【机器学习】机器学习的基本分类-半监督学习-半监督生成对抗网络（Semi-supervised GANs）

阅读量：

半监督生成对抗网络（Semi-supervised GANs，简称 SGAN）是一种结合生成对抗网络（GAN）和半监督学习的模型，能够在有限标注数据和大量未标注数据的情况下训练分类器。它扩展了传统 GAN 的结构，使得判别器不仅仅用于区分真假样本，还用于对标注样本进行分类。

半监督 GAN 的核心思想

生成器 (Generator)： * 模仿数据分布，生成与真实数据类似的样本。
* 输入为噪声 z im p 。
2.

判别器 (Discriminator)： * 在传统 GAN 中，判别器只是二分类器，用于区分生成样本和真实样本。
* 在 SGAN 中，判别器被改造成多分类器，除了区分真假样本外，还负责对真实数据进行分类（有监督任务）。
3.

未标注数据的利用： * SGAN 将未标注数据作为“真实样本”，它们在训练中被用来提升生成器的质量以及判别器的分类能力。

半监督 GAN 的结构

判别器的输出

判别器 DDD 的输出层被设计为 K+1 个神经元，其中：

前 K 个神经元表示标注样本的 K 个类别概率。
第 K+1 个神经元表示“生成样本”的概率。

损失函数

分类损失（监督）：
$athcal{L}{ext{supervised}} = -athbb{E}{ im p_{ext{data}}} og D_y$

其中 D_y 表示判别器对类别 y 的预测概率。

生成对抗损失：
$athcal{L}{ext{unsupervised}} = -athbb{E}{x im p_{ext{data}}} og - athbb{E}{z im p} og D{K+1}$

其中 $D_{K+1}$ 表示判别器认为样本是生成样本的概率。

生成器损失：
athcal{L}G = -athbb{E}{z im p} og

总损失为上述损失的加权和。

半监督 GAN 的实现

以下是一个使用 TensorFlow/Keras 的 SGAN 实现示例：

复制代码

 import numpy as np

    
 from keras.models import Sequential
    
 from keras.layers import Dense, Flatten, Reshape
    
 from keras.layers import Conv2D, Conv2DTranspose, LeakyReLU, Dropout, BatchNormalization
    
 from keras.optimizers import Adam
    
 from keras.losses import CategoricalCrossentropy
    
  
    
 # 超参数
    
 latent_dim = 100  # 随机噪声维度
    
 image_shape = (28, 28, 1)  # 输入图像形状
    
 num_classes = 11  # 类别数
    
  
    
  
    
 # 创建生成器
    
 def build_generator(latent_dim):
    
     model = Sequential([
    
     Dense(128 * 7 * 7, activation='relu', input_dim=latent_dim),
    
     Reshape((7, 7, 128)),
    
     BatchNormalization(),
    
     Conv2DTranspose(128, kernel_size=4, strides=2, padding='same', activation='relu'),
    
     BatchNormalization(),
    
     Conv2DTranspose(64, kernel_size=4, strides=2, padding='same', activation='relu'),
    
     BatchNormalization(),
    
     Conv2D(1, kernel_size=7, activation='tanh', padding='same')  # 输出形状为 (28, 28, 1)
    
     ])
    
     return model
    
  
    
  
    
 # 创建判别器
    
 def build_discriminator(image_shape, num_classes):
    
     model = Sequential([
    
     Conv2D(64, kernel_size=3, strides=2, padding='same', input_shape=image_shape),
    
     LeakyReLU(alpha=0.2),
    
     Dropout(0.3),
    
     Conv2D(128, kernel_size=3, strides=2, padding='same'),
    
     LeakyReLU(alpha=0.2),
    
     Dropout(0.3),
    
     Flatten(),
    
     Dense(num_classes, activation='softmax')  # 输出类别概率分布
    
     ])
    
     return model
    
  
    
  
    
 # 定义训练过程
    
 def train_sgan(generator, discriminator, latent_dim, X_labeled, y_labeled, X_unlabeled, epochs=10000, batch_size=64):
    
     # 编译判别器
    
     discriminator.compile(optimizer=Adam(learning_rate=0.0002, beta_1=0.5),
    
                       loss=CategoricalCrossentropy(),
    
                       metrics=['accuracy'])
    
  
    
     # 构建生成器-判别器联合模型
    
     discriminator.trainable = False
    
     sgan = Sequential([generator, discriminator])
    
     sgan.compile(optimizer=Adam(learning_rate=0.0002, beta_1=0.5), loss=CategoricalCrossentropy())
    
  
    
     for epoch in range(epochs):
    
     # 生成虚假样本
    
     noise = np.random.normal(0, 1, size=(batch_size, latent_dim))
    
     fake_images = generator.predict(noise)
    
     fake_labels = np.eye(num_classes)[np.random.choice(num_classes, size=batch_size)]
    
  
    
     # 训练生成器
    
     generator_loss = sgan.train_on_batch(noise, fake_labels)
    
  
    
     # 训练判别器
    
     idx = np.random.randint(0, X_unlabeled.shape[0], batch_size)
    
     real_images = X_unlabeled[idx]
    
     real_labels = np.eye(num_classes)[np.random.choice(num_classes, size=batch_size)]
    
  
    
     d_loss_real = discriminator.train_on_batch(real_images, real_labels)
    
     d_loss_fake = discriminator.train_on_batch(fake_images, fake_labels)
    
  
    
     d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
    
  
    
     # 打印日志
    
     if epoch % 100 == 0:
    
         print(
    
             f"Epoch {epoch}/{epochs} | D Loss: {d_loss[0]:.4f}, D Accuracy: {d_loss[1]:.4f} | G Loss: {generator_loss:.4f}")
    
  
    
  
    
 # 数据加载示例（使用 MNIST 数据）
    
 from keras.datasets import mnist
    
 from keras.utils import to_categorical
    
  
    
 (X_train, y_train), (_, _) = mnist.load_data()
    
 X_train = (X_train.astype(np.float32) - 127.5) / 127.5  # 归一化到 [-1, 1]
    
 X_train = np.expand_dims(X_train, axis=-1)  # 转换为 (N, 28, 28, 1) 格式
    
 y_train = to_categorical(y_train, num_classes=num_classes)
    
  
    
 # 拆分有标签和无标签数据
    
 X_labeled = X_train[:1000]
    
 y_labeled = y_train[:1000]
    
 X_unlabeled = X_train[1000:]
    
  
    
 # 初始化模型
    
 generator = build_generator(latent_dim)
    
 discriminator = build_discriminator(image_shape, num_classes)
    
  
    
 # 训练 SGAN
    
 train_sgan(generator, discriminator, latent_dim, X_labeled, y_labeled, X_unlabeled)

部分结果

复制代码

 2/2 [==============================] - 0s 21ms/step

    
 Epoch 0/10000 | D Loss: 2.4036, D Accuracy: 0.0859 | G Loss: 2.4005
    
 2/2 [==============================] - 0s 20ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 34ms/step
    
 2/2 [==============================] - 0s 18ms/step
    
 2/2 [==============================] - 0s 20ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 33ms/step
    
 2/2 [==============================] - 0s 18ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 20ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 24ms/step
    
 2/2 [==============================] - 0s 31ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 33ms/step
    
 2/2 [==============================] - 0s 24ms/step
    
 2/2 [==============================] - 0s 23ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 18ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 25ms/step
    
 2/2 [==============================] - 0s 27ms/step
    
 2/2 [==============================] - 0s 18ms/step
    
 2/2 [==============================] - 0s 34ms/step
    
 2/2 [==============================] - 0s 18ms/step
    
 2/2 [==============================] - 0s 20ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 20ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 18ms/step
    
 2/2 [==============================] - 0s 35ms/step
    
 2/2 [==============================] - 0s 18ms/step
    
 2/2 [==============================] - 0s 20ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 21ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 24ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 20ms/step
    
 2/2 [==============================] - 0s 36ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 18ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 18ms/step
    
 2/2 [==============================] - 0s 20ms/step
    
 2/2 [==============================] - 0s 20ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 39ms/step
    
 2/2 [==============================] - 0s 22ms/step
    
 2/2 [==============================] - 0s 28ms/step
    
 2/2 [==============================] - 0s 20ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 23ms/step
    
 2/2 [==============================] - 0s 22ms/step
    
 2/2 [==============================] - 0s 24ms/step
    
 2/2 [==============================] - 0s 55ms/step
    
 2/2 [==============================] - 0s 20ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 23ms/step
    
 2/2 [==============================] - 0s 20ms/step
    
 2/2 [==============================] - 0s 18ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 20ms/step
    
 2/2 [==============================] - 0s 22ms/step
    
 2/2 [==============================] - 0s 33ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 21ms/step
    
 2/2 [==============================] - 0s 30ms/step
    
 2/2 [==============================] - 0s 18ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 34ms/step
    
 2/2 [==============================] - 0s 26ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 20ms/step
    
 2/2 [==============================] - 0s 20ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 34ms/step
    
 2/2 [==============================] - 0s 47ms/step
    
 2/2 [==============================] - 0s 29ms/step
    
 2/2 [==============================] - 0s 18ms/step
    
 2/2 [==============================] - 0s 20ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 2/2 [==============================] - 0s 31ms/step
    
 2/2 [==============================] - 0s 19ms/step
    
 Epoch 100/10000 | D Loss: 2.3941, D Accuracy: 0.1250 | G Loss: 2.3827
    
 2/2 [==============================] - 0s 20ms/step
    
 2/2 [==============================] - 0s 19ms/step

总结

半监督 GAN 的核心在于将判别器扩展为多分类器，充分利用未标注数据和生成样本的对抗训练，提升分类器性能。相比传统的 GAN 和全监督学习方法，SGAN 能在标注数据不足的情况下取得更好的分类效果。

全部评论 (0)

还没有任何评论哟~

【机器学习】机器学习的基本分类-半监督学习-半监督生成对抗网络（Semi-supervised GANs）

半监督生成对抗网络（SemisupervisedGANs，简称SGAN）是一种结合生成对抗网络（GAN）和半监督学习的模型，能够在有限标注数据和大量未标注数据的情况下训练分类器。它扩展了传统GAN的结...

半监督学习 - 半监督生成对抗网络（Semi-Supervised GANs）

半监督生成对抗网络（SemiSupervisedGANs）是一种结合生成对抗网络（GANs）和半监督学习的方法。在这种方法中，通过同时使用有标签和无标签的数据，模型旨在提高生成模型和判别模型的性能，以...

【机器学习】机器学习的基本分类-半监督学习（Semi-supervised Learning）

半监督学习是一种介于监督学习和无监督学习之间的机器学习方法。它利用少量的标注数据（有监督数据）和大量的未标注数据（无监督数据）来进行模型训练，从而在标注数据不足的情况下，提升模型的性能。半监督学习的...

机器学习的分类——半监督学习（Semi-supervised Learning）

半监督学习（SemisupervisedLearning）是介于监督学习和无监督学习之间的一种机器学习方法。它主要用于那些有大量未标记数据和少量标记数据的场景。半监督学习试图通过这两种类型的数据来改进...

机器学习16：半监督学习semi-supervised

一、whysemisupervised dataset中只有部分数据进行了lable标注，即，有的数据成对出现｛输入，输出｝，有的数据只有输入｛输入｝； Transductivelearning（直推...

机器学习-算法-半监督学习：半监督学习（Semi-supervised Learning）算法

人工智能机器学习算法半监督学习：半监督学习（SemisupervisedLearning）算法一、半监督学习算法提出的背景 1、监督学习算法 2、无监督学习算法 3、监督学习的特征选择方法 4、无监...

【机器学习】机器学习的基本分类-半监督学习ーSemi-supervised SVM

SemisupervisedSVM（S³VM） SemisupervisedSVMS³VM是一种半监督支持向量机方法，旨在结合标注数据和未标注数据训练模型。相比传统的监督学习SVM，它在优化过程中利用...

机器学习10 -- 半监督学习 Semi-supervised Learning

1为什么要做半监督学习有监督机器学习已经在很多领域证明了它的有效性，比如ImageNet图像分类任务，深度学习模型早在2017年，准确率就已经超过了人类。机器学习被认为是一门数据驱动的学科，数据分为...

半监督学习 - 半监督聚类（Semi-Supervised Clustering）

什么是机器学习半监督聚类是一种集成了有标签数据和无标签数据的聚类方法，其目标是在聚类的过程中利用有标签数据的信息来提高聚类性能。在半监督聚类中，一部分数据集有已知的标签，而另一部分没有标签。

半监督生成对抗网络_生成对抗网络（GAN）的半监督学习

前言如果您曾经听说过或研究过深度学习，那么您可能就知道MNIST,SVHN,ImageNet,PascalVoc或者其他数据集。这些数据集都有一个共同点：它们由成千上万个有标签的数据组成。换句话说，...

是否确定退出登录?

【机器学习】机器学习的基本分类-半监督学习-半监督生成对抗网络（Semi-supervised GANs）

半监督 GAN 的核心思想

半监督 GAN 的结构

判别器的输出

损失函数

半监督 GAN 的实现

总结

全部评论 (0)

相关文章推荐

【机器学习】机器学习的基本分类-半监督学习-半监督生成对抗网络（Semi-supervised GANs）

半监督学习 - 半监督生成对抗网络（Semi-Supervised GANs）

【机器学习】机器学习的基本分类-半监督学习（Semi-supervised Learning）

机器学习的分类——半监督学习（Semi-supervised Learning）

机器学习16：半监督学习semi-supervised

机器学习-算法-半监督学习：半监督学习（Semi-supervised Learning）算法

【机器学习】机器学习的基本分类-半监督学习ーSemi-supervised SVM

机器学习10 -- 半监督学习 Semi-supervised Learning

半监督学习 - 半监督聚类（Semi-Supervised Clustering）

半监督生成对抗网络_生成对抗网络（GAN）的半监督学习