最近对对抗生成网络GAN比较感兴趣，相关知识点文章还在编辑中，以下这个是一个练手的小项目~
(在原模型上做了，为了减少计算量让其好训练一些。)
一、导入工具包
import tensorflow as tf
from tensorflow.keras import layers
import numpy as np
import os
import time
import glob
import matplotlib.pyplot as plt
from IPython.display import clear_output
from IPython import display1.1 设置GPU
gpus = tf.config.list_physical_devices("GPU")
if gpus:
gpu0 = gpus[0] #如果有多个GPU，仅使用第0个GPU
tf.config.experimental.set_memory_growth(gpu0, True) #设置GPU显存用量按需使用
tf.config.set_visible_devices([gpu0],"GPU")
gpus [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
二、导入训练数据
链接： 点这里
fileList = glob.glob('./ani_face/*.jpg')
len(fileList)41621
2.1 数据可视化
# 随机显示几张图
for index,i in enumerate(fileList[:3]):
display.display(display.Image(fileList[index]))
2.2 数据预处理
# 文件名列表
path_ds = tf.data.Dataset.from_tensor_slices(fileList)
# 预处理，归一化，缩放
def load_and_preprocess_image(path):
image = tf.io.read_file(path)
image = tf.image.decode_jpeg(image, channels=3)
image = tf.image.resize(image, [64, 64])
image /= 255.0 # normalize to [0,1] range
image = tf.reshape(image, [1, 64,64,3])
return image
image_ds = path_ds.map(load_and_preprocess_image)
image_ds<MapDataset shapes: (1, 64, 64, 3), types: tf.float32>
# 查看一张图片
for x in image_ds:
plt.axis("off")
plt.imshow((x.numpy() * 255).astype("int32")[0])
break
三、网络构建
3.1 D网络
discriminator = keras.Sequential(
[
keras.Input(shape=(64, 64, 3)),
layers.Conv2D(64, kernel_size=4, strides=2, padding="same"),
layers.LeakyReLU(alpha=0.2),
layers.Conv2D(128, kernel_size=4, strides=2, padding="same"),
layers.LeakyReLU(alpha=0.2),
layers.Conv2D(128, kernel_size=4, strides=2, padding="same"),
layers.LeakyReLU(alpha=0.2),
layers.Flatten(),
layers.Dropout(0.2),
layers.Dense(1, activation="sigmoid"),
],
name="discriminator",
)
discriminator.summary()Model: "discriminator" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= conv2d (Conv2D) (None, 32, 32, 64) 3136 _________________________________________________________________ leaky_re_lu (LeakyReLU) (None, 32, 32, 64) 0 _________________________________________________________________ conv2d_1 (Conv2D) (None, 16, 16, 128) 131200 _________________________________________________________________ leaky_re_lu_1 (LeakyReLU) (None, 16, 16, 128) 0 _________________________________________________________________ conv2d_2 (Conv2D) (None, 8, 8, 128) 262272 _________________________________________________________________ leaky_re_lu_2 (LeakyReLU) (None, 8, 8, 128) 0 _________________________________________________________________ flatten (Flatten) (None, 8192) 0 _________________________________________________________________ dropout (Dropout) (None, 8192) 0 _________________________________________________________________ dense (Dense) (None, 1) 8193 ================================================================= Total params: 404,801 Trainable params: 404,801 Non-trainable params: 0
3.2 G网络
latent_dim = 128
generator = keras.Sequential(
[
keras.Input(shape=(latent_dim,)),
layers.Dense(8 * 8 * 128),
layers.Reshape((8, 8, 128)),
layers.Conv2DTranspose(128, kernel_size=4, strides=2, padding="same"),
layers.LeakyReLU(alpha=0.2),
layers.Conv2DTranspose(256, kernel_size=4, strides=2, padding="same"),
layers.LeakyReLU(alpha=0.2),
layers.Conv2DTranspose(512, kernel_size=4, strides=2, padding="same"),
layers.LeakyReLU(alpha=0.2),
layers.Conv2D(3, kernel_size=5, padding="same", activation="sigmoid"),
],
name="generator",
)
generator.summary()3.3 重写 train_step
class GAN(keras.Model):
def __init__(self, discriminator, generator, latent_dim):
super(GAN, self).__init__()
self.discriminator = discriminator
self.generator = generator
self.latent_dim = latent_dim
def compile(self, d_optimizer, g_optimizer, loss_fn):
super(GAN, self).compile()
self.d_optimizer = d_optimizer
self.g_optimizer = g_optimizer
self.loss_fn = loss_fn
self.d_loss_metric = keras.metrics.Mean(name="d_loss")
self.g_loss_metric = keras.metrics.Mean(name="g_loss")
@property
def metrics(self):
return [self.d_loss_metric, self.g_loss_metric]
def train_step(self, real_images):
# 生成噪音
batch_size = tf.shape(real_images)[0]
random_latent_vectors = tf.random.normal(shape=(batch_size, self.latent_dim))
# 生成的图片
generated_images = self.generator(random_latent_vectors)
# Combine them with real images
combined_images = tf.concat([generated_images, real_images], axis=0)
# Assemble labels discriminating real from fake images
labels = tf.concat(
[tf.ones((batch_size, 1)), tf.zeros((batch_size, 1))], axis=0
)
# Add random noise to the labels - important trick!
labels += 0.05 * tf.random.uniform(tf.shape(labels))
# 训练判别器，生成的当成0，真实的当成1
with tf.GradientTape() as tape:
predictions = self.discriminator(combined_images)
d_loss = self.loss_fn(labels, predictions)
grads = tape.gradient(d_loss, self.discriminator.trainable_weights)
self.d_optimizer.apply_gradients(
zip(grads, self.discriminator.trainable_weights)
)
# Sample random points in the latent space
random_latent_vectors = tf.random.normal(shape=(batch_size, self.latent_dim))
# Assemble labels that say "all real images"
misleading_labels = tf.zeros((batch_size, 1))
# Train the generator (note that we should *not* update the weights
# of the discriminator)!
with tf.GradientTape() as tape:
predictions = self.discriminator(self.generator(random_latent_vectors))
g_loss = self.loss_fn(misleading_labels, predictions)
grads = tape.gradient(g_loss, self.generator.trainable_weights)
self.g_optimizer.apply_gradients(zip(grads, self.generator.trainable_weights))
# Update metrics
self.d_loss_metric.update_state(d_loss)
self.g_loss_metric.update_state(g_loss)
return {
"d_loss": self.d_loss_metric.result(),
"g_loss": self.g_loss_metric.result(),
}3.4 设置回调函数
class GANMonitor(keras.callbacks.Callback):
def __init__(self, num_img=3, latent_dim=128):
self.num_img = num_img
self.latent_dim = latent_dim
def on_epoch_end(self, epoch, logs=None):
random_latent_vectors = tf.random.normal(shape=(self.num_img, self.latent_dim))
generated_images = self.model.generator(random_latent_vectors)
generated_images *= 255
generated_images.numpy()
for i in range(self.num_img):
img = keras.preprocessing.image.array_to_img(generated_images[i])
display.display(img)
img.save("gen_ani/generated_img_%03d_%d.png" % (epoch, i))四、训练模型
epochs = 100 # In practice, use ~100 epochs
gan = GAN(discriminator=discriminator, generator=generator, latent_dim=latent_dim)
gan.compile(
d_optimizer=keras.optimizers.Adam(learning_rate=0.0001),
g_optimizer=keras.optimizers.Adam(learning_rate=0.0001),
loss_fn=keras.losses.BinaryCrossentropy(),
)
gan.fit(
image_ds, epochs=epochs, callbacks=[GANMonitor(num_img=10, latent_dim=latent_dim)]
)五、保存模型
#保存模型
gan.generator.save('./data/ani_G_model')生成模型文件：点这里
六、生成漫画脸
G_model = tf.keras.models.load_model('./data/ani_G_model/',compile=False)
def randomGenerate():
noise_seed = tf.random.normal([16, 128])
predictions = G_model(noise_seed, training=False)
fig = plt.figure(figsize=(8, 8))
for i in range(predictions.shape[0]):
plt.subplot(4, 4, i+1)
img = (predictions[i].numpy() * 255 ).astype('int')
plt.imshow(img )
plt.axis('off')
plt.show()count = 0
while True:
randomGenerate()
clear_output(wait=True)
time.sleep(0.1)
if count > 100:
break
count+=1