手把手教你搭建一个GAN（生成对抗网络）模型

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这个教程用 Keras 实现一个简单的GAN （生成对抗网络）模型. 基于这个:mnistGAN项目，我们将用到Keras 这个python 库。

将用到的python packages:

keras: 神经网络模型相关
matplotlib: 画图
tensorflow: keras运行基础
tqdm: 进度条

Talk is cheap, show me the code.

1. 导入包：

import os
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm

from keras.layers import Input  # input layer  
from keras.models import Model, Sequential
from keras.layers.core import Dense, Dropout
from keras.layers.advanced_activations import LeakyReLU
from keras.datasets import mnist
from keras.optimizers import Adam
from keras import initializers

2. 定义几个变量：

# Let Keras know that we are using tensorflow as our backend engine
os.environ["KERAS_BACKEND"] = "tensorflow"

# To make sure that we can reproduce the experiment and get the same results by same random output
np.random.seed(10)

# The dimension of our random noise vector. this code is from: sonictl_at_cnblogs.net
random_dim = 100

3. 准备数据：

这里要用到手写阿拉伯数字（MNIST）数据集。MNIST 数据集已经是一个被”嚼烂”了的数据集, 很多教程都会对它”下手”, 几乎成为一个 “典范”. 介绍一下:

MNIST 数据集来自美国国家标准与技术研究所, National Institute of Standards and Technology (NIST). 训练集 (training set) 由来自 250 个不同人手写的数字构成, 其中 50% 是高中学生, 50% 来自人口普查局 (the Census Bureau) 的工作人员. 测试集(test set) 也是同样比例的手写数字数据.

MNIST 数据集可在 http://yann.lecun.com/exdb/mnist/ 获取, 它包含了四个部分:

Training set images(包含 60,000 个样本)
Training set labels(包含 60,000 个标签)
Test set images(10,000 个样本)
Test set labels(包含 10,000 个标签)
在 MNIST 数据集中的每张图片由 28 x 28 个像素点构成, 每个像素点用一个灰度值表示. labels包含了相应的目标变量, 也就是手写数字的类标签(整数 0-9).
如图：

代码：

def load_minst_data():
    # load the data
    (x_train, y_train), (x_test, y_test) = mnist.load_data()    # y is the set of labels, mnist.load_data() is part of Keras and allows you to easily import the MNIST dataset
    # normalize our inputs to be in the range[-1, 1]
    x_train = (x_train.astype(np.float32) - 127.5)/127.5
    # convert x_train with a shape of (60000, 28, 28) to (60000, 784) so we have
    # 784 columns per row
    x_train = x_train.reshape(60000, 784)
    return (x_train, y_train, x_test, y_test)

4. 生成器和判别器

现在要建立生成器和判别器，我们要用Adam 优化器来优化这2个网络。对于生成器和判别器中的神经网络，我们都用3个隐藏层，并用Leaky Relu函数来作为激活函数。这里还有个技巧，为了提升判别器在它没见过的数据上的鲁棒性，在里面加了一个DropOut 层

# You will use the Adam optimizer
def get_optimizer():
    return Adam(lr=0.0002, beta_1=0.5)

def get_generator(optimizer):
    generator = Sequential()
    generator.add(Dense(256, input_dim=random_dim, kernel_initializer=initializers.RandomNormal(stddev=0.02)))
    generator.add(LeakyReLU(0.2))

    generator.add(Dense(512))
    generator.add(LeakyReLU(0.2))

    generator.add(Dense(1024))
    generator.add(LeakyReLU(0.2))

    generator.add(Dense(784, activation='tanh'))
    generator.compile(loss='binary_crossentropy', optimizer=optimizer)
    return generator

def get_discriminator(optimizer):
    discriminator = Sequential()
    discriminator.add(Dense(1024, input_dim=784, kernel_initializer=initializers.RandomNormal(stddev=0.02)))
    discriminator.add(LeakyReLU(0.2))
    discriminator.add(Dropout(0.3))

    discriminator.add(Dense(512))
    discriminator.add(LeakyReLU(0.2))
    discriminator.add(Dropout(0.3))

    discriminator.add(Dense(256))
    discriminator.add(LeakyReLU(0.2))
    discriminator.add(Dropout(0.3))

    discriminator.add(Dense(1, activation='sigmoid'))
    discriminator.compile(loss='binary_crossentropy', optimizer=optimizer)
    return discriminator

一个流程的代码

有关的注释都已经写了。一行行弄懂基本就懂了。

import os
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm


from keras.layers import Input
from keras.models import Model, Sequential
from keras.layers.core import Dense, Dropout
from keras.layers.advanced_activations import LeakyReLU
from keras.datasets import mnist
from keras.optimizers import Adam
from keras import initializers

# fix the multiple OpenMP runtime linked error:
os.environ['KMP_DUPLICATE_LIB_OK']='True'

# Let Keras know that we are using tensorflow as our backend engine
os.environ["KERAS_BACKEND"] = "tensorflow"

# To make sure that we can reproduce the experiment and get the same results
np.random.seed(10)

# The dimension of our random noise vector. This vector is the most initial input of the gan Model.
random_dim = 100

# size of each batch
batch_size = 128

# load the data:
(x_train, y_train), (x_test, y_test) = mnist.load_data('mnist.npz')
x_train = (x_train.astype(np.float32) - 127.5)/127.5   # x_train_i is from 0 to 255, 255 = 127.5*2
# convert x_train with a shape of (60000, 28, 28) to (60000, 784) so we have
# 784 columns per row
x_train = x_train.reshape(60000, 784)

# Split the training data into batches of size 128
batch_count = x_train.shape[0] / batch_size
batch_count = int(round(batch_count))
print('batch_count:', batch_count)

#
# ==== build the GAN ====
adam = Adam(lr=0.0002, beta_1=0.5)

# -- generator building: --
generator = Sequential()   # keras Sequential model, model of sequential stack of layers
generator.add(Dense(256, input_dim=random_dim, kernel_initializer=initializers.RandomNormal(stddev=0.02)))
# layer: (256, input_dim=random_dim, kernel_initializer=initializers.RandomNormal(stddev=0.02))
generator.add(LeakyReLU(0.2))
generator.add(Dense(512))     # second layer
generator.add(LeakyReLU(0.2))
generator.add(Dense(1024))    # third layer
generator.add(LeakyReLU(0.2))
generator.add(Dense(28*28, activation='tanh'))    # output layer, 784 = 28*28
generator.compile(loss='binary_crossentropy', optimizer=adam)    # compile Before training a model,
# you need to configure the learning process, which is done via the compile method.

# -- discriminator building: --
discriminator = Sequential()

discriminator.add(Dense(1024, input_dim=784, kernel_initializer=initializers.RandomNormal(stddev=0.02)))
discriminator.add(LeakyReLU(0.2))
discriminator.add(Dropout(0.3))

discriminator.add(Dense(512))
discriminator.add(LeakyReLU(0.2))
discriminator.add(Dropout(0.3))

discriminator.add(Dense(256))
discriminator.add(LeakyReLU(0.2))
discriminator.add(Dropout(0.3))

discriminator.add(Dense(1, activation='sigmoid'))
discriminator.compile(loss='binary_crossentropy', optimizer=adam)

# -- gan building with generator&discriminator: --
discriminator.trainable = False  # frozen discriminator first. Train either the generator or discriminator at a time
gan_input = Input(shape=(random_dim,))   # keras.layers.input, type: tensor.
# gan input (noise) will be 100-dimensional vectors

x = generator(gan_input)  # gan_input -> generator(a Sequential()) -> x , x is the input of discriminator, x is an image
gan_output = discriminator(x) # x -> discriminator -> gan_output. gan_output is a probability: if the image is real
gan = Model(inputs=gan_input, outputs=gan_output) # gan_input ->[GAN]-> gan_output. The Model takes the head and tail as
# its arguments of inputs and outputs.

gan.compile(loss='binary_crossentropy', optimizer=adam) # gan: <keras.engine.training.Model object at 0x647626d68>

# ==== one epoch of training: =====
e = 1
print( '-'*15, 'Epoch %d' % e, '-'*15)
for _ in tqdm(range(batch_count)):  # each batch in batch_count, Note: operations on each batch of 128 samples.
    # Get a random set of input noise and images
    noise = np.random.normal(0, 1, size=[batch_size, random_dim])   # batch_size = 128, one batch of noise.
    temp_idx = np.random.randint(0, x_train.shape[0], size=batch_size)  # pick 128 rand int numbers from [0, 60000]
    image_batch = x_train[temp_idx]  # shape=(128,784)

    generated_images = generator.predict(noise)  # Generate fake MNIST images: shape=(128,784)
    num_fake_img = len(generated_images)
    X = np.concatenate([image_batch, generated_images])  # concatenate the True image batch and Fake image  (256,784)

    # Labels for generated and real data
    y_dis = np.zeros(2*batch_size)
    y_dis[:batch_size] = 0.9   # One-sided label smoothing
    # read more: https://towardsdatascience.com/gan-ways-to-improve-gan-performance-acf37f9f59b?gi=ddcc9670e7bd

    # Train discriminator
    discriminator.trainable = True
    discriminator.train_on_batch(X, y_dis)   # The training of discriminator is as same as general neural networks

    # Train generator
    discriminator.trainable = False
    noise = np.random.normal(0, 1, size=[batch_size, random_dim])
    y_gen = np.ones(batch_size)
    gan.train_on_batch(noise, y_gen)

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