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GAN对抗网络入门教程

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致Great
发布2019-09-18 16:15:29
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发布2019-09-18 16:15:29
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文章被收录于专栏:程序生活

译:A Beginner's Guide to Generative Adversarial Networks (GANs) https://skymind.ai/wiki/generative-adversarial-network-gan

1 GAN简介

生成对抗网络(英语:Generative Adversarial Network,简称GAN)是非监督式学习的一种方法,通过让两个神经网络相互博弈的方式进行学习。该方法由伊恩·古德费洛等人于2014年提出。生成对抗网络由一个生成网络与一个判别网络组成。生成网络从潜在空间(latent space)中随机取样作为输入,其输出结果需要尽量模仿训练集中的真实样本。判别网络的输入则为真实样本或生成网络的输出,其目的是将生成网络的输出从真实样本中尽可能分辨出来。而生成网络则要尽可能地欺骗判别网络。两个网络相互对抗、不断调整参数,最终目的是使判别网络无法判断生成网络的输出结果是否真实。

生成对抗网络常用于生成以假乱真的图片。此外,该方法还被用于生成影片、三维物体模型等。

虽然生成对抗网络原先是为了无监督学习提出的,它也被证明对半监督学习、完全监督学习 、强化学习是有用的。

image

2 生成与判别算法

要理解GAN,你应该知道生成算法是如何工作的,但是在理解生成算法之前,将它们与判别算法进行对比可以加深理解。我们先看下什么事判别算法?

判别算法试图对输入数据进行分类; 也就是说,给定数据实例的特征,它们预测该数据所属的标签或类别。

例如,给定电子邮件中的所有单词(数据实例),判别算法可以预测该消息是spam(垃圾邮件)还是not_spam(非垃圾邮件)。 其中spam是标签之一,从电子邮件收集的单词包是构成输入数据的特征。 当以数学方式表达此问题时,标签称为y,并且要素称为x。公式p(y|x)用于表示“给定x条件下y发生的概率”,在这种情况下,它将转换为“在给定邮件所包含的字词情况下,电子邮件是垃圾邮件的概率”。

因此,判别算法是将特征映射到标签,而生成算法恰恰在做相反的事情。生成算法试图预测给定某个标签下的特征,而不是预测给定某些特征的标签。

生成算法试图回答的问题是:假设这封电子邮件是垃圾邮件,特征的分布或者概率是怎么样的? 虽然判别模型关注y和x之间的关系,但是生成模型关心“你如何得到x。”生成算法是为了计算出(x | y),给出y条件下x发生的概率,或者说给出标签时,特征的概率。 (也就是说,生成算法也可以用作分类器。恰好它们不是对输入数据进行分类。)

下面两句话将判别与生成区分开来:

  • 判别模型学习了类之间的界限
  • 生成模型模拟各个类的分布

3 GANs原理

GAN的基本原理其实非常简单,这里以生成图片为例进行说明。假设我们有两个网络,G(Generator)和D(Discriminator)。正如它的名字所暗示的那样,它们的功能分别是:一个神经网络,称为生成器,生成新的数据实例,而另一个神经网络,判别器,评估它们的真实性; 即判别器决定它所评测的每个数据实例是否属于实际训练数据集。

G是一个生成图片的网络,它接收一个随机的噪声z,通过这个噪声生成图片,记做G(z)。 D是一个判别网络,判别一张图片是不是“真实的”。它的输入参数是x,x代表一张图片,输出D(x)代表x为真实图片的概率,如果为1,就代表100%是真实的图片,而输出为0,就代表不可能是真实的图片。 在训练过程中,生成网络G的目标就是尽量生成真实的图片去欺骗判别网络D。而D的目标就是尽量把G生成的图片和真实的图片分别开来。这样,G和D构成了一个动态的“博弈过程”。

最后博弈的结果是什么?在最理想的状态下,G可以生成足以“以假乱真”的图片G(z)。对于D来说,它难以判定G生成的图片究竟是不是真实的,因此D(G(z)) = 0.5。

reference:https://zhuanlan.zhihu.com/p/24767059

以下是GAN大致步骤:

  • 生成器接收随机数并返回图像。
  • 将生成的图像与从真实数据集中获取的图像流一起馈送到判别器中。
  • 判别器接收真实和假图像并返回概率,0到1之间的数字,1表示真实性的预测,0表示假。

image

您可以将GAN视为诈骗者和警察在猫与老鼠游戏中的反对,其中诈骗者正在学习传递虚假信息,并且警察正在学习如何检测它们。 两者都是动态的; 也就是说,警察也在接受培训,每一方都在不断升级中学习对方的方法。

对于MNIST数据集,判别器网络是标准卷积网络,可以对馈送给它的图像进行分类,二项分类器将图像标记为真实或伪造。 在某种意义上,生成器是反卷积网络:当标准卷积分类器采用图像并对其进行下采样以产生概率时,生成器采用随机噪声矢量并将其上采样到图像。 第一个通过下采样技术(如maxpooling)丢弃数据,第二个生成新数据。

image

4 GANs, Autoencoders and VAEs

下面对生成性对抗网络与其他神经网络(例如自动编码器和变分自动编码器)进行比较。

自动编码器将输入数据编码为矢量。它们创建原始数据的隐藏或压缩表示,在减少维数方面很有用; 也就是说,用作隐藏表示的向量将原始数据压缩为较少数量的突出维度。 自动编码器可以与所谓的解码器配对,允许您根据其隐藏的表示重建输入数据,就像使用受限制的Boltzmann机器一样。

image

变分自动编码器是生成算法,其为编码输入数据添加额外约束,即隐藏表示被标准化。 变分自动编码器能够像自动编码器一样压缩数据并像GAN一样合成数据。 然而GAN可以更精细、细粒度的生成数据,VAE生成的图像往往更加模糊。 Deeplearning4j的例子包括自动编码器和变分自动编码器。(https://github.com/deeplearning4j/dl4j-examples/tree/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/unsupervised

5 Keras 实现GAN

https://github.com/eriklindernoren/Keras-GAN

代码语言:javascript
复制
from __future__ import print_function, division

from keras.datasets import mnist
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam

import matplotlib.pyplot as plt

import sys

import numpy as np
代码语言:javascript
复制
Using TensorFlow backend.
代码语言:javascript
复制
class GAN():
    def __init__(self):
        self.img_rows = 28
        self.img_cols = 28
        self.channels = 1
        self.img_shape = (self.img_rows, self.img_cols, self.channels)

        optimizer = Adam(0.0002, 0.5)

        # Build and compile the discriminator
        self.discriminator = self.build_discriminator()
        self.discriminator.compile(loss='binary_crossentropy',
            optimizer=optimizer,
            metrics=['accuracy'])

        # Build and compile the generator
        self.generator = self.build_generator()
        self.generator.compile(loss='binary_crossentropy', optimizer=optimizer)

        # The generator takes noise as input and generated imgs
        z = Input(shape=(100,))
        img = self.generator(z)

        # For the combined model we will only train the generator
        self.discriminator.trainable = False

        # The valid takes generated images as input and determines validity
        valid = self.discriminator(img)

        # The combined model  (stacked generator and discriminator) takes
        # noise as input => generates images => determines validity
        self.combined = Model(z, valid)
        self.combined.compile(loss='binary_crossentropy', optimizer=optimizer)

    def build_generator(self):

        noise_shape = (100,)

        model = Sequential()

        model.add(Dense(256, input_shape=noise_shape))
        model.add(LeakyReLU(alpha=0.2))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Dense(512))
        model.add(LeakyReLU(alpha=0.2))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Dense(1024))
        model.add(LeakyReLU(alpha=0.2))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Dense(np.prod(self.img_shape), activation='tanh'))
        model.add(Reshape(self.img_shape))

        model.summary()

        noise = Input(shape=noise_shape)
        img = model(noise)

        return Model(noise, img)

    def build_discriminator(self):

        img_shape = (self.img_rows, self.img_cols, self.channels)

        model = Sequential()

        model.add(Flatten(input_shape=img_shape))
        model.add(Dense(512))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dense(256))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dense(1, activation='sigmoid'))
        model.summary()

        img = Input(shape=img_shape)
        validity = model(img)

        return Model(img, validity)

    def train(self, epochs, batch_size=128, save_interval=50):

        # Load the dataset
        (X_train, _), (_, _) = mnist.load_data()

        # Rescale -1 to 1
        X_train = (X_train.astype(np.float32) - 127.5) / 127.5
        X_train = np.expand_dims(X_train, axis=3)

        half_batch = int(batch_size / 2)

        for epoch in range(epochs):

            # ---------------------
            #  Train Discriminator
            # ---------------------

            # Select a random half batch of images
            idx = np.random.randint(0, X_train.shape[0], half_batch)
            imgs = X_train[idx]

            noise = np.random.normal(0, 1, (half_batch, 100))

            # Generate a half batch of new images
            gen_imgs = self.generator.predict(noise)

            # Train the discriminator
            d_loss_real = self.discriminator.train_on_batch(imgs, np.ones((half_batch, 1)))
            d_loss_fake = self.discriminator.train_on_batch(gen_imgs, np.zeros((half_batch, 1)))
            d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)


            # ---------------------
            #  Train Generator
            # ---------------------

            noise = np.random.normal(0, 1, (batch_size, 100))

            # The generator wants the discriminator to label the generated samples
            # as valid (ones)
            valid_y = np.array([1] * batch_size)

            # Train the generator
            g_loss = self.combined.train_on_batch(noise, valid_y)

            # Plot the progress
            if epoch%1000==0:
                print ("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100*d_loss[1], g_loss))

            # If at save interval => save generated image samples
            if epoch % save_interval == 0:
                self.save_imgs(epoch)

    def save_imgs(self, epoch):
        r, c = 5, 5
        noise = np.random.normal(0, 1, (r * c, 100))
        gen_imgs = self.generator.predict(noise)

        # Rescale images 0 - 1
        gen_imgs = 0.5 * gen_imgs + 0.5

        fig, axs = plt.subplots(r, c)
        cnt = 0
        for i in range(r):
            for j in range(c):
                axs[i,j].imshow(gen_imgs[cnt, :,:,0], cmap='gray')
                axs[i,j].axis('off')
                cnt += 1
        fig.savefig("data/gan/images/mnist_%d.png" % epoch)
        plt.close()


if __name__ == '__main__':
    gan = GAN()
    gan.train(epochs=30000, batch_size=32, save_interval=200)
代码语言:javascript
复制
WARNING:tensorflow:From D:\ProgramData\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.

WARNING:tensorflow:From D:\ProgramData\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.

WARNING:tensorflow:From D:\ProgramData\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.

Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
flatten_1 (Flatten)          (None, 784)               0         
_________________________________________________________________
dense_1 (Dense)              (None, 512)               401920    
_________________________________________________________________
leaky_re_lu_1 (LeakyReLU)    (None, 512)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 256)               131328    
_________________________________________________________________
leaky_re_lu_2 (LeakyReLU)    (None, 256)               0         
_________________________________________________________________
dense_3 (Dense)              (None, 1)                 257       
=================================================================
Total params: 533,505
Trainable params: 533,505
Non-trainable params: 0
_________________________________________________________________
WARNING:tensorflow:From D:\ProgramData\Anaconda3\lib\site-packages\keras\optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.

WARNING:tensorflow:From D:\ProgramData\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py:3657: The name tf.log is deprecated. Please use tf.math.log instead.

WARNING:tensorflow:From D:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\ops\nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
WARNING:tensorflow:From D:\ProgramData\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py:148: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.

Model: "sequential_2"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_4 (Dense)              (None, 256)               25856     
_________________________________________________________________
leaky_re_lu_3 (LeakyReLU)    (None, 256)               0         
_________________________________________________________________
batch_normalization_1 (Batch (None, 256)               1024      
_________________________________________________________________
dense_5 (Dense)              (None, 512)               131584    
_________________________________________________________________
leaky_re_lu_4 (LeakyReLU)    (None, 512)               0         
_________________________________________________________________
batch_normalization_2 (Batch (None, 512)               2048      
_________________________________________________________________
dense_6 (Dense)              (None, 1024)              525312    
_________________________________________________________________
leaky_re_lu_5 (LeakyReLU)    (None, 1024)              0         
_________________________________________________________________
batch_normalization_3 (Batch (None, 1024)              4096      
_________________________________________________________________
dense_7 (Dense)              (None, 784)               803600    
_________________________________________________________________
reshape_1 (Reshape)          (None, 28, 28, 1)         0         
=================================================================
Total params: 1,493,520
Trainable params: 1,489,936
Non-trainable params: 3,584
_________________________________________________________________


D:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py:493: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?
  'Discrepancy between trainable weights and collected trainable'


0 [D loss: 0.735185, acc.: 46.88%] [G loss: 0.829077]


D:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py:493: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?
  'Discrepancy between trainable weights and collected trainable'


1000 [D loss: 0.590758, acc.: 71.88%] [G loss: 0.793450]
2000 [D loss: 0.587990, acc.: 62.50%] [G loss: 0.956186]
3000 [D loss: 0.644352, acc.: 59.38%] [G loss: 0.914777]
4000 [D loss: 0.673936, acc.: 62.50%] [G loss: 0.971460]
5000 [D loss: 0.759974, acc.: 53.12%] [G loss: 0.904706]
6000 [D loss: 0.555306, acc.: 81.25%] [G loss: 0.835633]
7000 [D loss: 0.674409, acc.: 62.50%] [G loss: 0.823623]
8000 [D loss: 0.672854, acc.: 53.12%] [G loss: 0.863680]
9000 [D loss: 0.743683, acc.: 46.88%] [G loss: 0.868321]
10000 [D loss: 0.635190, acc.: 59.38%] [G loss: 0.854181]
11000 [D loss: 0.700397, acc.: 56.25%] [G loss: 0.778778]
12000 [D loss: 0.741978, acc.: 46.88%] [G loss: 0.813542]
13000 [D loss: 0.760614, acc.: 46.88%] [G loss: 0.833507]
14000 [D loss: 0.671199, acc.: 68.75%] [G loss: 0.853395]
15000 [D loss: 0.676217, acc.: 62.50%] [G loss: 0.920993]
16000 [D loss: 0.593898, acc.: 68.75%] [G loss: 0.889001]
17000 [D loss: 0.724363, acc.: 50.00%] [G loss: 0.893431]
18000 [D loss: 0.779740, acc.: 43.75%] [G loss: 0.853765]
19000 [D loss: 0.642237, acc.: 59.38%] [G loss: 0.830348]
20000 [D loss: 0.587237, acc.: 62.50%] [G loss: 0.876839]
21000 [D loss: 0.645381, acc.: 62.50%] [G loss: 0.827465]
22000 [D loss: 0.723597, acc.: 46.88%] [G loss: 0.862281]
23000 [D loss: 0.671319, acc.: 65.62%] [G loss: 0.903444]
24000 [D loss: 0.684801, acc.: 62.50%] [G loss: 0.807403]
25000 [D loss: 0.737355, acc.: 43.75%] [G loss: 0.813877]
26000 [D loss: 0.606201, acc.: 68.75%] [G loss: 0.802509]
27000 [D loss: 0.711020, acc.: 56.25%] [G loss: 0.894887]
28000 [D loss: 0.641023, acc.: 56.25%] [G loss: 0.856079]
29000 [D loss: 0.696889, acc.: 46.88%] [G loss: 0.728626]

可以看到D的判别准确率最终在46%-56%之间,也就是说G网络生成的图片已经真假难分

6 参考资料

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目录
  • 1 GAN简介
  • 2 生成与判别算法
  • 3 GANs原理
  • 4 GANs, Autoencoders and VAEs
  • 5 Keras 实现GAN
  • 6 参考资料
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