前两篇我们介绍了celeB数据集
CelebA Datasets——Readme
基于CelebA数据集的GAN模型
直接上代码咯
导入依赖：
代码语言：javascript复制# example of a gan for generating faces
from numpy import load
from numpy import zeros
from numpy import ones
from numpy.random import randn
from numpy.random import randint
from keras.optimizers import Adam
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Reshape
from keras.layers import Flatten
from keras.layers import Conv2D
from keras.layers import Conv2DTranspose
from keras.layers import LeakyReLU
from keras.layers import Dropout
from matplotlib import pyplot然后是定义判别器：
代码语言：javascript复制# define the standalone discriminator model
def define_discriminator(in_shape=(80,80,3)):
model = Sequential()
# normal
model.add(Conv2D(128, (5,5), padding='same', input_shape=in_shape))
model.add(LeakyReLU(alpha=0.2))
# downsample to 40x40
model.add(Conv2D(128, (5,5), strides=(2,2), padding='same'))
model.add(LeakyReLU(alpha=0.2))
# downsample to 20x30
model.add(Conv2D(128, (5,5), strides=(2,2), padding='same'))
model.add(LeakyReLU(alpha=0.2))
# downsample to 10x10
model.add(Conv2D(128, (5,5), strides=(2,2), padding='same'))
model.add(LeakyReLU(alpha=0.2))
# downsample to 5x5
model.add(Conv2D(128, (5,5), strides=(2,2), padding='same'))
model.add(LeakyReLU(alpha=0.2))
# classifier
model.add(Flatten())
model.add(Dropout(0.4))
model.add(Dense(1, activation='sigmoid'))
# compile model
opt = Adam(lr=0.0002, beta_1=0.5)
model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])
return model接下来定义生成器：
代码语言：javascript复制# define the standalone generator model
def define_generator(latent_dim):
model = Sequential()
# foundation for 5x5 feature maps
n_nodes = 128 * 5 * 5
model.add(Dense(n_nodes, input_dim=latent_dim))
model.add(LeakyReLU(alpha=0.2))
model.add(Reshape((5, 5, 128)))
# upsample to 10x10
model.add(Conv2DTranspose(128, (4,4), strides=(2,2), padding='same'))
model.add(LeakyReLU(alpha=0.2))
# upsample to 20x20
model.add(Conv2DTranspose(128, (4,4), strides=(2,2), padding='same'))
model.add(LeakyReLU(alpha=0.2))
# upsample to 40x40
model.add(Conv2DTranspose(128, (4,4), strides=(2,2), padding='same'))
model.add(LeakyReLU(alpha=0.2))
# upsample to 80x80
model.add(Conv2DTranspose(128, (4,4), strides=(2,2), padding='same'))
model.add(LeakyReLU(alpha=0.2))
# output layer 80x80x3
model.add(Conv2D(3, (5,5), activation='tanh', padding='same'))
return model定义GAN：
代码语言：javascript复制# define the combined generator and discriminator model, for updating the generator
def define_gan(g_model, d_model):
# make weights in the discriminator not trainable
d_model.trainable = False
# connect them
model = Sequential()
# add generator
model.add(g_model)
# add the discriminator
model.add(d_model)
# compile model
opt = Adam(lr=0.0002, beta_1=0.5)
model.compile(loss='binary_crossentropy', optimizer=opt)
return model加载我们上一篇文章写到的numpy压缩数据包点npz结尾的文件
代码语言：javascript复制# load and prepare training images
def load_real_samples():
# load the face dataset
data = load('img_align_celeba.npz')
X = data['arr_0']
# convert from unsigned ints to floats
X = X.astype('float32')
# scale from [0,255] to [-1,1]
X = (X - 127.5) / 127.5
return X从latentSpace中生成样本点：
代码语言：javascript复制# generate points in latent space as input for the generator
def generate_latent_points(latent_dim, n_samples):
# generate points in the latent space
x_input = randn(latent_dim * n_samples)
# reshape into a batch of inputs for the network
x_input = x_input.reshape(n_samples, latent_dim)
return x_input生成假的样本：
代码语言：javascript复制# use the generator to generate n fake examples, with class labels
def generate_fake_samples(g_model, latent_dim, n_samples):
# generate points in latent space
x_input = generate_latent_points(latent_dim, n_samples)
# predict outputs
X = g_model.predict(x_input)
# create 'fake' class labels (0)
y = zeros((n_samples, 1))
return X, y定义一个画图的函数
代码语言：javascript复制# create and save a plot of generated images
def save_plot(examples, epoch, n=10):
# scale from [-1,1] to [0,1]
examples = (examples 1) / 2.0
# plot images
for i in range(n * n):
# define subplot
pyplot.subplot(n, n, 1 i)
# turn off axis
pyplot.axis('off')
# plot raw pixel data
pyplot.imshow(examples[i])
# save plot to file
filename = 'generated_plot_ed.png' % (epoch 1)
pyplot.savefig(filename)
pyplot.close()定义评估函数：
代码语言：javascript复制# evaluate the discriminator, plot generated images, save generator model
def summarize_performance(epoch, g_model, d_model, dataset, latent_dim, n_samples=100):
# prepare real samples
X_real, y_real = generate_real_samples(dataset, n_samples)
# evaluate discriminator on real examples
_, acc_real = d_model.evaluate(X_real, y_real, verbose=0)
# prepare fake examples
x_fake, y_fake = generate_fake_samples(g_model, latent_dim, n_samples)
# evaluate discriminator on fake examples
_, acc_fake = d_model.evaluate(x_fake, y_fake, verbose=0)
# summarize discriminator performance
print('>Accuracy real: %.0f%%, fake: %.0f%%' % (acc_real*100, acc_fake*100))
# save plot
save_plot(x_fake, epoch)
# save the generator model tile file
filename = 'generator_model_d.h5' % (epoch 1)
g_model.save(filename)最后是我们的训练函数：
代码语言：javascript复制# train the generator and discriminator
def train(g_model, d_model, gan_model, dataset, latent_dim, n_epochs=100, n_batch=128):
bat_per_epo = int(dataset.shape[0] / n_batch)
half_batch = int(n_batch / 2)
# manually enumerate epochs
for i in range(n_epochs):
# enumerate batches over the training set
for j in range(bat_per_epo):
# get randomly selected 'real' samples
X_real, y_real = generate_real_samples(dataset, half_batch)
# update discriminator model weights
d_loss1, _ = d_model.train_on_batch(X_real, y_real)
# generate 'fake' examples
X_fake, y_fake = generate_fake_samples(g_model, latent_dim, half_batch)
# update discriminator model weights
d_loss2, _ = d_model.train_on_batch(X_fake, y_fake)
# prepare points in latent space as input for the generator
X_gan = generate_latent_points(latent_dim, n_batch)
# create inverted labels for the fake samples
y_gan = ones((n_batch, 1))
# update the generator via the discriminator's error
g_loss = gan_model.train_on_batch(X_gan, y_gan)
# summarize loss on this batch
print('>%d, %d/%d, d1=%.3f, d2=%.3f g=%.3f' %
(i 1, j 1, bat_per_epo, d_loss1, d_loss2, g_loss))
# evaluate the model performance, sometimes
if (i 1) % 10 == 0:
summarize_performance(i, g_model, d_model, dataset, latent_dim)
# size of the latent space
latent_dim = 100
# create the discriminator
d_model = define_discriminator()
# create the generator
g_model = define_generator(latent_dim)
# create the gan
gan_model = define_gan(g_model, d_model)
# load image data
dataset = load_real_samples()
# train model
train(g_model, d_model, gan_model, dataset, latent_dim)最后生成的图像：
