From defbd27ffd8eb4ae21b4435c1316ca59634433d0 Mon Sep 17 00:00:00 2001 From: wangyang59 Date: Fri, 6 Jan 2017 15:33:35 -0800 Subject: [PATCH] added history and introduction --- gan/.gitignore | 8 + gan/README.md | 364 +++++++++++++++++++++++++++-- gan/image/cifar_comparisons.jpg | Bin 0 -> 467835 bytes gan/{ => image}/dcgan.png | Bin gan/{ => image}/gan.png | Bin gan/{ => image}/gan_conf_graph.png | Bin gan/image/gan_ig.png | Bin 0 -> 234869 bytes gan/{ => image}/mnist_sample.png | Bin gan/{ => image}/uniform_sample.png | Bin 9 files changed, 357 insertions(+), 15 deletions(-) create mode 100644 gan/.gitignore create mode 100644 gan/image/cifar_comparisons.jpg rename gan/{ => image}/dcgan.png (100%) rename gan/{ => image}/gan.png (100%) rename gan/{ => image}/gan_conf_graph.png (100%) create mode 100644 gan/image/gan_ig.png rename gan/{ => image}/mnist_sample.png (100%) rename gan/{ => image}/uniform_sample.png (100%) diff --git a/gan/.gitignore b/gan/.gitignore new file mode 100644 index 0000000..b020716 --- /dev/null +++ b/gan/.gitignore @@ -0,0 +1,8 @@ +output/ +uniform_params/ +cifar_params/ +mnist_params/ +*.log +*.pyc +data/mnist_data/ +data/cifar-10-batches-py/ diff --git a/gan/README.md b/gan/README.md index 4ee1a11..11d8b47 100644 --- a/gan/README.md +++ b/gan/README.md @@ -2,20 +2,29 @@ ## 背景介绍 -本章我们介绍对抗式生成网络,也称为Generative Adversarial Network (GAN) \[[1](#参考文献)\]。GAN的核心思想是,为了更好地训练一个生成式神经元网络模型(generative model),我们引入一个判别式神经元网络模型来构造优化目标函数。实验证明,在图像自动生成、图像去噪、和缺失图像补全等应用里,这种方法可以训练处一个能更逼近训练数据分布的生成式模型。 +本章我们介绍对抗式生成网络,也称为Generative Adversarial Network (GAN) \[[1](#参考文献)\]。GAN的核心思想是,为了更好地训练一个生成式神经元网络模型(generative model),我们引入一个判别式神经元网络模型来构造优化目标函数。实验证明,这种方法可以训练出一个能更逼近训练数据分布的生成式模型。 到目前为止,大部分取得好的应用效果的神经元网络模型都是有监督训练(supervised learning)的判别式模型(discriminative models),包括图像识别中使用的convolutional networks和在语音识别中使用的connectionist temporal classification (CTC) networks。在这些例子里,训练数据 X 都是带有标签 y 的——每张图片附带了一个或者多个tag,每段语音附带了一段对应的文本;而模型的输入是 X,输出是 y,训练得到的模型表示从X到y的映射函数 y=f(X)。 和判别式神经元网络模型相对的一类模型是生成式模型(generative models)。它们通常是通过非监督训练(unsupervised learning)来得到的。这类模型的训练数据里只有 X,没有y。训练的目标是希望模型能蕴含训练数据的统计分布信息,从而可以从训练好的模型里产生出新的、在训练数据里没有出现过的新数据 x'。 -一些为人熟知的生成模型的例子包括受限玻尔兹曼机(Restricted Boltzmann Machine)\[[4](#参考文献)\],深度玻尔兹曼机(Deep Boltzmann Machine)\[[5](#参考文献)\],神经自回归分布估计(Neural Autoregressive Distribution Estimator)\[[6](#参考文献)\]等。 +生成模型在很多方面都有广泛应用。比如在图像处理方面的图像自动生成、图像去噪、和缺失图像补全等应用。比如在增强学习的条件下,可以根据之前观测到的数据和可能的操作来生成未来的数据,使得agent能够从中选择最佳的操作。比如在半监督(semi-supervised)学习的条件下,把生成模型生成的数据加入分类器训练当中,能够减少分类器训练对于标记数据数量的要求。真实世界中大量数据都是没有标注的,人为标注数据会耗费大量人力财力,这就使生成模型有了它的用武之地。 -近年出现了一些专门用来生成图像的模型,一种是变分自编码器(variational autoencoder)\[[3](#参考文献)\],它是在概率图模型(probabilistic graphical model)的框架下面搭建了一个生成模型,对数据有完整的概率描述,训练时是通过调节参数来最大化数据的概率。用这种方法产生的图片,虽然似然(likelihood)比较高,但经常看起来比较模糊。另一种是像素循环神经网络(Pixel Recurrent Neural Network)\[[7](#参考文献)\],它是通过根据周围的像素来一个像素一个像素的生成图片,但这种方法生成的图片在全局看来会不太一致。为了解决这些问题,人们又提出了本章所要介绍的另一种生成模型,对抗式生成网络。 +之前出现的生成模型,一般是直接构造模型$P_model(x; \theta)$来模拟真实数据分布$P_data(x)$。而这个模拟的过程,通常是由最大似然(Maximum Likelihood)的办法来调节模型参数,使得观测到的真实数据在该模型下概率最大。这里模型的种类又可以分为两大类,一类是tractable的,一类是untractable的。第一类里的一个例子是像素循环神经网络(Pixel Recurrent Neural Network)\[[7](#参考文献)\],它是用概率的链式规则把对于n维数据的概率分解成n个一维数据的概率相乘,也就是说根据周围的像素来一个像素一个像素的生成图片。这种方法的问题是对于一个n维的数据,需要n步才能生成,速度较慢,而且图片整体看来各处不连续。 + +为了能有更复杂的模型来模拟数据分布,人们提出了第二类untractable的模型,这样就只能用近似的办法来学习模型参数。近似的办法一种是构造一个似然的下限(Likelihood lower-bound),然后用变分的办法来提高这个下限的值,其中一个例子是变分自编码器(variational autoencoder)\[[3](#参考文献)\]。用这种方法产生的图片,虽然似然比较高,但经常看起来会比较模糊。近似的另一种办法是通过马尔可夫链-蒙地卡罗(Markov-Chain-Monte-Carlo)来取样本,比如深度玻尔兹曼机(Deep Boltzmann Machine)\[[5](#参考文献)\]就是用的这个方法。这种方法的问题是取样本的计算量非常大,而且没有办法并行化。 + +为了解决这些问题,人们又提出了本章所要介绍的另一种生成模型,对抗式生成网络。它相比于前面提到的方法,具有生成网络结构灵活,产生样本快,生成图像看起来更真实的优点。 + +

+
+ 图1. Cifar-10生成图像对比 +

本文介绍如何训练一个产生式神经元网络模型,它的输入是一个随机生成的向量(相当于不需要任何有意义的输入),而输出是一幅图像,其中有一个数字。换句话说,我们训练一个会写字(阿拉伯数字)的神经元网络模型。它“写”的一些数字如下图:

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图1. GAN生成的MNIST例图

@@ -27,22 +36,22 @@ 因为神经元网络是一个有向图,总是有输入和输出的。当我们用无监督学习方式来训练一个神经元网络,用于描述训练数据分布的时候,一个通常的学习目标是估计一组参数,使得输出和输入很接近 —— 或者说输入是什么输出就是什么。很多早期的生成式神经元网络模型,包括受限波尔茨曼机和 autoencoder 都是这么训练的。这种情况下优化目标经常是最小化输出和输入的差别。

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+
图2. GAN模型原理示意图 - figure credit + figure credit

对抗式训练里,我们用一个判别式模型 D 辅助构造优化目标函数,来训练一个生成式模型 G。如图2所示。具体训练流程是不断交替执行如下两步: 1. 更新模型 D: 1. 固定G的参数不变,对于一组随机输入,得到一组(产生式)输出,$X_f$,并且将其label成“假”。 - 1. 从训练数据 X 采样一组 $X_r$,并且label为“真”。 - 1. 用这两组数据更新模型 D,从而使D能够分辨G产生的数据和真实训练数据。 + 2. 从训练数据 X 采样一组 $X_r$,并且label为“真”。 + 3. 用这两组数据更新模型 D,从而使D能够分辨G产生的数据和真实训练数据。 -1. 更新模型 G: +2. 更新模型 G: 1. 把G的输出和D的输入连接起来,得到一个网路。 - 1. 给G一组随机输入,期待G的输出让D认为像是“真”的。 - 1. 在D的输出端,优化目标是通过更新G的参数来最小化D的输出和“真”的差别。 + 2. 给G一组随机输入,期待G的输出让D认为像是“真”的。 + 3. 在D的输出端,优化目标是通过更新G的参数来最小化D的输出和“真”的差别。 上述方法实际上在优化如下目标: @@ -53,14 +62,14 @@ $$\min_G \max_D \frac{1}{N}\sum_{i=1}^N[\log D(x^i) + \log(1-D(G(z^i)))]$$ 在最早的对抗式生成网络的论文中,生成器和分类器用的都是全联接层。在附带的代码[`gan_conf.py`](./gan_conf.py)中,我们实现了一个类似的结构。G和D是由三层全联接层构成,并且在某些全联接层后面加入了批标准化层(batch normalization)。所用网络结构在图3中给出。

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+
图3. GAN模型结构图

由于上面的这种网络都是由全联接层组成,所以没有办法很好的生成图片数据,也没有办法做的很深。所以在随后的论文中,人们提出了深度卷积对抗式生成网络(deep convolutional generative adversarial network or DCGAN)\[[2](#参考文献)\]。在DCGAN中,生成器 G 是由多个卷积转置层(transposed convolution)组成的,这样可以用更少的参数来生成质量更高的图片。具体网络结果可参见图4。而判别器是由多个卷积层组成。

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图4. DCGAN生成器模型结构 figure credit

@@ -131,6 +140,331 @@ settings( ``` ### 模型结构 +本章里我们主要用到两种模型。一种是基本的GAN模型,主要由全联接层搭建,在gan_conf.py里面定义。另一种是DCGAN模型,主要由卷基层搭建,在gan_conf_image.py里面定义。 + +```python +# 下面这个函数定义了GAN模型里面的判别器结构 +def discriminator(sample): + """ + discriminator ouputs the probablity of a sample is from generator + or real data. + The output has two dimenstional: dimension 0 is the probablity + of the sample is from generator and dimension 1 is the probabblity + of the sample is from real data. + """ + param_attr = ParamAttr(is_static=is_generator_training) + bias_attr = ParamAttr( + is_static=is_generator_training, initial_mean=1.0, initial_std=0) + + hidden = fc_layer( + input=sample, + name="dis_hidden", + size=hidden_dim, + bias_attr=bias_attr, + param_attr=param_attr, + act=ReluActivation()) + + hidden2 = fc_layer( + input=hidden, + name="dis_hidden2", + size=hidden_dim, + bias_attr=bias_attr, + param_attr=param_attr, + act=LinearActivation()) + + hidden_bn = batch_norm_layer( + hidden2, + act=ReluActivation(), + name="dis_hidden_bn", + bias_attr=bias_attr, + param_attr=ParamAttr( + is_static=is_generator_training, initial_mean=1.0, + initial_std=0.02), + use_global_stats=False) + + return fc_layer( + input=hidden_bn, + name="dis_prob", + size=2, + bias_attr=bias_attr, + param_attr=param_attr, + act=SoftmaxActivation()) + +# 下面这个函数定义了GAN模型里面生成器的结构 +def generator(noise): + """ + generator generates a sample given noise + """ + param_attr = ParamAttr(is_static=is_discriminator_training) + bias_attr = ParamAttr( + is_static=is_discriminator_training, initial_mean=1.0, initial_std=0) + + hidden = fc_layer( + input=noise, + name="gen_layer_hidden", + size=hidden_dim, + bias_attr=bias_attr, + param_attr=param_attr, + act=ReluActivation()) + + hidden2 = fc_layer( + input=hidden, + name="gen_hidden2", + size=hidden_dim, + bias_attr=bias_attr, + param_attr=param_attr, + act=LinearActivation()) + + hidden_bn = batch_norm_layer( + hidden2, + act=ReluActivation(), + name="gen_layer_hidden_bn", + bias_attr=bias_attr, + param_attr=ParamAttr( + is_static=is_discriminator_training, + initial_mean=1.0, + initial_std=0.02), + use_global_stats=False) + + return fc_layer( + input=hidden_bn, + name="gen_layer1", + size=sample_dim, + bias_attr=bias_attr, + param_attr=param_attr, + act=LinearActivation()) +``` + +```python +# 一个卷积/卷积转置层和一个批标准化层打包在一起 +def conv_bn(input, + channels, + imgSize, + num_filters, + output_x, + stride, + name, + param_attr, + bias_attr, + param_attr_bn, + bn, + trans=False, + act=ReluActivation()): + """ + conv_bn is a utility function that constructs a convolution/deconv layer + with an optional batch_norm layer + :param bn: whether to use batch_norm_layer + :type bn: bool + :param trans: whether to use conv (False) or deconv (True) + :type trans: bool + """ + + # calculate the filter_size and padding size based on the given + # imgSize and ouput size + tmp = imgSize - (output_x - 1) * stride + if tmp <= 1 or tmp > 5: + raise ValueError("conv input-output dimension does not fit") + elif tmp <= 3: + filter_size = tmp + 2 + padding = 1 + else: + filter_size = tmp + padding = 0 + + print(imgSize, output_x, stride, filter_size, padding) + + if trans: + nameApx = "_conv" + else: + nameApx = "_convt" + + if bn: + conv = img_conv_layer( + input, + filter_size=filter_size, + num_filters=num_filters, + name=name + nameApx, + num_channels=channels, + act=LinearActivation(), + groups=1, + stride=stride, + padding=padding, + bias_attr=bias_attr, + param_attr=param_attr, + shared_biases=True, + layer_attr=None, + filter_size_y=None, + stride_y=None, + padding_y=None, + trans=trans) + + conv_bn = batch_norm_layer( + conv, + act=act, + name=name + nameApx + "_bn", + bias_attr=bias_attr, + param_attr=param_attr_bn, + use_global_stats=False) + + return conv_bn + else: + conv = img_conv_layer( + input, + filter_size=filter_size, + num_filters=num_filters, + name=name + nameApx, + num_channels=channels, + act=act, + groups=1, + stride=stride, + padding=padding, + bias_attr=bias_attr, + param_attr=param_attr, + shared_biases=True, + layer_attr=None, + filter_size_y=None, + stride_y=None, + padding_y=None, + trans=trans) + return conv + +# 下面这个函数定义了DCGAN模型里面的生成器的结构 +def generator(noise): + """ + generator generates a sample given noise + """ + param_attr = ParamAttr( + is_static=is_discriminator_training, initial_mean=0.0, initial_std=0.02) + bias_attr = ParamAttr( + is_static=is_discriminator_training, initial_mean=0.0, initial_std=0.0) + + param_attr_bn = ParamAttr( + is_static=is_discriminator_training, initial_mean=1.0, initial_std=0.02) + + h1 = fc_layer( + input=noise, + name="gen_layer_h1", + size=s8 * s8 * gf_dim * 4, + bias_attr=bias_attr, + param_attr=param_attr, + act=LinearActivation()) + + h1_bn = batch_norm_layer( + h1, + act=ReluActivation(), + name="gen_layer_h1_bn", + bias_attr=bias_attr, + param_attr=param_attr_bn, + use_global_stats=False) + + h2_bn = conv_bn( + h1_bn, + channels=gf_dim * 4, + output_x=s8, + num_filters=gf_dim * 2, + imgSize=s4, + stride=2, + name="gen_layer_h2", + param_attr=param_attr, + bias_attr=bias_attr, + param_attr_bn=param_attr_bn, + bn=True, + trans=True) + + h3_bn = conv_bn( + h2_bn, + channels=gf_dim * 2, + output_x=s4, + num_filters=gf_dim, + imgSize=s2, + stride=2, + name="gen_layer_h3", + param_attr=param_attr, + bias_attr=bias_attr, + param_attr_bn=param_attr_bn, + bn=True, + trans=True) + + return conv_bn( + h3_bn, + channels=gf_dim, + output_x=s2, + num_filters=c_dim, + imgSize=sample_dim, + stride=2, + name="gen_layer_h4", + param_attr=param_attr, + bias_attr=bias_attr, + param_attr_bn=param_attr_bn, + bn=False, + trans=True, + act=TanhActivation()) + +# 下面这个函数定义了DCGAN模型里面的判别器结构 +def discriminator(sample): + """ + discriminator ouputs the probablity of a sample is from generator + or real data. + The output has two dimenstional: dimension 0 is the probablity + of the sample is from generator and dimension 1 is the probabblity + of the sample is from real data. + """ + param_attr = ParamAttr( + is_static=is_generator_training, initial_mean=0.0, initial_std=0.02) + bias_attr = ParamAttr( + is_static=is_generator_training, initial_mean=0.0, initial_std=0.0) + + param_attr_bn = ParamAttr( + is_static=is_generator_training, initial_mean=1.0, initial_std=0.02) + + h0 = conv_bn( + sample, + channels=c_dim, + imgSize=sample_dim, + num_filters=df_dim, + output_x=s2, + stride=2, + name="dis_h0", + param_attr=param_attr, + bias_attr=bias_attr, + param_attr_bn=param_attr_bn, + bn=False) + + h1_bn = conv_bn( + h0, + channels=df_dim, + imgSize=s2, + num_filters=df_dim * 2, + output_x=s4, + stride=2, + name="dis_h1", + param_attr=param_attr, + bias_attr=bias_attr, + param_attr_bn=param_attr_bn, + bn=True) + + h2_bn = conv_bn( + h1_bn, + channels=df_dim * 2, + imgSize=s4, + num_filters=df_dim * 4, + output_x=s8, + stride=2, + name="dis_h2", + param_attr=param_attr, + bias_attr=bias_attr, + param_attr_bn=param_attr_bn, + bn=True) + + return fc_layer( + input=h2_bn, + name="dis_prob", + size=2, + bias_attr=bias_attr, + param_attr=param_attr, + act=SoftmaxActivation()) +``` + 在文件`gan_conf.py`当中我们定义了三个网络, **generator_training**, **discriminator_training** and **generator**. 和前文提到的模型结构的关系是:**discriminator_training** 是分类器,**generator** 是生成器,**generator_training** 是生成器加分类器因为训练生成器时需要用到分类器提供目标函数。这个对应关系在下面这段代码中定义: @@ -185,8 +519,8 @@ I0105 17:16:37.172737 20517 TrainerInternal.cpp:165] Batch=100 samples=12800 Av 为了能够训练在gan_conf.py中定义的网络,我们需要如下几个步骤: 1. 初始化Paddle环境, -1. 解析设置, -1. 由设置创造GradientMachine以及由GradientMachine创造trainer。 +2. 解析设置, +3. 由设置创造GradientMachine以及由GradientMachine创造trainer。 这几步分别由下面几段代码实现: diff --git a/gan/image/cifar_comparisons.jpg b/gan/image/cifar_comparisons.jpg new file mode 100644 index 0000000000000000000000000000000000000000..8f0b33f091bbcfe88923706b5cd735ef421a3161 GIT binary patch literal 467835 zcmeFYcT^P5*EiTOWDq1u&OtzOMuLcB0f|FKa?Ux9f|3OR0VOIq2#AuSfFx0noHIzy 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gan/image/mnist_sample.png diff --git a/gan/uniform_sample.png b/gan/image/uniform_sample.png similarity index 100% rename from gan/uniform_sample.png rename to gan/image/uniform_sample.png -- GitLab

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