GAN 的 keras 实现
本文结构：
- 什么是 GAN？
- 优点？
- keras 例子？
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。
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
Keras 例子：
任务：生成 sin 曲线。
%matplotlib inline
import os
import random
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from tqdm import tqdm_notebook as tqdm
from keras.models import Model
from keras.layers import Input, Reshape
from keras.layers.core import Dense, Activation, Dropout, Flatten
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import UpSampling1D, Conv1D
from keras.layers.advanced_activations import LeakyReLU
from keras.optimizers import Adam, SGD
from keras.callbacks import TensorBoard1. 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_out2. 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_out3. 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 = trainable
def 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_out4. 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, y
def 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_loss
d_loss, g_loss = train(GAN, G, D, verbose=True)5. Results：
N_VIEWED_SAMPLES = 2
data_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()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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