原文
本博客是 One Day One GAN [DAY 2] 的 learning notes！GAN 是用 CNN 搭建的！！！
DCGAN：《Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks》
把 GAN 中的 MLP 换成了 CNN，应用在人脸生成中！
文章目录
1 GAN 的介绍
有关 GAN 的介绍可以参考 【Keras-MLP-GAN】MNIST，更多有关 GAN 的小实验，可以参考 【Programming】 中的 3.5 小节！
2 DCGAN for MNIST
2.1 导入必要的库
from keras.datasets import mnist
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam
import matplotlib.pyplot as plt
import matplotlib
matplotlib.use('Agg')
import sys
import numpy as np
2.2 搭建 generator
输入 100 维的 noisy，输出 （28，28，1）的图片
100 → 128*7*7 reshape 成（7，7，128）→上采样（14，14，128）→ conv（14，14，128）→上采样（28，28，128）→ conv（28，28，64）→ conv（28，28，1）
# build_generator(self)
model = Sequential()
model.add(Dense(128 * 7 * 7, activation="relu", input_dim=100)) # 7,7,128
model.add(Reshape((7, 7, 128)))
model.add(UpSampling2D()) # 14,14,128
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(Activation("relu"))
model.add(UpSampling2D()) # 28,28,128
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(Activation("relu"))
model.add(Conv2D(1, kernel_size=3, padding="same"))
model.add(Activation("tanh"))
model.summary()
noise = Input(shape=(100,))
img = model(noise)
generator = Model(noise, img)
output
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_1 (Dense) (None, 6272) 633472
_________________________________________________________________
reshape_1 (Reshape) (None, 7, 7, 128) 0
_________________________________________________________________
up_sampling2d_1 (UpSampling2 (None, 14, 14, 128) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None, 14, 14, 128) 147584
_________________________________________________________________
batch_normalization_1 (Batch (None, 14, 14, 128) 512
_________________________________________________________________
activation_1 (Activation) (None, 14, 14, 128) 0
_________________________________________________________________
up_sampling2d_2 (UpSampling2 (None, 28, 28, 128) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 28, 28, 64) 73792
_________________________________________________________________
batch_normalization_2 (Batch (None, 28, 28, 64) 256
_________________________________________________________________
activation_2 (Activation) (None, 28, 28, 64) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 28, 28, 1) 577
_________________________________________________________________
activation_3 (Activation) (None, 28, 28, 1) 0
=================================================================
Total params: 856,193
Trainable params: 855,809
Non-trainable params: 384
_________________________________________________________________
2.3 搭建 discriminator
输入（28，28，1），输出概率值
（28，28，1）→ conv（14，14，32）→ conv（7，7，64）→ padding （8，8，64）→ conv（4，4，128）→ conv（4，4，256）→ flatten 4*4*256 → 1
# build_discriminator
model = Sequential()
model.add(Conv2D(32, kernel_size=3, strides=2, input_shape=(28,28,1), padding="same")) # 14,14,32
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same")) # 7,7,64
model.add(ZeroPadding2D(padding=((0,1),(0,1)))) # 8,8,64
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same")) # 4,4,128
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(256, kernel_size=3, strides=1, padding="same")) # 4,4,256
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Flatten()) # 4*4*256
model.add(Dense(1, activation='sigmoid'))
model.summary()
img = Input(shape=(28,28,1)) # 输入 （28，28，1）
validity = model(img) # 输出二分类结果
discriminator = Model(img, validity)
output
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_4 (Conv2D) (None, 14, 14, 32) 320
_________________________________________________________________
leaky_re_lu_1 (LeakyReLU) (None, 14, 14, 32) 0
_________________________________________________________________
dropout_1 (Dropout) (None, 14, 14, 32) 0
_________________________________________________________________
conv2d_5 (Conv2D) (None, 7, 7, 64) 18496
_________________________________________________________________
zero_padding2d_1 (ZeroPaddin (None, 8, 8, 64) 0
_________________________________________________________________
batch_normalization_3 (Batch (None, 8, 8, 64) 256
_________________________________________________________________
leaky_re_lu_2 (LeakyReLU) (None, 8, 8, 64) 0
_________________________________________________________________
dropout_2 (Dropout) (None, 8, 8, 64) 0
_________________________________________________________________
conv2d_6 (Conv2D) (None, 4, 4, 128) 73856
_________________________________________________________________
batch_normalization_4 (Batch (None, 4, 4, 128) 512
_________________________________________________________________
leaky_re_lu_3 (LeakyReLU) (None, 4, 4, 128) 0
_________________________________________________________________
dropout_3 (Dropout) (None, 4, 4, 128) 0
_________________________________________________________________
conv2d_7 (Conv2D) (None, 4, 4, 256) 295168
_________________________________________________________________
batch_normalization_5 (Batch (None, 4, 4, 256) 1024
_________________________________________________________________
leaky_re_lu_4 (LeakyReLU) (None, 4, 4, 256) 0
_________________________________________________________________
dropout_4 (Dropout) (None, 4, 4, 256) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 4096) 0
_________________________________________________________________
dense_2 (Dense) (None, 1) 4097
=================================================================
Total params: 393,729
Trainable params: 392,833
Non-trainable params: 896
_________________________________________________________________
2.4 compile 模型，对学习过程进行配置
optimizer = Adam(0.0002, 0.5)
# discriminator
discriminator.compile(loss='binary_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
# The combined model (stacked generator and discriminator)
z = Input(shape=(100,))
img = generator(z)
validity = discriminator(img)
# For the combined model we will only train the generator
discriminator.trainable = False
# Trains the generator to fool the discriminator
combined = Model(z, validity)
combined.summary()
combined.compile(loss='binary_crossentropy',
optimizer=optimizer)
output
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_3 (InputLayer) (None, 100) 0
_________________________________________________________________
model_1 (Model) (None, 28, 28, 1) 856193
_________________________________________________________________
model_2 (Model) (None, 1) 393729
=================================================================
Total params: 1,249,922
Trainable params: 855,809
Non-trainable params: 394,113
_________________________________________________________________
2.5 保存生成的图片
def sample_images(epoch):
r, c = 5, 5
noise = np.random.normal(0, 1, (r * c, 100))
gen_imgs = generator.predict(noise)
# Rescale images 0 - 1
gen_imgs = 0.5 * gen_imgs + 0.5
fig, axs = plt.subplots(r, c)
cnt = 0
for i in range(r):
for j in range(c):
axs[i,j].imshow(gen_imgs[cnt, :,:,0], cmap='gray')
axs[i,j].axis('off')
cnt += 1
fig.savefig("images/%d.png" % epoch)
plt.close()
2.6 训练
batch_size = 32
sample_interval = 50
# Load the dataset
(X_train, _), (_, _) = mnist.load_data() # (60000,28,28)
# Rescale -1 to 1
X_train = X_train / 127.5 - 1.
X_train = np.expand_dims(X_train, axis=3) # (60000,28,28,1)
# Adversarial ground truths
valid = np.ones((batch_size, 1))
fake = np.zeros((batch_size, 1))
for epoch in range(4001):
# ---------------------
# Train Discriminator
# ---------------------
# Select a random batch of images
idx = np.random.randint(0, X_train.shape[0], batch_size) # 0-60000 中随机抽
imgs = X_train[idx]
noise = np.random.normal(0, 1, (batch_size, 100))# 生成标准的高斯分布噪声
# Generate a batch of new images
gen_imgs = generator.predict(noise)
# Train the discriminator
d_loss_real = discriminator.train_on_batch(imgs, valid) #真实数据对应标签１
d_loss_fake = discriminator.train_on_batch(gen_imgs, fake) #生成的数据对应标签０
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
# ---------------------
# Train Generator
# ---------------------
noise = np.random.normal(0, 1, (batch_size, 100))
# Train the generator (to have the discriminator label samples as valid)
g_loss = combined.train_on_batch(noise, valid)
# Plot the progress
if epoch % 50==0:
print ("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100*d_loss[1], g_loss))
# If at save interval => save generated image samples
if epoch % sample_interval == 0:
sample_images(epoch)
每隔 50 iteration 打印一次结果，保存一次图片（代码中的 epoch 理解为 iteration）
output
0 [D loss: 1.110515, acc.: 34.38%] [G loss: 0.947779]
50 [D loss: 0.857457, acc.: 56.25%] [G loss: 1.294673]
100 [D loss: 0.849929, acc.: 48.44%] [G loss: 1.071479]
………………
3900 [D loss: 0.715688, acc.: 56.25%] [G loss: 1.014293]
3950 [D loss: 0.772328, acc.: 45.31%] [G loss: 1.014349]
4000 [D loss: 0.596769, acc.: 71.88%] [G loss: 1.198742]
2.7 结果展示
iteration 0
iteration 50
iteration 100
iteration 3900
iteration 3950
iteration 4000
3 DCGAN for CIFAR-10
哈哈，用这套代码改改输入玩玩 cifar-10 看看，MNIST 图片是（28，28，1），cifar 则是（32，32，3）
3.1 导入必要的库
这里要导入 cifar10
from keras.datasets import cifar10
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam
import matplotlib.pyplot as plt
import matplotlib
matplotlib.use('Agg')
import sys
import numpy as np
3.2 搭建 generator
注意 feature map 的 size 即可
# build_generator(self)
model = Sequential()
model.add(Dense(128 * 8 * 8, activation="relu", input_dim=100)) # 8,8,128
model.add(Reshape((8, 8, 128)))
model.add(UpSampling2D()) # 16,16,128
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(Activation("relu"))
model.add(UpSampling2D()) # 32,32,128
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(Activation("relu"))
model.add(Conv2D(3, kernel_size=3, padding="same"))
model.add(Activation("tanh"))
model.summary()
noise = Input(shape=(100,))
img = model(noise)
generator = Model(noise, img)
output
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_1 (Dense) (None, 8192) 827392
_________________________________________________________________
reshape_1 (Reshape) (None, 8, 8, 128) 0
_________________________________________________________________
up_sampling2d_1 (UpSampling2 (None, 16, 16, 128) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None, 16, 16, 128) 147584
_________________________________________________________________
batch_normalization_1 (Batch (None, 16, 16, 128) 512
_________________________________________________________________
activation_1 (Activation) (None, 16, 16, 128) 0
_________________________________________________________________
up_sampling2d_2 (UpSampling2 (None, 32, 32, 128) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 32, 32, 64) 73792
_________________________________________________________________
batch_normalization_2 (Batch (None, 32, 32, 64) 256
_________________________________________________________________
activation_2 (Activation) (None, 32, 32, 64) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 32, 32, 3) 1731
_________________________________________________________________
activation_3 (Activation) (None, 32, 32, 3) 0
=================================================================
Total params: 1,051,267
Trainable params: 1,050,883
Non-trainable params: 384
_________________________________________________________________
3.3 搭建 discriminator
注意输入的改变即可
# build_discriminator
model = Sequential()
model.add(Conv2D(32, kernel_size=3, strides=2, input_shape=(32,32,3), padding="same")) # 16,16,32
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same")) # 8,8,64
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same")) # 4,4,128
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(256, kernel_size=3, strides=1, padding="same")) # 4,4,256
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Flatten()) # 4*4*256
model.add(Dense(1, activation='sigmoid'))
model.summary()
img = Input(shape=(32,32,3)) # 输入 （28，28，1）
validity = model(img) # 输出二分类结果
discriminator = Model(img, validity)
output
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_4 (Conv2D) (None, 16, 16, 32) 896
_________________________________________________________________
leaky_re_lu_1 (LeakyReLU) (None, 16, 16, 32) 0
_________________________________________________________________
dropout_1 (Dropout) (None, 16, 16, 32) 0
_________________________________________________________________
conv2d_5 (Conv2D) (None, 8, 8, 64) 18496
_________________________________________________________________
batch_normalization_3 (Batch (None, 8, 8, 64) 256
_________________________________________________________________
leaky_re_lu_2 (LeakyReLU) (None, 8, 8, 64) 0
_________________________________________________________________
dropout_2 (Dropout) (None, 8, 8, 64) 0
_________________________________________________________________
conv2d_6 (Conv2D) (None, 4, 4, 128) 73856
_________________________________________________________________
batch_normalization_4 (Batch (None, 4, 4, 128) 512
_________________________________________________________________
leaky_re_lu_3 (LeakyReLU) (None, 4, 4, 128) 0
_________________________________________________________________
dropout_3 (Dropout) (None, 4, 4, 128) 0
_________________________________________________________________
conv2d_7 (Conv2D) (None, 4, 4, 256) 295168
_________________________________________________________________
batch_normalization_5 (Batch (None, 4, 4, 256) 1024
_________________________________________________________________
leaky_re_lu_4 (LeakyReLU) (None, 4, 4, 256) 0
_________________________________________________________________
dropout_4 (Dropout) (None, 4, 4, 256) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 4096) 0
_________________________________________________________________
dense_2 (Dense) (None, 1) 4097
=================================================================
Total params: 394,305
Trainable params: 393,409
Non-trainable params: 896
_________________________________________________________________
3.4 compile 模型，对学习过程进行配置
optimizer = Adam(0.0002, 0.5)
# discriminator
discriminator.compile(loss='binary_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
# The combined model (stacked generator and discriminator)
z = Input(shape=(100,))
img = generator(z)
validity = discriminator(img)
# For the combined model we will only train the generator
discriminator.trainable = False
# Trains the generator to fool the discriminator
combined = Model(z, validity)
combined.summary()
combined.compile(loss='binary_crossentropy',
optimizer=optimizer)
output
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_3 (InputLayer) (None, 100) 0
_________________________________________________________________
model_1 (Model) (None, 32, 32, 3) 1051267
_________________________________________________________________
model_2 (Model) (None, 1) 394305
=================================================================
Total params: 1,445,572
Trainable params: 1,050,883
Non-trainable params: 394,689
_________________________________________________________________
3.5 保存生成的图片
这里要注意修改一下，因为 MNIST 产生的是黑白，CIFAR-10 则产生的是彩色
def sample_images(epoch):
r, c = 5, 5
noise = np.random.normal(0, 1, (r * c, 100))
gen_imgs = generator.predict(noise)
# Rescale images 0 - 1
gen_imgs = (0.5 * gen_imgs + 0.5)
fig, axs = plt.subplots(r, c)
cnt = 0
for i in range(r):
for j in range(c):
axs[i,j].imshow(gen_imgs[cnt, :,:,:])# 注意这里的改变
axs[i,j].axis('off')
cnt += 1
fig.savefig("images/%d.png" % epoch)
plt.close()
3.6 训练
训练 50001 个 iteration，每 500 个 iteration 输出一次结果
batch_size = 32
sample_interval = 500
# Load the dataset
(X_train,_),(_,_)=cifar10.load_data()#(50000, 32, 32, 3)
# Rescale -1 to 1
X_train = X_train / 127.5 - 1.
# Adversarial ground truths
valid = np.ones((batch_size, 1))
fake = np.zeros((batch_size, 1))
for epoch in range(50001):
# ---------------------
# Train Discriminator
# ---------------------
# Select a random batch of images
idx = np.random.randint(0, X_train.shape[0], batch_size) # 0-60000 中随机抽
imgs = X_train[idx]
noise = np.random.normal(0, 1, (batch_size, 100))# 生成标准的高斯分布噪声
# Generate a batch of new images
gen_imgs = generator.predict(noise)
# Train the discriminator
d_loss_real = discriminator.train_on_batch(imgs, valid) #真实数据对应标签１
d_loss_fake = discriminator.train_on_batch(gen_imgs, fake) #生成的数据对应标签０
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
# ---------------------
# Train Generator
# ---------------------
noise = np.random.normal(0, 1, (batch_size, 100))
# Train the generator (to have the discriminator label samples as valid)
g_loss = combined.train_on_batch(noise, valid)
# Plot the progress
if epoch % 500==0:
print ("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100*d_loss[1], g_loss))
# If at save interval => save generated image samples
if epoch % sample_interval == 0:
sample_images(epoch)
output
0 [D loss: 1.069107, acc.: 43.75%] [G loss: 0.424518]
500 [D loss: 0.858720, acc.: 45.31%] [G loss: 1.105853]
1000 [D loss: 0.807555, acc.: 46.88%] [G loss: 0.961553]
……
49000 [D loss: 0.835431, acc.: 35.94%] [G loss: 0.684724]
49500 [D loss: 0.745767, acc.: 48.44%] [G loss: 0.881849]
50000 [D loss: 0.651077, acc.: 57.81%] [G loss: 0.958367]
3.7 结果展示
iteration 0
iteration 500
iteration 1000
iteration 49000
iteration 49500
iteration 50000
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