My own implementation of Unrolled GAN of MNIST , and a DCGAN MNIST with pre-condition for the sake of comparison, using tensorflow.
Noticing the architecture might be inaccurate, I use 3 convolution in discriminator part, and each learning rate being 2e-4.
The batch norm parameters are not updated as other parameters while solving surrogate loss. I wonder if it is of great influence to results.
The output is poorly optimized, as you can see, there is still pretty many mode drop sample comparing with DCGAN：
Unrolled_GAN, K = 1,
$\eta$ =1e-4, generator_learning_rate=2e-4, discriminator_learning_rate=2e-4
DCGAN with pre-condition, generator_learning_rate=1e-4, discriminator_learning_rate=2e-4
MNIST data is required, and run
from tensorflow.examples.tutorials.mnist import input_data
input_data.read_data_sets('MNIST_data', one_hot=True)in shell would automatically download the data.