生成对抗网络训练
什么是生成对抗网络(GAN)？(What are Generative Adversarial Networks (GANs)?)
Designed by Ian Goodfellow and his colleagues in 2014, GANs consist of two neural networks that are trained together in a zero-sum game where one player’s loss is the gain of another.
GAN由Ian Goodfellow和他的同事在2014年设计，由两个神经网络组成，它们在零和游戏中一起训练，其中一个玩家的损失是另一个玩家的损失。
To understand GANs we need to be familiar with generative models and discriminative models.
要了解GAN，我们需要熟悉生成模型和判别模型。
Generative models try to output new data points using the distribution from the training set. These models generate new data instances. These models capture joint probability p(X, Y)
生成模型尝试使用训练集中的分布来输出新的数据点。 这些模型生成新的数据实例。 这些模型捕获联合概率p(X，Y)
Types of Generative models1. Explicit density models2. Implicit density models
生成模型的类型1.显式密度模型2。 隐式密度模型
Explicit density models define an explicit density function while implicit density models define a stochastic procedure that can directly generate data.
显式密度模型定义了显式密度函数，而隐式密度模型定义了可以直接生成数据的随机过程。
If you are interested in reading more about generative models, check out this popular GitHub repository below.
如果您有兴趣阅读有关生成模型的更多信息，请在下面查看此流行的GitHub存储库。
On the other hand, Discriminative models capture conditional probabilities p(X/Y) and they differentiate different data instances.
另一方面，判别模型捕获条件概率p(X / Y)，并区分不同的数据实例。
Generative models solve difficult tasks. The level of attention detail is more compared to that in discriminative models. Simply speaking generative models do more work. Generative models try to approximate the real data distribution as closely as possible.
生成模型可以解决难题。 与区分模型相比，关注细节的级别更高。 简而言之，生成模型可以做更多的工作。 生成模型试图尽可能接近实际数据的分布。
In the figure above we can see that the discriminative model tries to separate 0’s and 1’s data space. Whereas the generative models closely approximate the 0’s and 1’s data space.
在上图中，我们可以看到判别模型试图将0和1的数据空间分开。 生成模型非常接近0和1的数据空间。
Now that you know the basic definitions of generative and discriminative models, let us learn about GANs.
既然您已经知道了生成模型和判别模型的基本定义，那么让我们了解GAN。
鉴别器和生成器网络-GAN游戏 (The Discriminator & Generator networks — The GAN Game)
Generative Adversarial Networks (GANs) are generative models. They generate whole images in parallel. GANs consist of 2 networks: Discriminator & Generator networks
生成对抗网络(GAN)是生成模型。 它们并行生成整个图像。 GAN包含2个网络：鉴别器和生成器网络
GANs use a differentiable function. This is usually a neural network. We call it the generator network. This generator network takes random inputs. These inputs are noise. This noise is given to a differentiable function that transforms and reshapes the same into a recognizable structure. This could be an image and the same is highly dependent on the noise at the input of the differentiable function.
GAN使用可微函数。 这通常是一个神经网络。 我们称其为发电机网络。 该发电机网络采用随机输入。 这些输入是
