跟我学算法-对抗生成网络

对抗生成网络，通过对分别两个矛盾的函数，进行各自的训练，已达到两个函数都能实现各自的最优化，得到的参数就是一个较好的参数

两个对抗函数： 

函数一：是判别损失函数，使得判别式的准确率越来越大, 及self.D1被判断为1， self.D2被判断为0

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

函数二： 是造假损失值，及其self.D2越接近1越好，就是被判断为正确的概率越大越好

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

在下面的程序中我们使用了一个预判别函数

接下来将按照顺序解析

# 设置命令行选项
def parse_args():
    parser = argparse.ArgumentParser()
    # 用于参数的直接输入，默认值用default 表示
    parser.add_argument('--num-steps', type=int, default=1200,
                        help='the number of training step to take')
    parser.add_argument('--batch-size', type=int, default=12,
                        help='the batch size')
    parser.add_argument('--log-every', type=int, default=10,
                        help='print loss after this many steps')

    return parser.parse_args()

主函数

if __name__ == '__main__':
    main(parse_args())

定义主函数

def main(args):
    # 初始化GAN实例
    model = GAN(
        # 产生真实值
        DataDistribution(),
        # 产生虚假值
        GeneratorDistribution(range=8),
        args.num_steps,
        args.batch_size,
        args.log_every,
    )
    model.train()

编写GAN函数

class GAN(object):
    # 初始化变量
    def __init__(self, data, gen, num_steps, batch_size, log_every):
        self.data = data
        self.gen = gen
        self.num_steps = num_steps
        self.batch_size = batch_size
        self.log_every = log_every
        self.mip_hidden_size = 4

        self.learning_rate = 0.03

        self._create_model()
    # 建立模型
    def _create_model(self):
        # 建立预判别模型
        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))
            # 获得预测结果
            D_pre = discriminator(self.pre_input, self.mip_hidden_size)
            # 预测值与真实之间的差异
            self.pre_loss = tf.reduce_mean(tf.square(D_pre - self.pre_labels))
            # 训练缩小预测值与真实值的差异
            self.pre_opt = optimizer(self.pre_loss, None, self.learning_rate)
       # 建立造假模型
        with tf.variable_scope('Gen'):
            # 伪造数据的生成
            self.z = tf.placeholder(tf.float32, shape=(self.batch_size, 1))
            self.G = generator(self.z, self.mip_hidden_size)
        # 建立判别模型
        with tf.variable_scope('Disc') as scope:
            # 对真实值做预测， D1为真实值的概率
            self.x = tf.placeholder(tf.float32, shape=(self.batch_size, 1))
            self.D1 = discriminator(self.x, self.mip_hidden_size)
            # 变量重用
            scope.reuse_variables()
            # 对造假值做预测， D2为预测到造假值的概率
            self.D2 = discriminator(self.G, self.mip_hidden_size)
        # 第一个对抗函数
        self.loss_d = tf.reduce_mean(-tf.log(self.D1) - tf.log(1-self.D2))
        # 第二个对抗函数
        self.loss_g = tf.reduce_mean(-tf.log(self.D2))

        # 打包参数
        self.d_pre_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='D_pre')
        self.d_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='Disc')
        self.g_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='Gen')

        # 获得训练以后的参数
        self.opt_d = optimizer(self.loss_d, self.d_params, self.learning_rate)
        self.opt_g = optimizer(self.loss_g, self.g_params, self.learning_rate)
    # 进行训练
    def train(self):
        with tf.Session() as session:
            # 变量初始化
            tf.global_variables_initializer().run()

            # 进行预处理的训练
            num_pretrain_steps = 1000
            for step in range(num_pretrain_steps):
                # 生成随机值
                d = (np.random.random(self.batch_size) - 0.5) * 10.0
                # 生成随机值的标签
                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))
                })
            # 获得参数
            self.weightsD = session.run(self.d_pre_params)
            # 将d_pre_params 参数拷贝给 d_params
            for i, v in enumerate(self.d_params):
                session.run(v.assign(self.weightsD[i]))

            # 进行两个对抗函数的参数训练
            for step in range(self.num_steps):
               # 第一个对抗函数的训练
                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))
                })
               # 第二个对抗函数的训练
                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))
                })
                # 输出结果   
                if step % self.log_every == 0:
                    print('{}:{}	{}'.format(step, loss_d, loss_g))
               # 迭代一百次或者在最后一次进行画图   
                if step % 100 == 0 or step == self.num_steps - 1:
                    self._plot_distributions(session)

    def _samples(self, session, num_points=10000, num_bins=100):
        xs = np.linspace(-self.gen.range, self.gen.range, num_points)
        bins = np.linspace(-self.gen.range, self.gen.range, num_bins)

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

        # generated samples
        # 造假数据
        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)
                )
            })
        # 返回造假数据
        pg, _ = np.histogram(g, bins=bins, density=True)

        return pd, pg
    # 画图
    def _plot_distributions(self, session):
        pd, pg = self._samples(session)
        p_x = np.linspace(-self.gen.range, self.gen.range, len(pd))
        f, ax = plt.subplots(1)
        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()

定义discriminator函数，D判别式用于判别结果,使用了4层神经网络

def discriminator(input, h_dim):
    h0 = tf.tanh(linear(input, h_dim * 2, 'd0'))
    h1 = tf.tanh(linear(h0, h_dim * 2, 'd1'))
    h2 = tf.tanh(linear(h1, h_dim * 2, scope='d2'))

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

    return h3

定义optimizer函数，用于优化参数

def optimizer(loss, var_list, initial_learning_rate):
    # 学习率衰减系数
    decay = 0.95
    num_decay_steps = 150
    batch = tf.Variable(0)
    #进行学习率的衰减
    learning_rate = tf.train.exponential_decay(
        initial_learning_rate,
        batch,
        num_decay_steps,
        decay,
        staircase= True
    )
    # 对损失函数进行最小化的参数优化
    optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss,
         global_step=batch,
         var_list = var_list,  )
    return optimizer

定义generator,用于生成造假的数据，这里使用两层神经网络

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

定义linear， 用于进行卷积

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