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Stabilizing_GANs

Implementation of the NIPS17 paper Stabilizing Training of Generative Adversarial Networks through Regularization.

Summary

Check also our slides and Ferenc Huszar's excellent blog post on the topic From Instance Noise to Gradient Regularisation.

Big thanks to Ishaan Gulrajani (igul222), Taehoon Kim (carpedm20) and Ben Poole (poolio) for their open-source GAN implementations, on which our modified code is based.

JS-Regularizer & Regularized GAN Objective:

Simply copy-paste this definition into your GAN code and modify the disc_loss += (gamma/2.0)*disc_reg (it's plus the regularizer because the maximization of the discriminator's objective is implemented as a minimization)

def Discriminator_Regularizer(D1_logits, D1_arg, D2_logits, D2_arg):
    D1 = tf.nn.sigmoid(D1_logits)
    D2 = tf.nn.sigmoid(D2_logits)
    grad_D1_logits = tf.gradients(D1_logits, D1_arg)[0]
    grad_D2_logits = tf.gradients(D2_logits, D2_arg)[0]
    grad_D1_logits_norm = tf.norm(tf.reshape(grad_D1_logits, [batch_size,-1]), axis=1, keep_dims=True)
    grad_D2_logits_norm = tf.norm(tf.reshape(grad_D2_logits, [batch_size,-1]), axis=1, keep_dims=True)

    #set keep_dims=True/False such that grad_D_logits_norm.shape == D.shape
    assert grad_D1_logits_norm.shape == D1.shape
    assert grad_D2_logits_norm.shape == D2.shape

    reg_D1 = tf.multiply(tf.square(1.0-D1), tf.square(grad_D1_logits_norm))
    reg_D2 = tf.multiply(tf.square(D2), tf.square(grad_D2_logits_norm))
    disc_regularizer = tf.reduce_mean(reg_D1 + reg_D2)
    return disc_regularizer

Regularized GAN Objective:

disc_loss  = tf.reduce_mean( 
     tf.nn.sigmoid_cross_entropy_with_logits(logits=D1_logits, labels=tf.ones_like(D1_logits))
    +tf.nn.sigmoid_cross_entropy_with_logits(logits=D2_logits, labels=tf.zeros_like(D2_logits)) )

disc_reg   = Discriminator_Regularizer(D1_logits, data, D2_logits, G)
disc_loss += (gamma/2.0)*disc_reg

gen_loss   = tf.reduce_mean(
     tf.nn.sigmoid_cross_entropy_with_logits(logits=D2_logits, labels=tf.ones_like(D2_logits)) )

Notation:

  • D1 = disc_real, D2 = disc_fake
  • gamma is a placeholder that is fed with the annealed value of gamma during training

Prerequisites

  • Python3+
  • TensorFlow, NumPy, SciPy, Matplotlib, tqdm

Usage

carpedm20_DCGAN:

$ python3 main.py --gamma=2.0 --annealing --epochs=10 --dataset=celebA --output_height=32

igul222_GANs:

$ python3 gan_64x64.py --architecture=ResNet --gamma=2.0 --annealing --iters=100000 --dataset=lsun

3DGAN:

$ python3 3DGAN.py --gamma=0.1 --max_steps=100000

Loading data:

For carpedm20_DCGAN the datasets are by default loaded from ./data/dataset.

For igul222_GANs check the load_*.py files in /tflib for the appropriate directory naming ~/data/dataset/train resp ~/data/dataset/eval to load the training and test data from and adapt lines 50-60 in gan_64x64.py.

Datasets

CelebA, LSUN & MNIST datasets can be downloaded with carpedm20's download.py:

$ python download.py celebA lsun mnist

Cifar10 & ImageNet datasets can be downloaded from

$ https://www.cs.toronto.edu/~kriz/cifar.html
$ http://image-net.org/small/download.php