需要源码和数据集请点赞关注收藏后评论区留言私信~~~
一、生成对抗网络的概念
生成对抗网络(GANs，Generative Adversarial Nets),由Ian Goodfellow在2014年提出的,是当今计算机科学中最有趣的概念之一。GAN最早提出是为了弥补真实数据的不足，生成高质量的人工数据。GAN的主要思想是通过两个模型的对抗性训练。随着训练过程的推进，生成网络（Generator，G）逐渐变得擅长创建看起来真实的图像，而判别网络（Discriminator，D）则变得更擅长区分真实图像和生成器生成的图像。GAN网络不局限于提高单一网络的性能，而是希望实现生成器和鉴别器之间的纳什均衡。
假设在低维空间Z存在一个简单容易采样的分布p(z)，例如正态分布 ，生成网络构成一个映射函数G：Z→X，判别网络需要判别输入是来自真实数据X_real还是生成网络生成的数据X_fake，结构示意图如图8-1所示
下面给出DCGAN利用LSUN数据库生成卧室样本的例子和生成人脸样本的例子，虽然DCGAN还难以生成高精度的图像样本，但这样的结果已经足够让世人感到惊艳
二、DCGAN在MNIST手写数据集上实战
通过本程序可以完成两个模型的训练。一个是生成模型，一个是判别模型
1：项目结构如下
代码大致可以分为以下几部分
1：构建生成网络
2：构建判别网络
3：DCGAN网络训练
开始下载模型
2：效果展示
生成图片如下 可以说效果十分逼真
这是第一张生成图片 可以看出里面有些字体还是略微不够真实，容易被判别器鉴别出来
这一张是图片生成的十分逼真，几乎没有什么缺点
三、代码
部分代码如下 全部代码和数据集请点赞关注收藏后评论区留言私信~~~
-
from keras
.models import Sequential
-
from keras
.layers import Dense
-
from keras
.layers import Reshape
-
from keras
.layers
.core import Activation
-
from tensorflow
.python
.keras
.layers import BatchNormalization
-
from keras
.layers
.convolutional import UpSampling2D
-
from keras
.layers
.convolutional import Conv2D, MaxPooling2D
-
from keras
.layers
.core import Flatten
-
from tensorflow
.keras
.optimizers import SGD
-
from keras
.datasets import mnist
-
import numpy as np
-
from PIL import Image
-
import argparse
-
import math
-
-
-
def generator_model():
-
model =
Sequential()
-
model.
add(
Dense(input_dim=
100, units=
1024))
-
-
model.
add(
Activation(
'tanh'))
-
model.
add(
Dense(
128*
7*
7))
-
model.
add(
BatchNormalization())
-
model.
add(
Activation(
'tanh'))
-
model.
add(
Reshape((
7,
7,
128), input_shape=(
128*
7*
7,)))
-
model.
add(
UpSampling2D(size=(
2,
2)))
-
model.
add(
Conv2D(
64, (
5,
5), padding=
'same'))
-
model.
add(
Activation(
'tanh'))
-
model.
add(
UpSampling2D(size=(
2,
2)))
-
model.
add(
Conv2D(
1, (
5,
5), padding=
'same'))
-
model.
add(
Activation(
'tanh'))
-
return model
-
-
-
def
discriminator_model():
-
model =
Sequential()
-
model.
add(
-
Conv2D(
64, (
5,
5),
-
padding=
'same',
-
input_shape=(
28,
28,
1))
-
)
-
model.
add(
Activation(
'tanh'))
-
model.
add(
MaxPooling2D(pool_size=(
2,
2)))
-
model.
add(
Conv2D(
128, (
5,
5)))
-
model.
add(
Activation(
'tanh'))
-
model.
add(
MaxPooling2D(pool_size=(
2,
2)))
-
model.
add(
Flatten())
-
model.
add(
Dense(
1024))
-
model.
add(
Activation(
'tanh'))
-
model.
add(
Dense(
1))
-
model.
add(
Activation(
'sigmoid'))
-
return model
-
-
-
def
generator_containing_discriminator(g, d):
-
model =
Sequential()
-
model.
add(g)
-
d.trainable = False
-
model.
add(d)
-
return model
-
-
-
def
combine_images(generated_images):
-
num = generated_images.shape[
0]
-
width =
int(math.
sqrt(num))
-
height =
int(math.
ceil(
float(num)/width))
-
shape = generated_images.shape[
1:
3]
-
image = np.
zeros((height*shape[
0], width*shape[
1]),
-
dtype=generated_images.dtype)
-
for index, img in
enumerate(generated_images):
-
i =
int(index/width)
-
j = index % width
-
image[i*shape[
0]:(i+
1)*shape[
0], j*shape[
1]:(j+
1)*shape[
1]] = \
-
img[:, :,
0]
-
return image
-
-
-
def
train(BATCH_SIZE,path):
-
(X_train, y_train), (X_test, y_test) = mnist.
load_data()
-
X_train = (X_train.
astype(np.float32) -
127.5)/
127.5
-
X_train = X_train[:, :, :, None]
-
X_test = X_test[:, :, :, None]
-
# X_train = X_train.
reshape((X_train.shape,
1) + X_train.shape[
1:])
-
d =
discriminator_model()
-
g =
generator_model()
-
d_on_g =
generator_containing_discriminator(g, d)
-
d_optim =
SGD(lr=
0.0005, momentum=
0.9, nesterov=True)
-
g_optim =
SGD(lr=
0.0005, momentum=
0.9, nesterov=True)
-
g.
compile(loss=
'binary_crossentropy', optimizer=
"SGD")
-
d_on_g.
compile(loss=
'binary_crossentropy', optimizer=g_optim)
-
d.trainable = True
-
d.
compile(loss=
'binary_crossentropy', optimizer=d_optim)
-
for epoch in
range(
100):
-
print(
"Epoch is", epoch)
-
print(
"Number of batches",
int(X_train.shape[
0]/BATCH_SIZE))
-
for index in
range(
int(X_train.shape[
0]/BATCH_SIZE)):
-
noise = np.random.
uniform(-
1,
1, size=(BATCH_SIZE,
100))
-
image_batch = X_train[index*BATCH_SIZE:(index+
1)*BATCH_SIZE]
-
generated_images = g.
predict(noise, verbose=
0)
-
if index %
20 ==
0:
-
image =
combine_images(generated_images)
-
image = image*
127.5+
127.5
-
Image.
fromarray(image.
astype(np.uint8)).
save(
-
str(epoch)+
"_"+
str(index)+
".png")
-
X = np.
concatenate((image_batch, generated_images))
-
y = [
1] * BATCH_SIZE + [
0] * BATCH_SIZE
-
d_loss = d.
train_on_batch(X, y)
-
print(
"batch %d d_loss : %f" % (index, d_loss))
-
noise = np.random.
uniform(-
1,
1, (BATCH_SIZE,
100))
-
d.trainable = False
-
g_loss = d_on_g.
train_on_batch(noise, [
1] * BATCH_SIZE)
-
d.trainable = True
-
print(
"batch %d g_loss : %f" % (index, g_loss))
-
if index %
10 ==
9:
-
g.
save_weights(
'generator', True)
-
d.
save_weights(
'discriminator', True)
-
-
-
def
generate(BATCH_SIZE, nice=False):
-
g =
generator_model()
-
g.
compile(loss=
'binary_crossentropy', optimizer=
"SGD")
-
g.
load_weights(
'generator')
-
if nice:
-
s = g.
predict(noise, verbose=
1)
-
d_pret = d.
predict(generated_images, verbose=
1)
-
index = np.
arange(
0, BATCH_SIZE*
20)
-
index.
resize((BATCH_SIZE*
20,
1))
-
pre_with_index =
list(np.
append(d_pret, index, axis=
1))
-
pre_with_index.
sort(key=lambda x: x[
0], reverse=True)
-
nice_images = np.
zeros((BATCH_SIZE,) + generated_images.shape[
1:
3], dtype=np.float32)
-
nice_images = nice_images[:, :, :, None]
-
for i in
range(BATCH_SIZE):
-
idx =
int(pre_with_index[i][
1])
-
nice_images[i, :, :,
0] = generated_images[idx, :, :,
0]
-
.
predict(noise, verbose=
1)
-
image =
combine_images(generated_images)
-
image = image*
127.5+
127.5
-
Image.
fromarray(image.
astype(np.uint8)).
save(
-
"generated_image.png")
-
-
-
def
get_args():
-
parser = argparse.
ArgumentParser()
-
parser.
add_argument(
"--mode", type=str,default =
'train',)
-
# parser.
add_argument(
"--mode", type=str,default =
'generate',)
-
parser.
add_argument(
"--batch_size", type=int, default=
8)
-
parse
-
if __name__ ==
"__main__":
-
args =
get_args()
-
if args.mode ==
"train":
-
train(BATCH_SIZE=args.batch_size,path =args.path )
-
elif args.mode ==
"generate":
-
generate(BATCH_SIZE=args.batch_size, nice=args.nice)
创作不易 觉得有帮助请点赞关注收藏~~~
转载：https://blog.csdn.net/jiebaoshayebuhui/article/details/128262166
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