使用 TensorFlow 和 Keras 的深度卷积 GAN
您可以按照 Jupyter 笔记本中的代码ch-14b_DCGAN。
在 DCGAN 中，判别器和生成器都是使用深度卷积网络实现的：
- 在此示例中，我们决定将生成器实现为以下网络：
Generator:
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
g_in (Dense) (None, 3200) 822400
_________________________________________________________________
g_in_act (Activation) (None, 3200) 0
_________________________________________________________________
g_in_reshape (Reshape) (None, 5, 5, 128) 0
_________________________________________________________________
g_0_up2d (UpSampling2D) (None, 10, 10, 128) 0
_________________________________________________________________
g_0_conv2d (Conv2D) (None, 10, 10, 64) 204864
_________________________________________________________________
g_0_act (Activation) (None, 10, 10, 64) 0
_________________________________________________________________
g_1_up2d (UpSampling2D) (None, 20, 20, 64) 0
_________________________________________________________________
g_1_conv2d (Conv2D) (None, 20, 20, 32) 51232
_________________________________________________________________
g_1_act (Activation) (None, 20, 20, 32) 0
_________________________________________________________________
g_2_up2d (UpSampling2D) (None, 40, 40, 32) 0
_________________________________________________________________
g_2_conv2d (Conv2D) (None, 40, 40, 16) 12816
_________________________________________________________________
g_2_act (Activation) (None, 40, 40, 16) 0
_________________________________________________________________
g_out_flatten (Flatten) (None, 25600) 0
_________________________________________________________________
g_out (Dense) (None, 784) 20071184
=================================================================
Total params: 21,162,496
Trainable params: 21,162,496
Non-trainable params: 0
- 生成器是一个更强大的网络，有三个卷积层，然后是 tanh 激活。我们将判别器网络定义如下：
Discriminator:
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
d_0_reshape (Reshape) (None, 28, 28, 1) 0
_________________________________________________________________
d_0_conv2d (Conv2D) (None, 28, 28, 64) 1664
_________________________________________________________________
d_0_act (Activation) (None, 28, 28, 64) 0
_________________________________________________________________
d_0_maxpool (MaxPooling2D) (None, 14, 14, 64) 0
_________________________________________________________________
d_out_flatten (Flatten) (None, 12544) 0
_________________________________________________________________
d_out (Dense) (None, 1) 12545
=================================================================
Total params: 14,209
Trainable params: 14,209
Non-trainable params: 0
_________________________________________________________________
- GAN 网络由判别器和生成器组成，如前所述：
GAN:
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_in (InputLayer) (None, 256) 0
_________________________________________________________________
g (Sequential) (None, 784) 21162496
_________________________________________________________________
d (Sequential) (None, 1) 14209
=================================================================
Total params: 21,176,705
Trainable params: 21,162,496
Non-trainable params: 14,209
_________________________________________________________________
当我们运行这个模型 400 个周期时，我们得到以下输出：
如您所见，DCGAN 能够从 epoch 100 本身开始生成高质量的数字。 DGCAN 已被用于样式转移，图像和标题的生成以及图像代数，即拍摄一个图像的一部分并将其添加到另一个图像的部分。 MNIST DCGAN 的完整代码在笔记本ch-14b_DCGAN 中提供。