600], d_loss: 0.7529, g_loss: 2.2525, D(x): 0.72, D(G(z)): 0.15
Epoch [67/200], Step [400/600], d_loss: 0.6669, g_loss: 2.1734, D(x): 0.76, D(G(z)): 0.19
Epoch [67/200], Step [600/600], d_loss: 0.6977, g_loss: 2.0799, D(x): 0.77, D(G(z)): 0.20
Epoch [68/200], Step [200/600], d_loss: 0.5344, g_loss: 2.2810, D(x): 0.85, D(G(z)): 0.21
Epoch [68/200], Step [400/600], d_loss: 0.7502, g_loss: 2.7689, D(x): 0.73, D(G(z)): 0.21
Epoch [68/200], Step [600/600], d_loss: 0.8459, g_loss: 2.0199, D(x): 0.84, D(G(z)): 0.36
Epoch [69/200], Step [200/600], d_loss: 0.5745, g_loss: 2.1853, D(x): 0.78, D(G(z)): 0.18
Epoch [69/200], Step [400/600], d_loss: 0.7515, g_loss: 2.3225, D(x): 0.77, D(G(z)): 0.25
Epoch [69/200], Step [600/600], d_loss: 0.7967, g_loss: 1.8125, D(x): 0.74, D(G(z)): 0.22
Epoch [70/200], Step [200/600], d_loss: 0.7456, g_loss: 2.2358, D(x): 0.81, D(G(z)): 0.27
Epoch [70/200], Step [400/600], d_loss: 0.6154, g_loss: 2.4855, D(x): 0.81, D(G(z)): 0.24
Epoch [70/200], Step [600/600], d_loss: 0.5187, g_loss: 2.1255, D(x): 0.85, D(G(z)): 0.22
Epoch [71/200], Step [200/600], d_loss: 0.5937, g_loss: 3.1329, D(x): 0.80, D(G(z)): 0.19
Epoch [71/200], Step [400/600], d_loss: 0.5692, g_loss: 2.5442, D(x): 0.79, D(G(z)): 0.19
Epoch [71/200], Step [600/600], d_loss: 0.4669, g_loss: 2.8289, D(x): 0.84, D(G(z)): 0.18
Epoch [72/200], Step [200/600], d_loss: 0.5994, g_loss: 2.6761, D(x): 0.82, D(G(z)): 0.20
Epoch [72/200], Step [400/600], d_loss: 0.4832, g_loss: 2.6731, D(x): 0.83, D(G(z)): 0.17
Epoch [72/200], Step [600/600], d_loss: 0.5769, g_loss: 2.7867, D(x): 0.78, D(G(z)): 0.17
Epoch [73/200], Step [200/600], d_loss: 0.6073, g_loss: 2.0403, D(x): 0.79, D(G(z)): 0.17
Epoch [73/200], Step [400/600], d_loss: 0.7357, g_loss: 2.4262, D(x): 0.74, D(G(z)): 0.20
Epoch [73/200], Step [600/600], d_loss: 0.5897, g_loss: 2.4739, D(x): 0.82, D(G(z)): 0.21
Epoch [74/200], Step [200/600], d_loss: 0.6338, g_loss: 2.2802, D(x): 0.80, D(G(z)): 0.23
Epoch [74/200], Step [400/600], d_loss: 0.5724, g_loss: 2.7530, D(x): 0.84, D(G(z)): 0.23
Epoch [74/200], Step [600/600], d_loss: 0.6505, g_loss: 2.5929, D(x): 0.78, D(G(z)): 0.18
Epoch [75/200], Step [200/600], d_loss: 0.6580, g_loss: 2.5763, D(x): 0.85, D(G(z)): 0.28
Epoch [75/200], Step [400/600], d_loss: 0.6089, g_loss: 2.0517, D(x): 0.81, D(G(z)): 0.22
Epoch [75/200], Step [600/600], d_loss: 0.6480, g_loss: 2.3134, D(x): 0.83, D(G(z)): 0.27
Epoch [76/200], Step [200/600], d_loss: 0.8040, g_loss: 2.1533, D(x): 0.77, D(G(z)): 0.28
Epoch [76/200], Step [400/600], d_loss: 0.7031, g_loss: 2.7626, D(x): 0.79, D(G(z)): 0.24
Epoch [76/200], Step [600/600], d_loss: 0.7798, g_loss: 1.9259, D(x): 0.77, D(G(z)): 0.26
Epoch [77/200], Step [200/600], d_loss: 0.6174, g_loss: 2.2541, D(x): 0.76, D(G(z)): 0.16
Epoch [77/200], Step [400/600], d_loss: 0.7185, g_loss: 1.5864, D(x): 0.78, D(G(z)): 0.22
Epoch [77/200], Step [600/600], d_loss: 0.6941, g_loss: 2.3483, D(x): 0.82, D(G(z)): 0.27
Epoch [78/200], Step [200/600], d_loss: 0.8584, g_loss: 2.3806, D(x): 0.72, D(G(z)): 0.22
Epoch [78/200], Step [400/600], d_loss: 0.6060, g_loss: 1.8562, D(x): 0.83, D(G(z)): 0.24
Epoch [78/200], Step [600/600], d_loss: 0.7914, g_loss: 2.5783, D(x): 0.82, D(G(z)): 0.32
Epoch [79/200], Step [200/600], d_loss: 0.7219, g_loss: 2.3257, D(x): 0.73, D(G(z)): 0.20
Epoch [79/200], Step [400/600], d_loss: 0.7538, g_loss: 2.2944, D(x): 0.78, D(G(z)): 0.27
Epoch [79/200], Step [600/600], d_loss: 0.6531, g_loss: 2.0533, D(x): 0.80, D(G(z)): 0.24
Epoch [80/200], Step [200/600], d_loss: 0.9207, g_loss: 1.8896, D(x): 0.64, D(G(z)): 0.16
Epoch [80/200], Step [400/600], d_loss: 0.7419, g_loss: 2.2362, D(x): 0.69, D(G(z)): 0.17
Epoch [80/200], Step [600/600], d_loss: 0.5812, g_loss: 2.3372, D(x): 0.76, D(G(z)): 0.14
Epoch [81/200], Step [200/600], d_loss: 0.5252, g_loss: 2.2365, D(x): 0.78, D(G(z)): 0.17
Epoch [81/200], Step [400/600], d_loss: 0.7609, g_loss: 2.1495, D(x): 0.75, D(G(z)): 0.26
Epoch [81/200], Step [600/600], d_loss: 0.7870, g_loss: 2.3520, D(x): 0.75, D(G(z)): 0.26
Epoch [82/200], Step [200/600], d_loss: 0.7311, g_loss: 2.2137, D(x): 0.78, D(G(z)): 0.27
Epoch [82/200], Step [400/600], d_loss: 0.6972, g_loss: 1.7540, D(x): 0.77, D(G(z)): 0.26
Epoch [82/200], Step [600/600], d_loss: 0.8349, g_loss: 1.7994, D(x): 0.84, D(G(z)): 0.34
Epoch [83/200], Step [200/600], d_loss: 0.8138, g_loss: 2.1877, D(x): 0.80, D(G(z)): 0.32
Epoch [83/200], Step [400/600], d_loss: 0.7913, g_loss: 2.0897, D(x): 0.72, D(G(z)): 0.23
Epoch [83/200], Step [600/600], d_loss: 0.9098, g_loss: 1.5447, D(x): 0.66, D(G(z)): 0.23
Epoch [84/200], Step [200/600], d_loss: 0.8892, g_loss: 1.7672, D(x): 0.73, D(G(z)): 0.30
Epoch [84/200], Step [400/600], d_loss: 0.5531, g_loss: 2.2540, D(x): 0.80, D(G(z)): 0.21
Epoch [84/200], Step [600/600], d_loss: 0.8780, g_loss: 2.0353, D(x): 0.79, D(G(z)): 0.35
Epoch [85/200], Step [200/600], d_loss: 0.6664, g_loss: 2.4569, D(x): 0.83, D(G(z)): 0.25
Epoch [85/200], Step [400/600], d_loss: 0.9369, g_loss: 1.9261, D(x): 0.72, D(G(z)): 0.30
Epoch [85/200], Step [600/600], d_loss: 0.8626, g_loss: 1.3774, D(x): 0.74, D(G(z)): 0.30
Epoch [86/200], Step [200/600], d_loss: 0.8138, g_loss: 2.2834, D(x): 0.72, D(G(z)): 0.24
Epoch [86/200], Step [400/600], d_loss: 0.9225, g_loss: 2.0189, D(x): 0.70, D(G(z)): 0.29
Epoch [86/200], Step [600/600], d_loss: 0.7091, g_loss: 2.5431, D(x): 0.75, D(G(z)): 0.22
Epoch [87/200], Step [200/600], d_loss: 0.7513, g_loss: 1.6684, D(x): 0.72, D(G(z)): 0.23
Epoch [87/200], Step [400/600], d_loss: 0.7172, g_loss: 1.7539, D(x): 0.82, D(G(z)): 0.30
Epoch [87/200], Step [600/600], d_loss: 1.0858, g_loss: 2.5704, D(x): 0.64, D(G(z)): 0.24
Epoch [88/200], Step [200/600], d_loss: 0.8175, g_loss: 2.5280, D(x): 0.74, D(G(z)): 0.25
Epoch [88/200], Step [400/600], d_loss: 0.7610, g_loss: 1.8840, D(x): 0.78, D(G(z)): 0.28
Epoch [88/200], Step [600/600], d_loss: 0.7132, g_loss: 2.2979, D(x): 0.72, D(G(z)): 0.18
Epoch [89/200], Step [200/600], d_loss: 0.8786, g_loss: 1.6000, D(x): 0.72, D(G(z)): 0.25
Epoch [89/200], Step [400/600], d_loss: 0.7933, g_loss: 1.9502, D(x): 0.71, D(G(z)): 0.23
Epoch [89/200], Step [600/600], d_loss: 0.5541, g_loss: 2.7170, D(x): 0.81, D(G(z)): 0.19
Epoch [90/200], Step [200/600], d_loss: 0.9670, g_loss: 1.5065, D(x): 0.64, D(G(z)): 0.23
Epoch [90/200], Step [400/600], d_loss: 0.8945, g_loss: 1.5057, D(x): 0.74, D(G(z)): 0.32
Epoch [90/200], Step [600/600], d_loss: 0.8275, g_loss: 1.9291, D(x): 0.66, D(G(z)): 0.20
Epoch [91/200], Step [200/600], d_loss: 0.7411, g_loss: 2.1024, D(x): 0.75, D(G(z)): 0.24
Epoch [91/200], Step [400/600], d_loss: 0.8613, g_loss: 1.7976, D(x): 0.72, D(G(z)): 0.27
Epoch [91/200], Step [600/600], d_loss: 0.9204, g_loss: 1.9358, D(x): 0.81, D(G(z)): 0.38
Epoch [92/200], Step [200/600], d_loss: 0.5769, g_loss: 1.8164, D(x): 0.85, D(G(z)): 0.26
Epoch [92/200], Step [400/600], d_loss: 0.8222, g_loss: 1.5064, D(x): 0.73, D(G(z)): 0.26
Epoch [92/200], Step [600/600], d_loss: 0.5844, g_loss: 2.5825, D(x): 0.78, D(G(z)): 0.20
Epoch [93/200], Step [200/600], d_loss: 0.6836, g_loss: 1.9087, D(x): 0.74, D(G(z)): 0.22
Epoch [93/200], Step [400/600], d_loss: 0.7328, g_loss: 1.7869, D(x): 0.72, D(G(z)): 0.22
Epoch [93/200], Step [600/600], d_loss: 0.7112, g_loss: 1.6405, D(x): 0.79, D(G(z)): 0.28
Epoch [94/200], Step [200/600], d_loss: 0.7915, g_loss: 1.7422, D(x): 0.71, D(G(z)): 0.24
Epoch [94/200], Step [400/600], d_loss: 0.7935, g_loss: 1.4080, D(x): 0.85, D(G(z)): 0.36
Epoch [94/200], Step [600/600], d_loss: 1.0396, g_loss: 1.4284, D(x): 0.76, D(G(z)): 0.35
Epoch [95/200], Step [200/600], d_loss: 0.7436, g_loss: 2.2755, D(x): 0.73, D(G(z)): 0.18
Epoch [95/200], Step [400/600], d_loss: 0.8688, g_loss: 1.8874, D(x): 0.76, D(G(z)): 0.30
Epoch [95/200], Step [600/600], d_loss: 0.8444, g_loss: 2.3308, D(x): 0.66, D(G(z)): 0.19
Epoch [96/200], Step [200/600], d_loss: 0.7442, g_loss: 2.3771, D(x): 0.73, D(G(z)): 0.22
Epoch [96/200], Step [400/600], d_loss: 0.7074, g_loss: 2.2321, D(x): 0.74, D(G(z)): 0.18
Epoch [96/200], Step [600/600], d_loss: 0.8868, g_loss: 1.4519, D(x): 0.67, D(G(z)): 0.21
Epoch [97/200], Step [200/600], d_loss: 0.8345, g_loss: 1.6682, D(x): 0.72, D(G(z)): 0.28
Epoch [97/200], Step [400/600], d_loss: 0.7934, g_loss: 2.1283, D(x): 0.68, D(G(z)): 0.17
Epoch [97/200], Step [600/600], d_loss: 0.7260, g_loss: 1.4867, D(x): 0.76, D(G(z)): 0.25
Epoch [98/200], Step [200/600], d_loss: 0.7596, g_loss: 2.1651, D(x): 0.76, D(G(z)): 0.24
Epoch [98/200], Step [400/600], d_loss: 0.8643, g_loss: 1.9000, D(x): 0.69, D(G(z)): 0.24
Epoch [98/200], Step [600/600], d_loss: 0.7319, g_loss: 1.6552, D(x): 0.75, D(G(z)): 0.25
Epoch [99/200], Step [200/600], d_loss: 0.7711, g_loss: 1.7455, D(x): 0.69, D(G(z)): 0.20
Epoch [99/200], Step [400/600], d_loss: 0.6971, g_loss: 2.1426, D(x): 0.80, D(G(z)): 0.25
Epoch [99/200], Step [600/600], d_loss: 0.8339, g_loss: 2.5612, D(x): 0.68, D(G(z)): 0.23
Epoch [100/200], Step [200/600], d_loss: 0.9574, g_loss: 1.6630, D(x): 0.65, D(G(z)): 0.26
Epoch [100/200], Step [400/600], d_loss: 0.9069, g_loss: 1.6404, D(x): 0.65, D(G(z)): 0.23
Epoch [100/200], Step [600/600], d_loss: 0.7358, g_loss: 1.6987, D(x): 0.74, D(G(z)): 0.24
Epoch [101/200], Step [200/600], d_loss: 0.8096, g_loss: 1.5011, D(x): 0.73, D(G(z)): 0.26
Epoch [101/200], Step [400/600], d_loss: 0.9509, g_loss: 1.5224, D(x): 0.75, D(G(z)): 0.32
Epoch [101/200], Step [600/600], d_loss: 1.0163, g_loss: 1.7723, D(x): 0.76, D(G(z)): 0.41
Epoch [102/200], Step [200/600], d_loss: 0.9929, g_loss: 1.3493, D(x): 0.73, D(G(z)): 0.37
Epoch [102/200], Step [400/600], d_loss: 0.8234, g_loss: 2.2110, D(x): 0.67, D(G(z)): 0.19
Epoch [102/200], Step [600/600], d_loss: 0.6964, g_loss: 2.1019, D(x): 0.74, D(G(z)): 0.21

对分别对生成器和判别器的模型进行保存

# Save the model checkpoints
torch.save(G.state_dict(), 'G.ckpt')
torch.save(D.state_dict(), 'D.ckpt')

训练结果的展示

一开提供的minist的真实数据的图片

通过迭代1次过后生成器生成的图片

通过迭代10次过后生成器生成的图片

通过迭代50次过后生成器生成的图片

通过迭代103次过后生成器生成的图片

我们可以看到，随着迭代次数的增加，我们的生产起生成的图像逐渐的与minist数据的提供的数据图像相类似。