Keras入门——（3）生成式对抗网络GAN
导入 matplotlib 模块：
import matplotlib
查看自己版本所支持的backends：
print(matplotlib.rcsetup.all_backends)
返回信息：
['GTK3Agg', 'GTK3Cairo', 'MacOSX', 'nbAgg', 'Qt4Agg', 'Qt4Cairo', 'Qt5Agg', 'Qt5Cairo', 'TkAgg', 'TkCairo', 'WebAgg', 'WX', 'WXAgg', 'WXCairo', 'agg', 'cairo', 'pdf', 'pgf', 'ps', 'svg', 'template']
查看当前工作的matplotlibrc文件是哪个：
print(matplotlib.matplotlib_fname())
返回信息：
D:\ProgramData\Anaconda2\lib\site-packages\matplotlib\mpl-data\matplotlibrc
打开 matplotlibrc 查看相应内容：
将 backend 修改为 TkAgg：
执行如下代码：
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from keras.models import Model
from keras.layers import Dense, Activation, Input, Reshape
from keras.layers import Conv1D, Flatten, Dropout
from keras.optimizers import SGD, Adam
def sample_data(n_samples=10000, x_vals=np.arange(0, 5, .1), max_offset=100, mul_range=[1, 2]):
vectors = []
for i in range(n_samples):
offset = np.random.random() * max_offset
mul = mul_range[0] + np.random.random() * (mul_range[1] - mul_range[0])
vectors.append(
np.sin(offset + x_vals * mul) / 2 + .5
)
return np.array(vectors)
ax = pd.DataFrame(np.transpose(sample_data(5))).plot()
plt.show()
生成图像：
执行代码：
def get_generative(G_in, dense_dim=200, out_dim=50, lr=1e-3):
x = Dense(dense_dim)(G_in)
x = Activation('tanh')(x)
G_out = Dense(out_dim, activation='tanh')(x)
G = Model(G_in, G_out)
opt = SGD(lr=lr)
G.compile(loss='binary_crossentropy', optimizer=opt)
return G, G_out
G_in = Input(shape=[10])
G, G_out = get_generative(G_in)
G.summary()
生成图像：
执行代码：
def get_discriminative(D_in, lr=1e-3, drate=.25, n_channels=50, conv_sz=5, leak=.2):
x = Reshape((-1, 1))(D_in)
x = Conv1D(n_channels, conv_sz, activation='relu')(x)
x = Dropout(drate)(x)
x = Flatten()(x)
x = Dense(n_channels)(x)
D_out = Dense(2, activation='sigmoid')(x)
D = Model(D_in, D_out)
dopt = Adam(lr=lr)
D.compile(loss='binary_crossentropy', optimizer=dopt)
return D, D_out
D_in = Input(shape=[50])
D, D_out = get_discriminative(D_in)
D.summary()
生成图像：
执行代码：
def set_trainability(model, trainable=False):
model.trainable = trainable
for layer in model.layers:
layer.trainable = trainable
def make_gan(GAN_in, G, D):
set_trainability(D, False)
x = G(GAN_in)
GAN_out = D(x)
GAN = Model(GAN_in, GAN_out)
GAN.compile(loss='binary_crossentropy', optimizer=G.optimizer)
return GAN, GAN_out
GAN_in = Input([10])
GAN, GAN_out = make_gan(GAN_in, G, D)
GAN.summary()
生成图像：
执行代码：
def sample_data_and_gen(G, noise_dim=10, n_samples=10000):
XT = sample_data(n_samples=n_samples)
XN_noise = np.random.uniform(0, 1, size=[n_samples, noise_dim])
XN = G.predict(XN_noise)
X = np.concatenate((XT, XN))
y = np.zeros((2*n_samples, 2))
y[:n_samples, 1] = 1
y[n_samples:, 0] = 1
return X, y
def pretrain(G, D, noise_dim=10, n_samples=10000, batch_size=32):
X, y = sample_data_and_gen(G, n_samples=n_samples, noise_dim=noise_dim)
set_trainability(D, True)
D.fit(X, y, epochs=1, batch_size=batch_size)
pretrain(G, D)
返回信息：
Epoch 1/1 32/20000 [..............................] - ETA: 6:42 - loss: 0.7347 288/20000 [..............................] - ETA: 47s - loss: 0.4808 544/20000 [..............................] - ETA: 26s - loss: 0.3318 800/20000 [>.............................] - ETA: 19s - loss: 0.2359 1056/20000 [>.............................] - ETA: 15s - loss: 0.1805 1312/20000 [>.............................] - ETA: 12s - loss: 0.1459 1568/20000 [=>............................] - ETA: 11s - loss: 0.1223 1824/20000 [=>............................] - ETA: 10s - loss: 0.1053 2048/20000 [==>...........................] - ETA: 9s - loss: 0.0938 2272/20000 [==>...........................] - ETA: 8s - loss: 0.0847 2528/20000 [==>...........................] - ETA: 8s - loss: 0.0761 2784/20000 [===>..........................] - ETA: 7s - loss: 0.0692 3040/20000 [===>..........................] - ETA: 7s - loss: 0.0634 3296/20000 [===>..........................] - ETA: 6s - loss: 0.0585 3552/20000 [====>.........................] - ETA: 6s - loss: 0.0543 3808/20000 [====>.........................] - ETA: 6s - loss: 0.0507 4064/20000 [=====>........................] - ETA: 5s - loss: 0.0475 4352/20000 [=====>........................] - ETA: 5s - loss: 0.0444 4608/20000 [=====>........................] - ETA: 5s - loss: 0.0420 4864/20000 [======>.......................] - ETA: 5s - loss: 0.0398 5120/20000 [======>.......................] - ETA: 4s - loss: 0.0378 5376/20000 [=======>......................] - ETA: 4s - loss: 0.0360 5632/20000 [=======>......................] - ETA: 4s - loss: 0.0344 5888/20000 [=======>......................] - ETA: 4s - loss: 0.0329 6144/20000 [========>.....................] - ETA: 4s - loss: 0.0315 6400/20000 [========>.....................] - ETA: 4s - loss: 0.0303 6656/20000 [========>.....................] - ETA: 4s - loss: 0.0291 6880/20000 [=========>....................] - ETA: 3s - loss: 0.0282 7136/20000 [=========>....................] - ETA: 3s - loss: 0.0272 7392/20000 [==========>...................] - ETA: 3s - loss: 0.0262 7648/20000 [==========>...................] - ETA: 3s - loss: 0.0254 7904/20000 [==========>...................] - ETA: 3s - loss: 0.0246 8160/20000 [===========>..................] - ETA: 3s - loss: 0.0238 8416/20000 [===========>..................] - ETA: 3s - loss: 0.0231 8672/20000 [============>.................] - ETA: 3s - loss: 0.0224 8928/20000 [============>.................] - ETA: 3s - loss: 0.0218 9184/20000 [============>.................] - ETA: 2s - loss: 0.0212 9440/20000 [=============>................] - ETA: 2s - loss: 0.0206 9696/20000 [=============>................] - ETA: 2s - loss: 0.0200 9952/20000 [=============>................] - ETA: 2s - loss: 0.0195 10208/20000 [==============>...............] - ETA: 2s - loss: 0.0190 10464/20000 [==============>...............] - ETA: 2s - loss: 0.0186 10720/20000 [===============>..............] - ETA: 2s - loss: 0.0181 10976/20000 [===============>..............] - ETA: 2s - loss: 0.0177 11232/20000 [===============>..............] - ETA: 2s - loss: 0.0173 11488/20000 [================>.............] - ETA: 2s - loss: 0.0169 11712/20000 [================>.............] - ETA: 2s - loss: 0.0166 11968/20000 [================>.............] - ETA: 2s - loss: 0.0163 12224/20000 [=================>............] - ETA: 2s - loss: 0.0159 12480/20000 [=================>............] - ETA: 1s - loss: 0.0156 12736/20000 [==================>...........] - ETA: 1s - loss: 0.0153 12992/20000 [==================>...........] - ETA: 1s - loss: 0.0150 13248/20000 [==================>...........] - ETA: 1s - loss: 0.0147 13504/20000 [===================>..........] - ETA: 1s - loss: 0.0144 13760/20000 [===================>..........] - ETA: 1s - loss: 0.0141 14016/20000 [====================>.........] - ETA: 1s - loss: 0.0139 14272/20000 [====================>.........] - ETA: 1s - loss: 0.0136 14528/20000 [====================>.........] - ETA: 1s - loss: 0.0134 14784/20000 [=====================>........] - ETA: 1s - loss: 0.0132 15040/20000 [=====================>........] - ETA: 1s - loss: 0.0129 15296/20000 [=====================>........] - ETA: 1s - loss: 0.0127 15552/20000 [======================>.......] - ETA: 1s - loss: 0.0125 15808/20000 [======================>.......] - ETA: 1s - loss: 0.0123 16064/20000 [=======================>......] - ETA: 0s - loss: 0.0121 16320/20000 [=======================>......] - ETA: 0s - loss: 0.0119 16576/20000 [=======================>......] - ETA: 0s - loss: 0.0118 16832/20000 [========================>.....] - ETA: 0s - loss: 0.0116 17088/20000 [========================>.....] - ETA: 0s - loss: 0.0114 17344/20000 [=========================>....] - ETA: 0s - loss: 0.0112 17600/20000 [=========================>....] - ETA: 0s - loss: 0.0111 17856/20000 [=========================>....] - ETA: 0s - loss: 0.0109 18144/20000 [==========================>...] - ETA: 0s - loss: 0.0107 18400/20000 [==========================>...] - ETA: 0s - loss: 0.0106 18656/20000 [==========================>...] - ETA: 0s - loss: 0.0104 18912/20000 [===========================>..] - ETA: 0s - loss: 0.0103 19168/20000 [===========================>..] - ETA: 0s - loss: 0.0102 19456/20000 [============================>.] - ETA: 0s - loss: 0.0100 19712/20000 [============================>.] - ETA: 0s - loss: 0.0099 19968/20000 [============================>.] - ETA: 0s - loss: 0.0098 20000/20000 [==============================] - 5s 236us/step - loss: 0.0097
引入模块：
from tqdm import tqdm_notebook as tqdm
执行代码：
def sample_noise(G, noise_dim=10, n_samples=10000):
X = np.random.uniform(0, 1, size=[n_samples, noise_dim])
y = np.zeros((n_samples, 2))
y[:, 1] = 1
return X, y
def train(GAN, G, D, epochs=200, n_samples=10000, noise_dim=10, batch_size=32, verbose=False, v_freq=50):
d_loss = []
g_loss = []
e_range = range(epochs)
if verbose:
e_range = tqdm(e_range)
for epoch in e_range:
X, y = sample_data_and_gen(G, n_samples=n_samples, noise_dim=noise_dim)
set_trainability(D, True)
d_loss.append(D.train_on_batch(X, y))
X, y = sample_noise(G, n_samples=n_samples, noise_dim=noise_dim)
set_trainability(D, False)
g_loss.append(GAN.train_on_batch(X, y))
if verbose and (epoch + 1) % v_freq == 0:
print("Epoch #{}: Generative Loss: {}, Discriminative Loss: {}".format(epoch + 1, g_loss[-1], d_loss[-1]))
return d_loss, g_loss
d_loss, g_loss = train(GAN, G, D, verbose=True)
返回信息：
HBox(children=(IntProgress(value=0, max=200), HTML(value=''))) Epoch #50: Generative Loss: 5.842154026031494, Discriminative Loss: 0.4683375060558319 Epoch #100: Generative Loss: 3.4111320972442627, Discriminative Loss: 0.13123030960559845 Epoch #150: Generative Loss: 5.5205817222595215, Discriminative Loss: 0.03762095794081688 Epoch #200: Generative Loss: 4.994686603546143, Discriminative Loss: 0.045186348259449005
执行代码：
ax = pd.DataFrame(
{
'Generative Loss': g_loss,
'Discriminative Loss': d_loss,
}
).plot(title='Training loss', logy=True)
ax.set_xlabel("Epochs")
ax.set_ylabel("Loss")
plt.show()
生成图像：
执行代码：
N_VIEWED_SAMPLES = 2 data_and_gen, _ = sample_data_and_gen(G, n_samples=N_VIEWED_SAMPLES) pd.DataFrame(np.transpose(data_and_gen[N_VIEWED_SAMPLES:])).plot() plt.show()
生成图像：
执行代码：
N_VIEWED_SAMPLES = 2 data_and_gen, _ = sample_data_and_gen(G, n_samples=N_VIEWED_SAMPLES) pd.DataFrame(np.transpose(data_and_gen[N_VIEWED_SAMPLES:])).rolling(5).mean()[5:].plot() plt.show()
生成图像：
完整代码如下：
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from keras.models import Model
from keras.layers import Dense, Activation, Input, Reshape
from keras.layers import Conv1D, Flatten, Dropout
from keras.optimizers import SGD, Adam
from tqdm import tqdm_notebook as tqdm
#sec
def sample_data(n_samples=10000, x_vals=np.arange(0, 5, .1), max_offset=100, mul_range=[1, 2]):
vectors = []
for i in range(n_samples):
offset = np.random.random() * max_offset
mul = mul_range[0] + np.random.random() * (mul_range[1] - mul_range[0])
vectors.append(
np.sin(offset + x_vals * mul) / 2 + .5
)
return np.array(vectors)
ax = pd.DataFrame(np.transpose(sample_data(5))).plot()
plt.show()
#sec
def get_generative(G_in, dense_dim=200, out_dim=50, lr=1e-3):
x = Dense(dense_dim)(G_in)
x = Activation('tanh')(x)
G_out = Dense(out_dim, activation='tanh')(x)
G = Model(G_in, G_out)
opt = SGD(lr=lr)
G.compile(loss='binary_crossentropy', optimizer=opt)
return G, G_out
G_in = Input(shape=[10])
G, G_out = get_generative(G_in)
G.summary()
#sec
def get_discriminative(D_in, lr=1e-3, drate=.25, n_channels=50, conv_sz=5, leak=.2):
x = Reshape((-1, 1))(D_in)
x = Conv1D(n_channels, conv_sz, activation='relu')(x)
x = Dropout(drate)(x)
x = Flatten()(x)
x = Dense(n_channels)(x)
D_out = Dense(2, activation='sigmoid')(x)
D = Model(D_in, D_out)
dopt = Adam(lr=lr)
D.compile(loss='binary_crossentropy', optimizer=dopt)
return D, D_out
D_in = Input(shape=[50])
D, D_out = get_discriminative(D_in)
D.summary()
#sec
def set_trainability(model, trainable=False):
model.trainable = trainable
for layer in model.layers:
layer.trainable = trainable
def make_gan(GAN_in, G, D):
set_trainability(D, False)
x = G(GAN_in)
GAN_out = D(x)
GAN = Model(GAN_in, GAN_out)
GAN.compile(loss='binary_crossentropy', optimizer=G.optimizer)
return GAN, GAN_out
GAN_in = Input([10])
GAN, GAN_out = make_gan(GAN_in, G, D)
GAN.summary()
#sec
def sample_data_and_gen(G, noise_dim=10, n_samples=10000):
XT = sample_data(n_samples=n_samples)
XN_noise = np.random.uniform(0, 1, size=[n_samples, noise_dim])
XN = G.predict(XN_noise)
X = np.concatenate((XT, XN))
y = np.zeros((2*n_samples, 2))
y[:n_samples, 1] = 1
y[n_samples:, 0] = 1
return X, y
def pretrain(G, D, noise_dim=10, n_samples=10000, batch_size=32):
X, y = sample_data_and_gen(G, n_samples=n_samples, noise_dim=noise_dim)
set_trainability(D, True)
D.fit(X, y, epochs=1, batch_size=batch_size)
pretrain(G, D)
#sec
def sample_noise(G, noise_dim=10, n_samples=10000):
X = np.random.uniform(0, 1, size=[n_samples, noise_dim])
y = np.zeros((n_samples, 2))
y[:, 1] = 1
return X, y
def train(GAN, G, D, epochs=200, n_samples=10000, noise_dim=10, batch_size=32, verbose=False, v_freq=50):
d_loss = []
g_loss = []
e_range = range(epochs)
if verbose:
e_range = tqdm(e_range)
for epoch in e_range:
X, y = sample_data_and_gen(G, n_samples=n_samples, noise_dim=noise_dim)
set_trainability(D, True)
d_loss.append(D.train_on_batch(X, y))
X, y = sample_noise(G, n_samples=n_samples, noise_dim=noise_dim)
set_trainability(D, False)
g_loss.append(GAN.train_on_batch(X, y))
if verbose and (epoch + 1) % v_freq == 0:
print("Epoch #{}: Generative Loss: {}, Discriminative Loss: {}".format(epoch + 1, g_loss[-1], d_loss[-1]))
return d_loss, g_loss
d_loss, g_loss = train(GAN, G, D, verbose=True)
#sec
ax = pd.DataFrame(
{
'Generative Loss': g_loss,
'Discriminative Loss': d_loss,
}
).plot(title='Training loss', logy=True)
ax.set_xlabel("Epochs")
ax.set_ylabel("Loss")
plt.show()
#sec
N_VIEWED_SAMPLES = 2
data_and_gen, _ = sample_data_and_gen(G, n_samples=N_VIEWED_SAMPLES)
pd.DataFrame(np.transpose(data_and_gen[N_VIEWED_SAMPLES:])).plot()
plt.show()
#sec
N_VIEWED_SAMPLES = 2
data_and_gen, _ = sample_data_and_gen(G, n_samples=N_VIEWED_SAMPLES)
pd.DataFrame(np.transpose(data_and_gen[N_VIEWED_SAMPLES:])).rolling(5).mean()[5:].plot()
plt.show()
参考：
https://blog.csdn.net/tanmx219/article/details/88074600
https://blog.csdn.net/xqf1528399071/article/details/53385593
Keras入门——（3）生成式对抗网络GAN的更多相关文章
- 生成式对抗网络GAN 的研究进展与展望
生成式对抗网络GAN的研究进展与展望.pdf 摘要: 生成式对抗网络GAN (Generative adversarial networks) 目前已经成为人工智能学界一个热门的研究方向. GAN的基 ...
- 【CV论文阅读】生成式对抗网络GAN
生成式对抗网络GAN 1. 基本GAN 在论文<Generative Adversarial Nets>提出的GAN是最原始的框架,可以看成极大极小博弈的过程,因此称为“对抗网络”.一般 ...
- 生成式对抗网络(GAN)实战——书法字体生成练习赛
https://www.tinymind.cn/competitions/ai 生成式对抗网络(GAN)是近年来大热的深度学习模型. 目前GAN最常使用的场景就是图像生成,作为一种优秀的生成式模型,G ...
- 【神经网络与深度学习】生成式对抗网络GAN研究进展（五）——Deep Convolutional Generative Adversarial Nerworks，DCGAN
[前言] 本文首先介绍生成式模型,然后着重梳理生成式模型(Generative Models)中生成对抗网络(Generative Adversarial Network)的研究与发展.作者 ...
- 不要怂，就是GAN (生成式对抗网络) （一）
前面我们用 TensorFlow 写了简单的 cifar10 分类的代码,得到还不错的结果,下面我们来研究一下生成式对抗网络 GAN,并且用 TensorFlow 代码实现. 自从 Ian Goodf ...
- 不要怂，就是GAN (生成式对抗网络) （一）： GAN 简介
前面我们用 TensorFlow 写了简单的 cifar10 分类的代码,得到还不错的结果,下面我们来研究一下生成式对抗网络 GAN,并且用 TensorFlow 代码实现. 自从 Ian Goodf ...
- 生成式对抗网络（GAN）学习笔记
图像识别和自然语言处理是目前应用极为广泛的AI技术,这些技术不管是速度还是准确度都已经达到了相当的高度,具体应用例如智能手机的人脸解锁.内置的语音助手.这些技术的实现和发展都离不开神经网络,可是传统的 ...
- AI 生成式对抗网络（GAN）
生成式对抗网络(Generative Adversarial Network,简称GAN),主要由两部分构成:生成模型G和判别模型D.训练GAN就是两种模型的对抗过程. 生成模型:利用任意噪音(ran ...
- GAN生成式对抗网络（四）——SRGAN超高分辨率图片重构
论文pdf 地址:https://arxiv.org/pdf/1609.04802v1.pdf 我的实际效果 清晰度距离我的期待有距离. 颜色上面存在差距. 解决想法 增加一个颜色判别器.将颜色值反馈 ...
随机推荐
- dwz中的(tree)树形菜单的默认收缩
做网站后台时,为了方便管理,可能会用到dwz中的树形菜单,如下: 树形菜单的收缩有默认属性值,可以对其进行一定的初始设定: DWZ的树结构是按<ul>,<li>的嵌套格式构成, ...
- linux文本处理工具-1
文件内容: cat ,more,less 文件截取:head,tail 按列抽取:cut 排序和统计:sort,wc ----------------------------------------- ...
- 无线连接网络-FAST SSID Change
这篇随笔主要记录的是Apple设备连接思科无线可能出现的问题,尤其是在思科WLC3504下部署的无线网络,这种故障体现的尤为明显. For Single SSID To support Apple i ...
- Java面向对象编程 -2.2
构造方法与匿名对象 现在的程序在使用类的时候一般都按照了如下的步骤进行: 声明并实例化对象,这个时候实例化对象中的属性并没有任何的数据存在,都是其对于数据类型的默认值 需要通过一系列的setter方法 ...
- idea修改项目编码
- idea隐藏配置文件
- LockSupport源码分析
LockSupport提供park()和unpark()方法实现线程阻塞和唤醒.底层实现是通过sun.misc.Unsafe的park和unpark. 关于sun.misc.Unsafe的说明请参见我 ...
- 改写画质、突破性能， Unity 全面升级！
技术变革,时代更迭.从<神庙逃亡>.<暗影之枪>等主流手游到独立联网的大型游戏,从绚丽多彩的影视动画到具备极致体验的运输建筑制造行业,从传统的2D 到立体3D 乃至沉浸式的VR ...
- 苹果应用商店AppStore审核规则指南
http://www.zesmob.com/blog/40161.html 新应用上架苹果AppStore或重大版本更新时,往往会被拒多次,造成审核不通过的原因,主要是因为对苹果应用商店AppStor ...
- 【剑指Offer面试编程题】题目1362：左旋转字符串--九度OJ
题目描述: 汇编语言中有一种移位指令叫做循环左移(ROL),现在有个简单的任务,就是用字符串模拟这个指令的运算结果.对于一个给定的字符序列S,请你把其循环左移K位后的序列输出.例如,字符序列S=&qu ...