基于百度飞浆的InfoGAN算法实现
时间: 2023-10-03 16:02:45 浏览: 217
InfoGAN是一种生成式对抗网络(GAN)的变体,它利用信息理论来学习数据的隐含表示。InfoGAN同时学习了生成器和判别器,以及一组连续和离散变量,这些变量用于控制生成器生成的图像的特征。在这个项目中,我们将使用百度飞浆实现InfoGAN算法。
首先,我们需要导入必要的库和模块:
```
import paddle
import paddle.fluid as fluid
import numpy as np
import os
import matplotlib.pyplot as plt
```
接下来,我们定义一些常量和超参数:
```
BATCH_SIZE = 128
EPOCH_NUM = 50
NOISE_DIM = 62
CAT_DIM = 10
CONT_DIM = 2
LR = 0.0002
BETA1 = 0.5
BETA2 = 0.999
```
其中,BATCH_SIZE是批大小,EPOCH_NUM是训练轮数,NOISE_DIM是噪声维度,CAT_DIM是离散变量的数量,CONT_DIM是连续变量的数量,LR是学习率,BETA1和BETA2是Adam优化器的超参数。
接下来,我们定义生成器和判别器网络:
```
def generator(noise, cat, cont):
noise_cat_cont = fluid.layers.concat([noise, cat, cont], axis=1)
fc1 = fluid.layers.fc(noise_cat_cont, size=1024)
bn1 = fluid.layers.batch_norm(fc1, act='relu')
fc2 = fluid.layers.fc(bn1, size=128 * 7 * 7)
bn2 = fluid.layers.batch_norm(fc2, act='relu')
reshape = fluid.layers.reshape(bn2, shape=(-1, 128, 7, 7))
conv1 = fluid.layers.conv2d_transpose(reshape, num_filters=64, filter_size=4, stride=2, padding=1)
bn3 = fluid.layers.batch_norm(conv1, act='relu')
conv2 = fluid.layers.conv2d_transpose(bn3, num_filters=1, filter_size=4, stride=2, padding=1, act='sigmoid')
return conv2
def discriminator(img, cat, cont):
conv1 = fluid.layers.conv2d(img, num_filters=64, filter_size=4, stride=2, padding=1, act='leaky_relu')
conv2 = fluid.layers.conv2d(conv1, num_filters=128, filter_size=4, stride=2, padding=1, act='leaky_relu')
reshape = fluid.layers.reshape(conv2, shape=(-1, 128 * 7 * 7))
cat_cont = fluid.layers.concat([cat, cont], axis=1)
cat_cont_expand = fluid.layers.expand(cat_cont, expand_times=(0, 128 * 7 * 7))
concat = fluid.layers.concat([reshape, cat_cont_expand], axis=1)
fc1 = fluid.layers.fc(concat, size=1024, act='leaky_relu')
fc2 = fluid.layers.fc(fc1, size=1)
return fc2
```
在生成器中,我们将噪声、离散变量和连续变量连接起来,经过两个全连接层和两个反卷积层后生成图像。在判别器中,我们将图像、离散变量和连续变量连接起来,经过两个卷积层和两个全连接层后输出判别结果。
接下来,我们定义损失函数和优化器:
```
noise = fluid.layers.data(name='noise', shape=[NOISE_DIM], dtype='float32')
cat = fluid.layers.data(name='cat', shape=[CAT_DIM], dtype='int64')
cont = fluid.layers.data(name='cont', shape=[CONT_DIM], dtype='float32')
real_img = fluid.layers.data(name='real_img', shape=[1, 28, 28], dtype='float32')
fake_img = generator(noise, cat, cont)
d_real = discriminator(real_img, cat, cont)
d_fake = discriminator(fake_img, cat, cont)
loss_d_real = fluid.layers.sigmoid_cross_entropy_with_logits(d_real, fluid.layers.fill_constant_batch_size_like(d_real, shape=[BATCH_SIZE, 1], value=1.0))
loss_d_fake = fluid.layers.sigmoid_cross_entropy_with_logits(d_fake, fluid.layers.fill_constant_batch_size_like(d_fake, shape=[BATCH_SIZE, 1], value=0.0))
loss_d = fluid.layers.mean(loss_d_real + loss_d_fake)
loss_g_fake = fluid.layers.sigmoid_cross_entropy_with_logits(d_fake, fluid.layers.fill_constant_batch_size_like(d_fake, shape=[BATCH_SIZE, 1], value=1.0))
loss_g = fluid.layers.mean(loss_g_fake)
opt_d = fluid.optimizer.Adam(learning_rate=LR, beta1=BETA1, beta2=BETA2)
opt_g = fluid.optimizer.Adam(learning_rate=LR, beta1=BETA1, beta2=BETA2)
opt_d.minimize(loss_d)
opt_g.minimize(loss_g)
```
在损失函数中,我们使用二元交叉熵损失函数,其中对于判别器,真实图像的标签为1,生成图像的标签为0;对于生成器,生成图像的标签为1。我们使用Adam优化器来训练模型。
接下来,我们定义训练过程:
```
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=500
),
batch_size=BATCH_SIZE
)
place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda() else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
for epoch_id in range(EPOCH_NUM):
for batch_id, data in enumerate(train_reader()):
noise_data = np.random.uniform(-1.0, 1.0, size=[BATCH_SIZE, NOISE_DIM]).astype('float32')
cat_data = np.random.randint(low=0, high=10, size=[BATCH_SIZE, CAT_DIM]).astype('int64')
cont_data = np.random.uniform(-1.0, 1.0, size=[BATCH_SIZE, CONT_DIM]).astype('float32')
real_img_data = np.array([x[0].reshape([1, 28, 28]) for x in data]).astype('float32')
d_loss, g_loss = exe.run(
fluid.default_main_program(),
feed={'noise': noise_data, 'cat': cat_data, 'cont': cont_data, 'real_img': real_img_data},
fetch_list=[loss_d, loss_g]
)
if batch_id % 100 == 0:
print("Epoch %d, Batch %d, D Loss: %f, G Loss: %f" % (epoch_id, batch_id, d_loss[0], g_loss[0]))
if batch_id % 500 == 0:
fake_img_data = exe.run(
fluid.default_main_program(),
feed={'noise': noise_data[:16], 'cat': cat_data[:16], 'cont': cont_data[:16]},
fetch_list=[fake_img]
)[0]
fig, axes = plt.subplots(nrows=4, ncols=4, figsize=(8, 8))
for i, ax in enumerate(axes.flatten()):
img = fake_img_data[i][0]
ax.imshow(img, cmap='gray')
ax.axis('off')
plt.show()
```
我们使用MNIST数据集进行训练,每次迭代从数据集中随机采样一个批次的数据。在每个迭代中,我们生成噪声、离散变量和连续变量,使用生成器生成图像,并对生成的图像和真实图像进行判别。根据损失函数计算判别器和生成器的损失,并使用Adam优化器更新网络参数。
每训练500个批次,我们使用生成器生成16张图像进行可视化。最后,我们输出生成的图像和训练过程中的损失。
完整代码如下:
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