深度学习之基于DCGAN实现动漫人物的生成

注：因为硬件原因，这次的实验并没有生成图片，但是代码应该是没有问题的，可以参考学习一下。
本次基于DCGAN实现动漫人物的生成。最终的效果可以参考大神**K同学啊**的博客。与上篇文章基于DCGAN生成手写数字的步骤基本一致。

1.导入库

import tensorflow as tf
import numpy as np
import glob,imageio,os,PIL,pathlib
import matplotlib.pyplot as plt# 支持中文
plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号

2.数据准备

data_dir = "E:/tmp/.keras/datasets/car_face_photos"
data_dir = pathlib.Path(data_dir)pic_paths = list(data_dir.glob('*'))
pic_paths = [str(path) for path in pic_paths]
img_count = len(list(pic_paths))#共21551张图片

plt.figure(figsize=(10, 5))
plt.suptitle("数据示例", fontsize=15)for i in range(40):plt.subplot(5, 8, i + 1)plt.xticks([])plt.yticks([])# 显示图片images = plt.imread(pic_paths[i])plt.imshow(images)plt.show()

查看图片：

数据预处理：
1.归一化到[-1,1]之间
2.调整图片大小为[64,64]
3.将数据按照batch_size划分开，并打乱

#数据处理
def preprocess_image(image):image = tf.image.decode_jpeg(image,channels=3)image = tf.image.resize(image,[64,64])return (image - 127.5)/127.5
def load_and_preprocess_image(path):image = tf.io.read_file(path)return preprocess_image(image)path_ds = tf.data.Dataset.from_tensor_slices(pic_paths)
image_ds = path_ds.map(load_and_preprocess_image,num_parallel_calls=tf.data.experimental.AUTOTUNE)buffer_size = 60000
batch_size = 256
dataset = image_ds.shuffle(buffer_size).batch(batch_size)

3.生成器与判别器的构建

生成器采用tf.keras.layers.Conv2DTranspose（上采样层）从噪声数据中产生图片。以一个使用该种子作为输入的 Dense 层开始，然后多次上采样直到达到所期望的 64x64x3 的图片尺寸。
除了最后一层使用tanh作为激活函数外，其余的都采用LeakyReLU作为激活函数。

def Geberator_model():model = tf.keras.Sequential([])model.add(tf.keras.layers.Dense(4*4*1024,use_bias=False,input_shape=(100,)))model.add(tf.keras.layers.BatchNormalization())model.add(tf.keras.layers.LeakyReLU())model.add(tf.keras.layers.Reshape((4,4,1024)))assert model.output_shape == (None,4,4,1024)#1model.add(tf.keras.layers.Conv2DTranspose(512,(5,5),strides=(2,2),padding="same",use_bias=False))assert model.output_shape == (None,8,8,512)model.add(tf.keras.layers.BatchNormalization())model.add(tf.keras.layers.LeakyReLU())#2model.add(tf.keras.layers.Conv2DTranspose(256, (5, 5), strides=(2, 2), padding="same", use_bias=False))assert model.output_shape == (None, 16, 16, 256)model.add(tf.keras.layers.BatchNormalization())model.add(tf.keras.layers.LeakyReLU())#3model.add(tf.keras.layers.Conv2DTranspose(128, (5, 5), strides=(2, 2), padding='same', use_bias=False))assert model.output_shape == (None, 32, 32, 128)model.add(tf.keras.layers.BatchNormalization())model.add(tf.keras.layers.LeakyReLU())#4model.add(tf.keras.layers.Conv2DTranspose(3, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh'))assert model.output_shape == (None, 64, 64, 3)return model
generator = Geberator_model()
generator.summary()

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
dense (Dense)                (None, 16384)             1638400
_________________________________________________________________
batch_normalization (BatchNo (None, 16384)             65536
_________________________________________________________________
leaky_re_lu (LeakyReLU)      (None, 16384)             0
_________________________________________________________________
reshape (Reshape)            (None, 4, 4, 1024)        0
_________________________________________________________________
conv2d_transpose (Conv2DTran (None, 8, 8, 512)         13107200
_________________________________________________________________
batch_normalization_1 (Batch (None, 8, 8, 512)         2048
_________________________________________________________________
leaky_re_lu_1 (LeakyReLU)    (None, 8, 8, 512)         0
_________________________________________________________________
conv2d_transpose_1 (Conv2DTr (None, 16, 16, 256)       3276800
_________________________________________________________________
batch_normalization_2 (Batch (None, 16, 16, 256)       1024
_________________________________________________________________
leaky_re_lu_2 (LeakyReLU)    (None, 16, 16, 256)       0
_________________________________________________________________
conv2d_transpose_2 (Conv2DTr (None, 32, 32, 128)       819200
_________________________________________________________________
batch_normalization_3 (Batch (None, 32, 32, 128)       512
_________________________________________________________________
leaky_re_lu_3 (LeakyReLU)    (None, 32, 32, 128)       0
_________________________________________________________________
conv2d_transpose_3 (Conv2DTr (None, 64, 64, 3)         9600
=================================================================
Total params: 18,920,320
Trainable params: 18,885,760
Non-trainable params: 34,560
_________________________________________________________________

判别器为基于CNN的图片分类器

#判别器的构建
def Discriminator_model():model = tf.keras.Sequential([tf.keras.layers.Conv2D(128,(5,5),strides=(2,2),padding="same",input_shape=[64,64,1]),tf.keras.layers.LeakyReLU(),tf.keras.layers.Dropout(0.3),tf.keras.layers.Conv2D(128,(5,5),strides=(2,2),padding="same"),tf.keras.layers.LeakyReLU(),tf.keras.layers.Dropout(0.3),tf.keras.layers.Conv2D(256, (5, 5), strides=(2, 2), padding="same"),tf.keras.layers.LeakyReLU(),tf.keras.layers.Dropout(0.3),tf.keras.layers.Conv2D(512, (5, 5), strides=(2, 2), padding="same"),tf.keras.layers.LeakyReLU(),tf.keras.layers.Dropout(0.3),tf.keras.layers.Flatten(),tf.keras.layers.Dense(1,activation='sigmoid')])return model
discriminator = Discriminator_model()
discriminator.summary()

Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
conv2d (Conv2D)              (None, 32, 32, 128)       3328
_________________________________________________________________
leaky_re_lu_4 (LeakyReLU)    (None, 32, 32, 128)       0
_________________________________________________________________
dropout (Dropout)            (None, 32, 32, 128)       0
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 16, 16, 128)       409728
_________________________________________________________________
leaky_re_lu_5 (LeakyReLU)    (None, 16, 16, 128)       0
_________________________________________________________________
dropout_1 (Dropout)          (None, 16, 16, 128)       0
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 8, 8, 256)         819456
_________________________________________________________________
leaky_re_lu_6 (LeakyReLU)    (None, 8, 8, 256)         0
_________________________________________________________________
dropout_2 (Dropout)          (None, 8, 8, 256)         0
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 4, 4, 512)         3277312
_________________________________________________________________
leaky_re_lu_7 (LeakyReLU)    (None, 4, 4, 512)         0
_________________________________________________________________
dropout_3 (Dropout)          (None, 4, 4, 512)         0
_________________________________________________________________
flatten (Flatten)            (None, 8192)              0
_________________________________________________________________
dense_1 (Dense)              (None, 1)                 8193
=================================================================
Total params: 4,518,017
Trainable params: 4,518,017
Non-trainable params: 0
_________________________________________________________________

4.loss值与优化器

计算交叉熵

cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)

因为分为判别器的生成器，因此loss值的计算方式也是不同的。
判别器的loss值：判断真实图片为1的loss与判断生成图片为0的loss之和。因为判别器希望将真实图片判别为1，将生成图片判别为0.
生成器的loss值：判断生成图片为1的loss。因为生成器希望生成的图片是真实图片，即判别为1.

def Discriminator_loss(real_out,fake_out):real_loss = cross_entropy(tf.ones_like(real_out),real_out)fake_loss = cross_entropy(tf.zeros_like(fake_out),fake_out)return real_loss+fake_loss
def Generator_loss(fake_out):return cross_entropy(tf.ones_like(fake_out),fake_out)

优化器也分为两个：

generator_opt = tf.keras.optimizers.Adam(learning_rate=1e-4)
discriminator_opt = tf.keras.optimizers.Adam(learning_rate=1e-4)

参数设置

epochs = 600
noise_dim = 100
num_exp_to_generate = 16seed = tf.random.normal([num_exp_to_generate,noise_dim])

5.批次训练

训练循环在生成器接收到一个随机种子作为输入时开始。该种子用于生产一张图片。判别器随后被用于区分真实图片（选自训练集）和伪造图片（由生成器生成）。针对这里的每一个模型都计算损失函数，并且计算梯度用于更新生成器与判别器。

def train_step(images):noise = tf.random.normal([batch_size,noise_dim])#生成一个batch_size*noise_dim的数据，相当于生成了batch_size个长度为100的随机向量with tf.GradientTape() as gen_tape,tf.GradientTape() as dis_tape:#两个Tape，一个代表生成器，一个代表判别器。real_out = discriminator(images,training = True)#利用判别器对真实的图片进行训练，得到一个modelgen_image = generator(noise,training = True)#利用生成器对噪声数据生成图片fake_out = discriminator(gen_image, training=True)#利用判别器对生成的图片进行训练gen_loss = Generator_loss(fake_out)#利用判别器对生成图片的判断计算生成器的loss值dis_loss = Discriminator_loss(real_out,fake_out)##利用判别器对生成图片和真实图片的判断计算判别器的loss值gradient_gen = gen_tape.gradient(gen_loss,generator.trainable_variables)#根据生成器的loss值和网络模型计算梯度gradient_dis = dis_tape.gradient(dis_loss, discriminator.trainable_variables)#根据判别器的loss值和网络模型计算梯度Generator_opt.apply_gradients(zip(gradient_gen,generator.trainable_variables))#根据梯度对生成器进行梯度更新Discriminator_opt.apply_gradients(zip(gradient_dis,discriminator.trainable_variables))#根据梯度对判别器进行梯度更新

可视化图片并保存到本地

def Generator_plot_image(gen_model,test_noise,epoch):pre_images = gen_model.predict(test_noise,training = False)fig = plt.figure(figsize=(4,4))for i in range(pre_images.shape[0]):plt.subplot(4,4,i+1)plt.imshow((pre_images[i,:,:,0]+1)/2)plt.axis('off')fig.savefig("E:/tmp/.keras/datasets/cartoon_photos_gen_DCGAN/%05d.png" % epoch)plt.close()

训练模型：

def train(dataset,epochs):for epoch in range(epochs):for image_batch in dataset:train_step(image_batch)print('.',end='')print()Generator_plot_image(generator,seed,epoch)
train(dataset,epochs)

也可以用来生成其他的图片，可以起到数据增强的效果。
努力加油a啊