GAN 的 keras 实现

本文结构：

• 什么是 GAN？
• 优点？
• keras 例子？

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什么是 GAN？

GAN，全称为 Generative Adversarial Nets，直译为生成式对抗网络，是一种非监督式模型。

一种应用是生成在原始数据集中不存在的但是却比较合理的数据，还可以拓展一张图片，生成下一帧影像，由简单几笔生成一幅画：

模型：

主要有两部分：

The Generative Model：通过输入任意随机数据，尝试生成一些真实的东西（曲线，图像，声音，文本，…）

The Discriminative Model：试图判定哪些是虚假的数据，来减小对真实数据的误报。

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优点：

Markov chains are never needed
避免了计算复杂度特别高的过程，直接进行采样和推断，应用效率相应提高。

a wide variety of functions can be incorporated into the model
针对不同的任务就可以设计不同类型的损失函数。

can represent very sharp, even degenerate distributions

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Keras 例子：

任务：生成 sin 曲线。

%matplotlib inlineimport osimport randomimport numpy as npimport pandas as pdimport matplotlib.pyplot as pltimport seaborn as snsfrom tqdm import tqdm_notebook as tqdmfrom keras.models import Modelfrom keras.layers import Input, Reshapefrom keras.layers.core import Dense, Activation, Dropout, Flattenfrom keras.layers.normalization import BatchNormalizationfrom keras.layers.convolutional import UpSampling1D, Conv1Dfrom keras.layers.advanced_activations import LeakyReLUfrom keras.optimizers import Adam, SGDfrom keras.callbacks import TensorBoard

1. Generative model:

输入：noise data
输出：尝试生成真实的 sin 数据

def get_generative(G_in, dense_dim=200, out_dim=50, lr=1e-3):    x = Dense(dense_dim)(G_in)    x = Activation('tanh')(x)    G_out = Dense(out_dim, activation='tanh')(x)    G = Model(G_in, G_out)    opt = SGD(lr=lr)    G.compile(loss='binary_crossentropy', optimizer=opt)    return G, G_out

2. Discriminative model：

输出：识别此数据是真实的，还是由 Generative model 生成的

def get_discriminative(D_in, lr=1e-3, drate=.25, n_channels=50, conv_sz=5, leak=.2):    x = Reshape((-1, 1))(D_in)    x = Conv1D(n_channels, conv_sz, activation='relu')(x)    x = Dropout(drate)(x)    x = Flatten()(x)    x = Dense(n_channels)(x)    D_out = Dense(2, activation='sigmoid')(x)    D = Model(D_in, D_out)    dopt = Adam(lr=lr)    D.compile(loss='binary_crossentropy', optimizer=dopt)    return D, D_out

3. chain the two models into a GAN：

set_trainability 的作用是每次训练 generator 时要冻住 discriminator。

def set_trainability(model, trainable=False):    model.trainable = trainable    for layer in model.layers:        layer.trainable = trainabledef make_gan(GAN_in, G, D):    set_trainability(D, False)    x = G(GAN_in)    GAN_out = D(x)    GAN = Model(GAN_in, GAN_out)    GAN.compile(loss='binary_crossentropy', optimizer=G.optimizer)    return GAN, GAN_out

4. Training：

交替训练 discriminator 和 chained GAN，在训练 chained GAN 时要冻住 discriminator 的参数：

def sample_noise(G, noise_dim=10, n_samples=10000):    X = np.random.uniform(0, 1, size=[n_samples, noise_dim])    y = np.zeros((n_samples, 2))    y[:, 1] = 1    return X, ydef train(GAN, G, D, epochs=500, n_samples=10000, noise_dim=10, batch_size=32, verbose=False, v_freq=50):    d_loss = []    g_loss = []    e_range = range(epochs)    if verbose:        e_range = tqdm(e_range)    for epoch in e_range:        X, y = sample_data_and_gen(G, n_samples=n_samples, noise_dim=noise_dim)        set_trainability(D, True)        d_loss.append(D.train_on_batch(X, y))        X, y = sample_noise(G, n_samples=n_samples, noise_dim=noise_dim)        set_trainability(D, False)        g_loss.append(GAN.train_on_batch(X, y))        if verbose and (epoch + 1) % v_freq == 0:            print("Epoch #{}: Generative Loss: {}, Discriminative Loss: {}".format(epoch + 1, g_loss[-1], d_loss[-1]))    return d_loss, g_lossd_loss, g_loss = train(GAN, G, D, verbose=True)

5. Results：

N_VIEWED_SAMPLES = 2data_and_gen, _ = sample_data_and_gen(G, n_samples=N_VIEWED_SAMPLES)pd.DataFrame(np.transpose(data_and_gen[N_VIEWED_SAMPLES:])).rolling(5).mean()[5:].plot()

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学习资料：
https://arxiv.org/pdf/1406.2661.pdf
http://www.rricard.me/machine/learning/generative/adversarial/networks/2017/04/05/gans-part1.html
http://www.rricard.me/machine/learning/generative/adversarial/networks/keras/tensorflow/2017/04/05/gans-part2.html

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