From 472dec1f4ecd3b6d6ffee231422f3b1735f71023 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Sun, 25 Dec 2016 22:25:25 -0800 Subject: [PATCH] gan first draft --- gan/README.md | 162 ++++++++++++++++++++++++++++++++++++++++- gan/gan.png | Bin 0 -> 33275 bytes gan/mnist_sample.png | Bin 0 -> 28721 bytes gan/uniform_sample.png | Bin 0 -> 20609 bytes 4 files changed, 161 insertions(+), 1 deletion(-) create mode 100644 gan/gan.png create mode 100644 gan/mnist_sample.png create mode 100644 gan/uniform_sample.png diff --git a/gan/README.md b/gan/README.md index 40b9b35..052ebb9 100644 --- a/gan/README.md +++ b/gan/README.md @@ -1 +1,161 @@ -TODO: Write about https://github.com/PaddlePaddle/Paddle/tree/develop/demo/gan +# 对抗式生成网络 + +## 背景介绍 +本章我们介绍对抗式生成网络,也称为Generative Adversarial Network(GAN)。对抗式生成网络是生成模型的一种,可以用非监督学习的办法来学习输入数据的分布,从而能达到产生和输入数据拥有同样概率分布的人造数据。 + +在本章里,我们展对抗式生产网络的细节,以及如何用PaddlePaddle训练一个GAN模型。 + +## 效果展示 +一个简单的例子是向对抗式生成网络输入MNIST手写数字的图片,然后让模型自己产生类似的手写数字图片。由训练好的GAN模型产生的手写数字图片的例子画在图1中。 + +

+
+ 图1. GAN生成的MNIST例图 +

+ +## 模型概览 +对抗式生成网络的大致结构在图2中画出,它由两部分组成:一个生成器(G)和一个分别器(D),两者都是有多层神经网络构成的。生成器的输入是一个多维的已知概率分布的噪音(z),通过神经网络变换,输出伪样本。分别器输的输入是真样本和伪样本,输出为判断样本为真样本的概率。训练时生成器和分别器处于相互竞争对抗状态,生成器会尽量生成和真样本相近的伪样本让分别器无法分辨真伪,而分别器则会尽力去分辨伪样本。具体的损失函数如下: + +$$\min_G\max_D \text{Loss} = \min_G\max_D \frac{1}{m}\sum_{i=1}^m[\log D(x^i) + log(1-D(G(z^i)))]$$ + +

+
+ 图2. GAN模型结构 + figure credit +

+ +训练时,生成器和分别器会轮流通过随机梯度下降算法更新参数。生成器的目标函数是让自己产生的样本被分别器分类为真,而分别器的目标函数则是正确的区分真伪样本。当对抗式生成模型训练收敛到平衡态的时候,生成器会把输入的噪音分布转化成真的样本数据分布,而分别器则完全无法分辨真伪图片。 + +## 数据准备 + +### 数据介绍与下载 +这章会用到两种数据,一种是简单的人造数据,一种是图片。 + +人造数据是二维均匀分布,由下面的代码生成: + +```python +# synthesize 2-D uniform data in gan_trainer.py:114 +def load_uniform_data(): + data = numpy.random.rand(1000000, 2).astype('float32') + return data +``` + +图片数据是MNIST手写数字,可由下面的代码下载: + +```bash +$cd data/ +$./get_mnist_data.sh +``` + +## 模型配置说明 +由于对抗式生产网络涉及到多个神经网络,所以必须用paddle Python API来训练。下面的介绍也可以部分的拿来当作paddle Python API的使用说明。 + +### 模型结构 +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** 是分别器,**generator** 是生成器,**generator_training** 是生成器加分别器因为训练生成器时需要用到分别器提供目标函数。这个对应关系在下面这段代码中定义: + +```python +if is_generator_training: + noise = data_layer(name="noise", size=noise_dim) + sample = generator(noise) + +if is_discriminator_training: + sample = data_layer(name="sample", size=sample_dim) + +if is_generator_training or is_discriminator_training: + label = data_layer(name="label", size=1) + prob = discriminator(sample) + cost = cross_entropy(input=prob, label=label) + classification_error_evaluator( + input=prob, label=label, name=mode + '_error') + outputs(cost) + +if is_generator: + noise = data_layer(name="noise", size=noise_dim) + outputs(generator(noise)) +``` + +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:初始化Paddle环境,解析设置,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: + +```python +import py_paddle.swig_paddle as api +# init paddle environment +api.initPaddle('--use_gpu=' + use_gpu, '--dot_period=10', + '--log_period=100', '--gpu_id=' + args.gpu_id, + '--save_dir=' + "./%s_params/" % data_source) + +# Parse config +gen_conf = parse_config(conf, "mode=generator_training,data=" + data_source) +dis_conf = parse_config(conf, "mode=discriminator_training,data=" + data_source) +generator_conf = parse_config(conf, "mode=generator,data=" + data_source) + +# Create GradientMachine +dis_training_machine = api.GradientMachine.createFromConfigProto( +dis_conf.model_config) +gen_training_machine = api.GradientMachine.createFromConfigProto( +gen_conf.model_config) +generator_machine = api.GradientMachine.createFromConfigProto( +generator_conf.model_config) + +# Create trainer +dis_trainer = api.Trainer.create(dis_conf, dis_training_machine) +gen_trainer = api.Trainer.create(gen_conf, gen_training_machine) +``` + +为了能够平衡生成器和分别器之间的能力,我们依据它们各自的损失函数的大小来决定训练对象,即我们选择训练那个损失函数更大的网络。损失函数的值可以通过GradientMachine的forward pass来计算。 + +```python +def get_training_loss(training_machine, inputs): + outputs = api.Arguments.createArguments(0) + training_machine.forward(inputs, outputs, api.PASS_TEST) + loss = outputs.getSlotValue(0).copyToNumpyMat() + return numpy.mean(loss) +``` + +每当训练完一个网络,我们需要和其他几个网络同步互相分享的参数值。下面的代码展示了其中一个例子: + +```python +# Train the gen_training +gen_trainer.trainOneDataBatch(batch_size, data_batch_gen) + +# Copy the parameters from gen_training to dis_training and generator +copy_shared_parameters(gen_training_machine, +dis_training_machine) +copy_shared_parameters(gen_training_machine, generator_machine) +``` + +### 数据定义 +这里数据没有通过dataprovider提供,而是在gan_trainer.py里面直接产生data_batch并提供给trainer。 + +```python +code to be inserted +``` + +### 算法配置 + +在这里,我们指定了模型的训练参数, 选择学习率和batch size。这里的beta1参数比默认值0.9小很多是为了使学习的过程更稳定。 + +```python +settings( + batch_size=128, + learning_rate=1e-4, + learning_method=AdamOptimizer(beta1=0.5)) + +``` + +##训练模型 +用MNIST手写数字图片训练对抗式生成网络可以用如下的命令: + +```bash +$python gan_trainer.py -d mnist --useGpu 1 +``` + +## 总结 +本章中, + + +## 参考文献 +1. Bengio Y, Ducharme R, Vincent P, et al. [A neural probabilistic language model](http://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf)[J]. journal of machine learning research, 2003, 3(Feb): 1137-1155. +2. Mikolov T, Sutskever I, Chen K, et al. [Distributed representations of words and phrases and their compositionality](http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf)[C]//Advances in neural information processing systems. 2013: 3111-3119. +3. Mikolov T, Kombrink S, Deoras A, et al. [Rnnlm-recurrent neural network language modeling toolkit](http://www.fit.vutbr.cz/~imikolov/rnnlm/rnnlm-demo.pdf)[C]//Proc. of the 2011 ASRU Workshop. 2011: 196-201. +4. Mikolov T, Chen K, Corrado G, et al. 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