//Description: Simple_GAN (一个简单的GAN例子)
//Create Date: 2022-01-25 15:28:39
//Author: channy
一个形象的比喻：
简单的来说，就是让无数的猴子随机敲键盘，那把些不听话，敲不出有规律字符的猴子全部干掉，接着在这群猴子里面找能敲出正确单词的猴子，把剩下的干掉，把敲不出有意义句子的猴子再干掉，敲不出莎士比亚的全干掉。。。剩下的就是莎士比亚猴了
开发环境：python3 (3.8.10)
使用到的库：numpy, matplotlib, keras(tensorflow), tqdm。
其中，原文用到的Adam在tensorflow 2.7.0版本中需要改成adam_v2，使用时改成adam_v2.Adam
>>> print(tf.version.VERSION)
2.7.0
from keras.datasets import mnist
from keras.layers import Dense, Dropout, Input
from keras.models import Model,Sequential
from keras.layers.advanced_activations import LeakyReLU
from keras.optimizers import adam_v2
from tqdm import tqdm
import numpy as np
import matplotlib.pyplot as plt
加载训练数据，这里使用的是keras自带的mnist数据集，即上一步导入的库from keras.datasets import mnist的作用
# Load the dataset
def load_data():
(x_train, y_train), (_, _) = mnist.load_data()
x_train = (x_train.astype(np.float32) - 127.5)/127.5
# Convert shape from (60000, 28, 28) to (60000, 784)
x_train = x_train.reshape(60000, 784)
return (x_train, y_train)
X_train, y_train = load_data()
print(X_train.shape, y_train.shape)
建立线性模型Sequential，类似于MLP/CNN。生成器G和判别器D都是MLP/CNN，即GAN由两个神经网络连接而成。
def build_generator():
model = Sequential()
model.add(Dense(units=256, input_dim=100))
model.add(LeakyReLU(alpha=0.2))
model.add(Dense(units=512))
model.add(LeakyReLU(alpha=0.2))
model.add(Dense(units=1024))
model.add(LeakyReLU(alpha=0.2))
model.add(Dense(units=784, activation='tanh'))
model.compile(loss='binary_crossentropy', optimizer=adam_v2.Adam(0.0002, 0.5))
return model
generator = build_generator()
generator.summary()
def build_discriminator():
model = Sequential()
model.add(Dense(units=1024 ,input_dim=784))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.3))
model.add(Dense(units=512))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.3))
model.add(Dense(units=256))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.3))
model.add(Dense(units=1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer=adam_v2.Adam(0.0002, 0.5))
return model
discriminator = build_discriminator()
discriminator.summary()
建立GAN，连接G和D
def build_GAN(discriminator, generator):
discriminator.trainable=False
GAN_input = Input(shape=(100,))
x = generator(GAN_input)
GAN_output= discriminator(x)
GAN = Model(inputs=GAN_input, outputs=GAN_output)
GAN.compile(loss='binary_crossentropy', optimizer=adam_v2.Adam(0.0002, 0.5))
return GAN
GAN = build_GAN(discriminator, generator)
GAN.summary()
画图函数，为了直观地感受结果，也是导入库matplotlib的作用
def draw_images(generator, epoch, examples=25, dim=(5,5), figsize=(10,10)):
noise= np.random.normal(loc=0, scale=1, size=[examples, 100])
generated_images = generator.predict(noise)
generated_images = generated_images.reshape(25,28,28)
plt.figure(figsize=figsize)
for i in range(generated_images.shape[0]):
plt.subplot(dim[0], dim[1], i+1)
plt.imshow(generated_images[i], interpolation='nearest', cmap='Greys')
plt.axis('off')
plt.tight_layout()
plt.savefig('Generated_images %d.png' %epoch)
训练函数，训练GAN。epochs为迭代次数，tqdm为动态显示进度条，batch_size为最终需要生成的128张图片，也是初始使用噪声生成的假图片。
每次迭代利用高斯分布的噪声noise作为训练的初始输入，从Mnist中随机选取128张图片，标注并和噪声图片混合。训练D，训练完成后，D的权重weight得到了更新。然后固定weight，训练G。
def train_GAN(epochs=1, batch_size=128):
#Loading the data
X_train, y_train = load_data()
# Creating GAN
generator= build_generator()
discriminator= build_discriminator()
GAN = build_GAN(discriminator, generator)
for i in range(1, epochs+1):
print("Epoch %d" %i)
for _ in tqdm(range(batch_size)):
# Generate fake images from random noiset
noise= np.random.normal(0,1, (batch_size, 100))
fake_images = generator.predict(noise)
# Select a random batch of real images from MNIST
real_images = X_train[np.random.randint(0, X_train.shape[0], batch_size)]
# Labels for fake and real images
label_fake = np.zeros(batch_size)
label_real = np.ones(batch_size)
# Concatenate fake and real images
X = np.concatenate([fake_images, real_images])
y = np.concatenate([label_fake, label_real])
# Train the discriminator
discriminator.trainable=True
discriminator.train_on_batch(X, y)
# Train the generator/chained GAN model (with frozen weights in discriminator)
discriminator.trainable=False
GAN.train_on_batch(noise, label_real)
# Draw generated images every 15 epoches
if i == 1 or i % 10 == 0:
draw_images(generator, i)
train_GAN(epochs=400, batch_size=128)
总的来说，具体操作步骤如下：