来自: https://ishmaelbelghazi.github.io/ALI

手把手教你写一个生成对抗网络 — 生成对抗网络代码全解析, 详细代码解析(TensorFlow, numpy, matplotlib, scipy)

今天我们接着上一讲“#9 生成对抗网络101 终极入门与通俗解析”, 手把手教你写一个生成对抗网络。参考代码是:https://github.com/AYLIEN/gan-intro

关键python库: TensorFlow, numpy, matplotlib, scipy

我们上次讲过,生成对抗网络同时训练两个模型, 叫做生成器判断器. 生成器竭尽全力模仿真实分布生成数据; 判断器竭尽全力区分出真实样本和生成器生成的模仿样本. 直到判断器无法区分出真实样本和模仿样本为止.

out
来自:http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/

上图是一个生成对抗网络的训练过程,我们所要讲解的代码就是要实现这样的训练过程。
其中, 绿色线的分布是一个高斯分布(真实分布),期望和方差都是固定值,所以分布稳定。红色线的分布是生成器分布,他在训练过程中与判断器对抗,不断改变分布模仿绿色线高斯分布. 整个过程不断模仿绿色线蓝色线的分布就是判断器,约定为, 概率密度越高, 认为真实数据的可能性越大. 可以看到蓝线在真实数据期望4的地方,蓝色线概率密度越来越小, 即, 判断器难区分出生成器和判断器.

接下来我们来啃一下David 9看过最复杂的TensorFlow源码逻辑:

首先看总体逻辑:

来自: https://ishmaelbelghazi.github.io/ALI
来自: https://ishmaelbelghazi.github.io/ALI

正像之前所说, 有两个神经模型在交替训练. 生成模型输入噪声分布, 把噪声分布映射成很像真实分布的分布, 生成仿造的样本. 判断模型输入生成模型的仿造样本, 区分这个样本是不真实样本. 如果最后区分不出, 恭喜你, 模型训练的很不错.

我们的生成器模型映射作用很像下图:

screenshot-from-2016-11-12-171611

Z是一个平均分布加了点噪声而已.  X是真实分布. 我们希望这个神经网络输入相同间隔的输入值 , 输出就能告诉我们这个值的概率密度(pdf)多大? 很显然-1这里pdf应该比较大.

Z如何写代码? 很简单:

class GeneratorDistribution(object):
    def __init__(self, range):
        self.range = range

    def sample(self, N):
        return np.linspace(-self.range, self.range, N) + \
            np.random.random(N) * 0.01

查不多采样值像下图:

screenshot-from-2016-11-12-172319

只是多了一点点噪声而已.

生成器用一层线性, 加一层非线性, 最后加一层线性的神经网络.

判断器需要强大一些, 用三层线神经网络去做:

def discriminator(input, hidden_size):
    h0 = tf.tanh(linear(input, hidden_size * 2, 'd0'))
    h1 = tf.tanh(linear(h0, hidden_size * 2, 'd1'))
    h2 = tf.tanh(linear(h1, hidden_size * 2, 'd2'))
    h3 = tf.sigmoid(linear(h2, 1, 'd3'))
    return h3

然后, 我们构造TensorFlow图, 还有判断器和生成器的损失函数:

with tf.variable_scope('G'):
    z = tf.placeholder(tf.float32, shape=(None, 1))
    G = generator(z, hidden_size)

with tf.variable_scope('D') as scope:
    x = tf.placeholder(tf.float32, shape=(None, 1))
    D1 = discriminator(x, hidden_size)
    scope.reuse_variables()
    D2 = discriminator(G, hidden_size)

loss_d = tf.reduce_mean(-tf.log(D1) - tf.log(1 - D2))
loss_g = tf.reduce_mean(-tf.log(D2))

最神奇的应该是这句:

loss_d = tf.reduce_mean(-tf.log(D1) - tf.log(1 - D2))

我们有同样的一个判断模型, D1和D2的区别仅仅是D1的输入是真实数据, D2的输入是生成器的伪造数据. 注意, 代码中判断模型的输出是“认为一个样本在真实分布中的可能性”. 所以优化时目标是, D1的输出要尽量大, D2的输出要尽量小.

此外, 优化生成器的时候, 我们要欺骗判断器, 让D2的输出尽量大:

loss_g = tf.reduce_mean(-tf.log(D2))

最难的难点, David 9 给大家已经讲解了. 如何写优化器(optimizer)和训练过程, 请大家参考源代码~

源代码:

'''
An example of distribution approximation using Generative Adversarial Networks in TensorFlow.

Based on the blog post by Eric Jang: http://blog.evjang.com/2016/06/generative-adversarial-nets-in.html,
and of course the original GAN paper by Ian Goodfellow et. al.: https://arxiv.org/abs/1406.2661.

The minibatch discrimination technique is taken from Tim Salimans et. al.: https://arxiv.org/abs/1606.03498.
'''
from __future__ import absolute_import
from __future__ import print_function
from __future__ import unicode_literals
from __future__ import division

import argparse
import numpy as np
from scipy.stats import norm
import tensorflow as tf
import matplotlib.pyplot as plt
from matplotlib import animation
import seaborn as sns

sns.set(color_codes=True)

seed = 42
np.random.seed(seed)
tf.set_random_seed(seed)


class DataDistribution(object):
    def __init__(self):
        self.mu = 4
        self.sigma = 0.5

    def sample(self, N):
        samples = np.random.normal(self.mu, self.sigma, N)
        samples.sort()
        return samples


class GeneratorDistribution(object):
    def __init__(self, range):
        self.range = range

    def sample(self, N):
        return np.linspace(-self.range, self.range, N) + \
            np.random.random(N) * 0.01


def linear(input, output_dim, scope=None, stddev=1.0):
    norm = tf.random_normal_initializer(stddev=stddev)
    const = tf.constant_initializer(0.0)
    with tf.variable_scope(scope or 'linear'):
        w = tf.get_variable('w', [input.get_shape()[1], output_dim], initializer=norm)
        b = tf.get_variable('b', [output_dim], initializer=const)
        return tf.matmul(input, w) + b


def generator(input, h_dim):
    h0 = tf.nn.softplus(linear(input, h_dim, 'g0'))
    h1 = linear(h0, 1, 'g1')
    return h1


def discriminator(input, h_dim, minibatch_layer=True):
    h0 = tf.tanh(linear(input, h_dim * 2, 'd0'))
    h1 = tf.tanh(linear(h0, h_dim * 2, 'd1'))

    # without the minibatch layer, the discriminator needs an additional layer
    # to have enough capacity to separate the two distributions correctly
    if minibatch_layer:
        h2 = minibatch(h1)
    else:
        h2 = tf.tanh(linear(h1, h_dim * 2, scope='d2'))

    h3 = tf.sigmoid(linear(h2, 1, scope='d3'))
    return h3


def minibatch(input, num_kernels=5, kernel_dim=3):
    x = linear(input, num_kernels * kernel_dim, scope='minibatch', stddev=0.02)
    activation = tf.reshape(x, (-1, num_kernels, kernel_dim))
    diffs = tf.expand_dims(activation, 3) - tf.expand_dims(tf.transpose(activation, [1, 2, 0]), 0)
    eps = tf.expand_dims(np.eye(int(input.get_shape()[0]), dtype=np.float32), 1)
    abs_diffs = tf.reduce_sum(tf.abs(diffs), 2) + eps
    minibatch_features = tf.reduce_sum(tf.exp(-abs_diffs), 2)
    return tf.concat(1, [input, minibatch_features])


def optimizer(loss, var_list):
    initial_learning_rate = 0.005
    decay = 0.95
    num_decay_steps = 150
    batch = tf.Variable(0)
    learning_rate = tf.train.exponential_decay(
        initial_learning_rate,
        batch,
        num_decay_steps,
        decay,
        staircase=True
    )
    optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(
        loss,
        global_step=batch,
        var_list=var_list
    )
    return optimizer


class GAN(object):
    def __init__(self, data, gen, num_steps, batch_size, minibatch, log_every, anim_path):
        self.data = data
        self.gen = gen
        self.num_steps = num_steps
        self.batch_size = batch_size
        self.minibatch = minibatch
        self.log_every = log_every
        self.mlp_hidden_size = 4
        self.anim_path = anim_path
        self.anim_frames = []
        self._create_model()

    def _create_model(self):
        # In order to make sure that the discriminator is providing useful gradient
        # information to the generator from the start, we're going to pretrain the
        # discriminator using a maximum likelihood objective. We define the network
        # for this pretraining step scoped as D_pre.
        with tf.variable_scope('D_pre'):
            self.pre_input = tf.placeholder(tf.float32, shape=(self.batch_size, 1))
            self.pre_labels = tf.placeholder(tf.float32, shape=(self.batch_size, 1))
            D_pre = discriminator(self.pre_input, self.mlp_hidden_size, self.minibatch)
            self.pre_loss = tf.reduce_mean(tf.square(D_pre - self.pre_labels))
            self.pre_opt = optimizer(self.pre_loss, None)

        # This defines the generator network - it takes samples from a noise
        # distribution as input, and passes them through an MLP.
        with tf.variable_scope('G'):
            self.z = tf.placeholder(tf.float32, shape=(self.batch_size, 1))
            self.G = generator(self.z, self.mlp_hidden_size)

        # The discriminator tries to tell the difference between samples from the
        # true data distribution (self.x) and the generated samples (self.z).
        #
        # Here we create two copies of the discriminator network (that share parameters),
        # as you cannot use the same network with different inputs in TensorFlow.
        with tf.variable_scope('D') as scope:
            self.x = tf.placeholder(tf.float32, shape=(self.batch_size, 1))
            self.D1 = discriminator(self.x, self.mlp_hidden_size, self.minibatch)
            scope.reuse_variables()
            self.D2 = discriminator(self.G, self.mlp_hidden_size, self.minibatch)


        # Define the loss for discriminator and generator networks (see the original
        # paper for details), and create optimizers for both
        #self.pre_loss = tf.reduce_mean(tf.square(D_pre - self.pre_labels))
        self.loss_d = tf.reduce_mean(-tf.log(self.D1) - tf.log(1 - self.D2))
        self.loss_g = tf.reduce_mean(-tf.log(self.D2))

        vars = tf.trainable_variables()
        self.d_pre_params = [v for v in vars if v.name.startswith('D_pre/')]
        self.d_params = [v for v in vars if v.name.startswith('D/')]
        self.g_params = [v for v in vars if v.name.startswith('G/')]

        #self.pre_opt = optimizer(self.pre_loss, self.d_pre_params)
        self.opt_d = optimizer(self.loss_d, self.d_params)
        self.opt_g = optimizer(self.loss_g, self.g_params)

    def train(self):
        with tf.Session() as session:
            tf.initialize_all_variables().run()

            # pretraining discriminator
            num_pretrain_steps = 1000
            for step in xrange(num_pretrain_steps):
                d = (np.random.random(self.batch_size) - 0.5) * 10.0
                labels = norm.pdf(d, loc=self.data.mu, scale=self.data.sigma)
                pretrain_loss, _ = session.run([self.pre_loss, self.pre_opt], {
                    self.pre_input: np.reshape(d, (self.batch_size, 1)),
                    self.pre_labels: np.reshape(labels, (self.batch_size, 1))
                })
            self.weightsD = session.run(self.d_pre_params)

            # copy weights from pre-training over to new D network
            for i, v in enumerate(self.d_params):
                session.run(v.assign(self.weightsD[i]))

            for step in xrange(self.num_steps):
                # update discriminator
                x = self.data.sample(self.batch_size)
                z = self.gen.sample(self.batch_size)
                loss_d, _ = session.run([self.loss_d, self.opt_d], {
                    self.x: np.reshape(x, (self.batch_size, 1)),
                    self.z: np.reshape(z, (self.batch_size, 1))
                })

                # update generator
                z = self.gen.sample(self.batch_size)
                loss_g, _ = session.run([self.loss_g, self.opt_g], {
                    self.z: np.reshape(z, (self.batch_size, 1))
                })

                if step % self.log_every == 0:
                    #pass
                    print('{}: {}\t{}'.format(step, loss_d, loss_g))

                if self.anim_path:
                    self.anim_frames.append(self._samples(session))

            if self.anim_path:
                self._save_animation()
            else:
                self._plot_distributions(session)

    def _samples(self, session, num_points=10000, num_bins=100):
        '''
        Return a tuple (db, pd, pg), where db is the current decision
        boundary, pd is a histogram of samples from the data distribution,
        and pg is a histogram of generated samples.
        '''
        xs = np.linspace(-self.gen.range, self.gen.range, num_points)
        bins = np.linspace(-self.gen.range, self.gen.range, num_bins)

        # decision boundary
        db = np.zeros((num_points, 1))
        for i in range(num_points // self.batch_size):
            db[self.batch_size * i:self.batch_size * (i + 1)] = session.run(self.D1, {
                self.x: np.reshape(
                    xs[self.batch_size * i:self.batch_size * (i + 1)],
                    (self.batch_size, 1)
                )
            })

        # data distribution
        d = self.data.sample(num_points)
        pd, _ = np.histogram(d, bins=bins, density=True)

        # generated samples
        zs = np.linspace(-self.gen.range, self.gen.range, num_points)
        g = np.zeros((num_points, 1))
        for i in range(num_points // self.batch_size):
            g[self.batch_size * i:self.batch_size * (i + 1)] = session.run(self.G, {
                self.z: np.reshape(
                    zs[self.batch_size * i:self.batch_size * (i + 1)],
                    (self.batch_size, 1)
                )
            })
        pg, _ = np.histogram(g, bins=bins, density=True)

        return db, pd, pg

    def _plot_distributions(self, session):
        db, pd, pg = self._samples(session)
        db_x = np.linspace(-self.gen.range, self.gen.range, len(db))
        p_x = np.linspace(-self.gen.range, self.gen.range, len(pd))
        f, ax = plt.subplots(1)
        ax.plot(db_x, db, label='decision boundary')
        ax.set_ylim(0, 1)
        plt.plot(p_x, pd, label='real data')
        plt.plot(p_x, pg, label='generated data')
        plt.title('1D Generative Adversarial Network')
        plt.xlabel('Data values')
        plt.ylabel('Probability density')
        plt.legend()
        plt.show()

    def _save_animation(self):
        f, ax = plt.subplots(figsize=(6, 4))
        f.suptitle('1D Generative Adversarial Network', fontsize=15)
        plt.xlabel('Data values')
        plt.ylabel('Probability density')
        ax.set_xlim(-6, 6)
        ax.set_ylim(0, 1.4)
        line_db, = ax.plot([], [], label='decision boundary')
        line_pd, = ax.plot([], [], label='real data')
        line_pg, = ax.plot([], [], label='generated data')
        frame_number = ax.text(
            0.02,
            0.95,
            '',
            horizontalalignment='left',
            verticalalignment='top',
            transform=ax.transAxes
        )
        ax.legend()

        db, pd, _ = self.anim_frames[0]
        db_x = np.linspace(-self.gen.range, self.gen.range, len(db))
        p_x = np.linspace(-self.gen.range, self.gen.range, len(pd))

        def init():
            line_db.set_data([], [])
            line_pd.set_data([], [])
            line_pg.set_data([], [])
            frame_number.set_text('')
            return (line_db, line_pd, line_pg, frame_number)

        def animate(i):
            frame_number.set_text(
                'Frame: {}/{}'.format(i, len(self.anim_frames))
            )
            db, pd, pg = self.anim_frames[i]
            line_db.set_data(db_x, db)
            line_pd.set_data(p_x, pd)
            line_pg.set_data(p_x, pg)
            return (line_db, line_pd, line_pg, frame_number)

        anim = animation.FuncAnimation(
            f,
            animate,
            init_func=init,
            frames=len(self.anim_frames),
            blit=True
        )
        anim.save(self.anim_path, fps=30, extra_args=['-vcodec', 'libx264'])


def main(args):
    model = GAN(
        DataDistribution(),
        GeneratorDistribution(range=8),
        args.num_steps,
        args.batch_size,
        args.minibatch,
        args.log_every,
        args.anim
    )
    model.train()


def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument('--num-steps', type=int, default=1200,
                        help='the number of training steps to take')
    parser.add_argument('--batch-size', type=int, default=12,
                        help='the batch size')
    parser.add_argument('--minibatch', type=bool, default=False,
                        help='use minibatch discrimination')
    parser.add_argument('--log-every', type=int, default=10,
                        help='print loss after this many steps')
    parser.add_argument('--anim', type=str, default=None,
                        help='name of the output animation file (default: none)')
    return parser.parse_args()


if __name__ == '__main__':
    '''
    data_sample = DataDistribution()
    d = data_sample.sample(10)
    print(d)
    '''
    main(parse_args())

 

参考文献:

  1. An introduction to Generative Adversarial Networks (with code in TensorFlow)
  2. Generative Adversarial Nets in TensorFlow (Part I)

发布者

David 9

David 9

邮箱:yanchao727@gmail.com 微信: david9ml

《手把手教你写一个生成对抗网络 — 生成对抗网络代码全解析, 详细代码解析(TensorFlow, numpy, matplotlib, scipy)》有3个想法

  1. 我们要欺骗判断器, 让D2的输出尽量大 ; loss_d = tf.reduce_mean(-tf.log(D1) – tf.log(1 – D2)) ;loss_g = tf.reduce_mean(-tf.log(D2)) ….. 这里不是很明白啊,作者可不可以再解释一下啊?

    1. 判别器和生成器的目标函数不同.
      判别器目标是, 区分真实数据和伪造的生成数据.
      而, 生成器的目标是, 让未来生成的数据更像真实数据, 所以, 它要使得D2的输出尽量大就可以了.
      如还有问题, 请联系微信yanchao727727或者邮箱: yanchao727@gmail.com

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