学习笔记TF051:生成式对抗网络

生成式对抗网络(gennerative adversarial network,GAN)，谷歌2014年提出网络模型。灵感自二人博弈的零和博弈，目前最火的非监督深度学习。GAN之父，Ian J.Goodfellow，公认人工智能顶级专家。

原理。
生成式对搞网络包含一个生成模型(generative model,G)和一个判别模型(discriminative model,D)。Ian J.Goodfellow、Jean Pouget-Abadie、Mehdi Mirza、Bing Xu、David Warde-Farley、Sherjil Ozair、Aaron Courville、Yoshua Bengio论文，《Generative Adversarial Network》，https://arxiv.org/abs/1406.2661 。
生成式对抗网络结构：
噪声数据->生成模型->假图片---|
|->判别模型->真/假
打乱训练数据->训练集->真图片-|
生成式对抗网络主要解决如何从训练样本中学习出新样本。生成模型负责训练出样本的分布，如果训练样本是图片就生成相似的图片，如果训练样本是文章名子就生成相似的文章名子。判别模型是一个二分类器，用来判断输入样本是真实数据还是训练生成的样本。
生成式对抗网络优化，是一个二元极小极大博弈(minimax two-player game)问题。使生成模型输出在输入给判别模型时，判断模型秀难判断是真实数据还是虚似数据。训练好的生成模型，能把一个噪声向量转化成和训练集类似的样本。Argustus Odena、Christopher Olah、Jonathon Shlens论文《Coditional Image Synthesis with Auxiliary Classifier GANs》。
辅助分类器生成式对抗网络(auxiliary classifier GAN，AC-GAN)实现。

生成式对抗网络应用。生成数字，生成人脸图像。

生成式对抗网络实现。https://github.com/fchollet/keras/blob/master/examples/mnist_acgan.py 。
Augustus Odena、Chistopher Olah和Jonathon Shlens 论文《Conditional Image Synthesis With Auxiliary Classifier GANs》。
通过噪声，让生成模型G生成虚假数据，和真实数据一起送到判别模型D，判别模型一方面输出数据真/假,一方面输出图片分类。
首先定义生成模型，目的是生成一对(z,L)数据，z是噪声向量，L是(1,28,28)的图像空间。

def build_generator(latent_size):
cnn = Sequential()
cnn.add(Dense(1024, input_dim=latent_size, activation='relu'))
cnn.add(Dense(128 * 7 * 7, activation='relu'))
cnn.add(Reshape((128, 7, 7)))
#上采样，图你尺寸变为 14X14
cnn.add(UpSampling2D(size=(2,2)))
cnn.add(Convolution2D(256, 5, 5, border_mode='same', activation='relu', init='glorot_normal'))
#上采样，图像尺寸变为28X28
cnn.add(UpSampling2D(size=(2,2)))
cnn.add(Convolution2D(128, 5, 5, border_mode='same', activation='relu', init='glorot_normal'))
#规约到1个通道
cnn.add(Convolution2D(1, 2, 2, border_mode='same', activation='tanh', init='glorot_normal'))
#生成模型输入层,特征向量
latent = Input(shape=(latent_size, ))
#生成模型输入层，标记
image_class = Input(shape=(1,), dtype='int32')
cls = Flatten()(Embedding(10, latent_size, init='glorot_normal')(image_class))
h = merge([latent, cls], mode='mul')
fake_image = cnn(h) #输出虚假图片
return Model(input=[latent, image_class], output=fake_image)
定义判别模型，输入(1,28,28)图片，输出两个值，一个是判别模型认为这张图片是否是虚假图片，另一个是判别模型认为这第图片所属分类。

def build_discriminator();
#采用激活函数Leaky ReLU来替换标准的卷积神经网络中的激活函数
cnn = Wequential()
cnn.add(Convolution2D(32, 3, 3, border_mode='same', subsample=(2, 2), input_shape=(1, 28, 28)))
cnn.add(LeakyReLU())
cnn.add(Dropout(0.3))
cnn.add(Convolution2D(64, 3, 3, border_mode='same', subsample=(1, 1)))
cnn.add(LeakyReLU())
cnn.add(Dropout(0.3))
cnn.add(Convolution2D(128, 3, 3, border_mode='same', subsample=(1, 1)))
cnn.add(LeakyReLU())
cnn.add(Dropout(0.3))
cnn.add(Convolution2D(256, 3, 3, border_mode='same', subsample=(1, 1)))
cnn.add(LeakyReLU())
cnn.add(Dropout(0.3))
cnn.add(Flatten())
image = Input(shape=(1, 28, 28))
features = cnn(image)
#有两个输出
#输出真假值，范围在0~1
fake = Dense(1, activation='sigmoid',name='generation')(features)
#辅助分类器，输出图片分类
aux = Dense(10, activation='softmax', name='auxiliary')(features)
return Model(input=image, output=[fake, aux])
训练过程，50轮(epoch),把权重保存，每轮把虚假数据生成图处保存，观察虚假数据演化过程。

if __name__ =='__main__':
#定义超参数
nb_epochs = 50
batch_size = 100
latent_size = 100
#优化器学习率
adam_lr = 0.0002
adam_beta_l = 0.5
#构建判别网络
discriminator = build_discriminator()
discriminator.compile(optimizer=adam(lr=adam_lr, beta_l=adam_beta_l), loss='binary_crossentropy')
latent = Input(shape=(lastent_size, ))
image_class = Input(shape-(1, ), dtype='int32')
#生成组合模型
discriminator.trainable = False
fake, aux = discriminator(fake)
combined = Model(input=[latent, image_class], output=[fake, aux])
combined.compile(optimizer=Adam(lr=adam_lr, beta_l=adam_beta_1), loss=['binary_crossentropy', 'sparse_categorical_crossentropy'])
#将mnist数据转化为(...,1,28,28)维度,取值范围为[-1,1]
(X_train,y_train),(X_test,y_test) = mnist.load_data()
X_train = (X_train.astype(np.float32) - 127.5) / 127.5
X_train = np.expand_dims(X_train, axis=1)
X_test = (X_test.astype(np.float32) - 127.5) / 127.5
X_test = np.expand_dims(X_test, axis=1)
num_train, num_test = X_train.shape[0], X_test.shape[0]
train_history = defaultdict(list)
test_history = defaultdict(list)
for epoch in range(epochs):
print('Epoch {} of {}'.format(epoch + 1, epochs))
num_batches = int(X_train.shape[0] / batch_size)
progress_bar = Progbar(target=num_batches)
epoch_gen_loss = []
epoch_disc_loss = []
for index in range(num_batches):
progress_bar.update(index)
#产生一个批次的噪声数据
noise = np.random.uniform(-1, 1, (batch_size, latent_size))
# 获取一个批次的真实数据
image_batch = X_train[index * batch_size:(index + 1) * batch_size]
label_batch = y_train[index * batch_size:(index + 1) * batch_size]
# 生成一些噪声标记
sampled_labels = np.random.randint(0, 10, batch_size)
# 产生一个批次的虚假图片
generated_images = generator.predict(
[noise, sampled_labels.reshape((-1, 1))], verbose=0)
X = np.concatenate((image_batch, generated_images))
y = np.array([1] * batch_size + [0] * batch_size)
aux_y = np.concatenate((label_batch, sampled_labels), axis=0)
epoch_disc_loss.append(discriminator.train_on_batch(X, [y, aux_y]))
# 产生两个批次噪声和标记
noise = np.random.uniform(-1, 1, (2 * batch_size, latent_size))
sampled_labels = np.random.randint(0, 10, 2 * batch_size)
# 训练生成模型来欺骗判别模型，输出真/假都设为真
trick = np.ones(2 * batch_size)
epoch_gen_loss.append(combined.train_on_batch(
[noise, sampled_labels.reshape((-1, 1))],
[trick, sampled_labels]))
print('\nTesting for epoch {}:'.format(epoch + 1))
# 评估测试集，产生一个新批次噪声数据
noise = np.random.uniform(-1, 1, (num_test, latent_size))
sampled_labels = np.random.randint(0, 10, num_test)
generated_images = generator.predict(
[noise, sampled_labels.reshape((-1, 1))], verbose=False)
X = np.concatenate((X_test, generated_images))
y = np.array([1] * num_test + [0] * num_test)
aux_y = np.concatenate((y_test, sampled_labels), axis=0)
# 判别模型是否能判别
discriminator_test_loss = discriminator.evaluate(
X, [y, aux_y], verbose=False)
discriminator_train_loss = np.mean(np.array(epoch_disc_loss), axis=0)
# 创建两个批次新噪声数据
noise = np.random.uniform(-1, 1, (2 * num_test, latent_size))
sampled_labels = np.random.randint(0, 10, 2 * num_test)
trick = np.ones(2 * num_test)
generator_test_loss = combined.evaluate(
[noise, sampled_labels.reshape((-1, 1))],
[trick, sampled_labels], verbose=False)
generator_train_loss = np.mean(np.array(epoch_gen_loss), axis=0)
# 损失值等性能指标记录下来，并输出
train_history['generator'].append(generator_train_loss)
train_history['discriminator'].append(discriminator_train_loss)
test_history['generator'].append(generator_test_loss)
test_history['discriminator'].append(discriminator_test_loss)
print('{0:<22s} | {1:4s} | {2:15s} | {3:5s}'.format(
'component', *discriminator.metrics_names))
print('-' * 65)
ROW_FMT = '{0:<22s} | {1:<4.2f} | {2:<15.2f} | {3:<5.2f}'
print(ROW_FMT.format('generator (train)',
*train_history['generator'][-1]))
print(ROW_FMT.format('generator (test)',
*test_history['generator'][-1]))
print(ROW_FMT.format('discriminator (train)',
*train_history['discriminator'][-1]))
print(ROW_FMT.format('discriminator (test)',
*test_history['discriminator'][-1]))
# 每个epoch保存一次权重
generator.save_weights(
'params_generator_epoch_{0:03d}.hdf5'.format(epoch), True)
discriminator.save_weights(
'params_discriminator_epoch_{0:03d}.hdf5'.format(epoch), True)
# 生成一些可视化虚假数字看演化过程
noise = np.random.uniform(-1, 1, (100, latent_size))
sampled_labels = np.array([
[i] * 10 for i in range(10)
]).reshape(-1, 1)
generated_images = generator.predict(
[noise, sampled_labels], verbose=0)
# 整理到一个方格
img = (np.concatenate([r.reshape(-1, 28)
for r in np.split(generated_images, 10)
], axis=-1) * 127.5 + 127.5).astype(np.uint8)
Image.fromarray(img).save(
'plot_epoch_{0:03d}_generated.png'.format(epoch))
pickle.dump({'train': train_history, 'test': test_history},
open('acgan-history.pkl', 'wb'))

训练结束，创建3类文件。params_discriminator_epoch_{{epoch_number}}.hdf5,判别模型权重参数。params_generator_epoch_{{epoch_number}}.hdf5,生成模型权重参数。plot_epoch_{{epoch_number}}_generated.png 。

生成式对抗网络改进。生成式对抗网络(generative adversarial network,GAN)在无监督学习非常有效。常规生成式对抗网络判别器使用Sigmoid交叉熵损失函数，学习过程梯度消失。Wasserstein生成式对抗网络(Wasserstein generative adversarial network,WGAN)，使用Wasserstein距离度量，而不是Jensen-Shannon散度(Jensen-Shannon divergence,JSD)。使用最小二乘生成式对抗网络(least squares generative adversarial network,LSGAN)，判别模型用最小平方损失小函数(least squares loss function)。Sebastian Nowozin、Botond Cseke、Ryota Tomioka论文《f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization》。

参考资料：
《TensorFlow技术解析与实战》

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