GAN网络实现图像4倍超分辨
时间: 2024-02-21 18:57:23 浏览: 17
GAN(Generative Adversarial Networks)是一种常见的深度学习模型,可以用于图像超分辨率。以下是实现图像4倍超分辨的GAN网络的Python代码示例,使用Tensorflow和Keras框架实现:
```python
import tensorflow as tf
from tensorflow import keras
# 定义生成器模型
def generator_model():
input_img = keras.layers.Input(shape=(None, None, 3))
x = keras.layers.Conv2D(64, (3, 3), activation='relu', padding='same')(input_img)
x = keras.layers.Conv2D(64, (3, 3), activation='relu', padding='same')(x)
x = keras.layers.Conv2D(32, (3, 3), activation='relu', padding='same')(x)
x = keras.layers.Conv2DTranspose(32, (3, 3), strides=(2, 2), padding='same')(x)
x = keras.layers.Conv2D(16, (3, 3), activation='relu', padding='same')(x)
x = keras.layers.Conv2DTranspose(16, (3, 3), strides=(2, 2), padding='same')(x)
x = keras.layers.Conv2D(3, (3, 3), activation='sigmoid', padding='same')(x)
output_img = keras.layers.Lambda(lambda x: x * 255)(x)
model = keras.models.Model(input_img, output_img)
return model
# 定义判别器模型
def discriminator_model():
input_img = keras.layers.Input(shape=(None, None, 3))
x = keras.layers.Conv2D(64, (3, 3), activation='relu', padding='same')(input_img)
x = keras.layers.Conv2D(32, (3, 3), activation='relu', padding='same')(x)
x = keras.layers.Conv2D(16, (3, 3), activation='relu', padding='same')(x)
x = keras.layers.Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)
output = keras.layers.Flatten()(x)
model = keras.models.Model(input_img, output)
return model
# 定义GAN模型
def gan_model(generator, discriminator):
discriminator.trainable = False
input_img = keras.layers.Input(shape=(None, None, 3))
generated_img = generator(input_img)
output = discriminator(generated_img)
model = keras.models.Model(input_img, output)
model.compile(optimizer='adam', loss='binary_crossentropy')
return model
# 加载训练数据和测试数据
(x_train, _), (x_test, _) = keras.datasets.cifar10.load_data()
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
# 缩小训练数据和测试数据的尺寸为1/4
x_train_lowres = tf.image.resize(x_train, [32, 32])
x_test_lowres = tf.image.resize(x_test, [32, 32])
# 定义生成器、判别器和GAN模型
generator = generator_model()
discriminator = discriminator_model()
gan = gan_model(generator, discriminator)
# 训练GAN模型
for epoch in range(100):
for step in range(len(x_train_lowres)):
# 训练判别器
real_img = x_train[step:step+1]
real_label = tf.ones((1, 1))
fake_img = generator.predict(x_train_lowres[step:step+1])
fake_label = tf.zeros((1, 1))
x = tf.concat([real_img, fake_img], axis=0)
y = tf.concat([real_label, fake_label], axis=0)
discriminator_loss = discriminator.train_on_batch(x, y)
# 训练生成器
gan_label = tf.ones((1, 1))
generator_loss = gan.train_on_batch(x_train_lowres[step:step+1], gan_label)
# 使用测试数据进行测试
test_img = x_test_lowres[0:1]
test_img_highres = generator.predict(test_img)
test_img_highres = tf.clip_by_value(test_img_highres, 0, 1)
# 输出训练日志
print('Epoch: %d/%d, Discriminator Loss: %f, Generator Loss: %f' % (epoch+1, 100, discriminator_loss, generator_loss))
# 保存模型和测试图片
if (epoch+1) % 10 == 0:
generator.save('generator_%d.h5' % (epoch+1))
keras.preprocessing.image.save_img('test_img_highres_%d.png' % (epoch+1), test_img_highres[0])
```
该代码示例实现了一个简单的GAN模型,使用CIFAR-10数据集进行训练和测试。在训练过程中,使用低分辨率图像作为生成器的输入,使用判别器判断生成器生成的高分辨率图像是否为真实图像。在每个训练周期结束时,使用测试数据进行测试,并保存模型和测试图片。