SYMBOL INDEX (216 symbols across 50 files)

FILE: GAN/ali_bigan/ali_bigan_pytorch.py
  function log (line 25) | def log(x):
  function D (line 52) | def D(X, z):
  function reset_grad (line 56) | def reset_grad():

FILE: GAN/ali_bigan/ali_bigan_tensorflow.py
  function plot (line 19) | def plot(samples):
  function xavier_init (line 35) | def xavier_init(size):
  function log (line 41) | def log(x):
  function sample_z (line 67) | def sample_z(m, n):
  function Q (line 71) | def Q(X):
  function P (line 77) | def P(z):
  function D (line 83) | def D(X, z):

FILE: GAN/auxiliary_classifier_gan/ac_gan_pytorch.py
  function G (line 32) | def G(z, c):
  function D (line 52) | def D(X):
  function reset_grad (line 64) | def reset_grad():

FILE: GAN/auxiliary_classifier_gan/ac_gan_tensorflow.py
  function plot (line 21) | def plot(samples):
  function xavier_init (line 37) | def xavier_init(size):
  function generator (line 53) | def generator(z, c):
  function discriminator (line 69) | def discriminator(X):
  function sample_z (line 80) | def sample_z(m, n):
  function cross_entropy (line 84) | def cross_entropy(logit, y):

FILE: GAN/boundary_equilibrium_gan/began_pytorch.py
  function D (line 44) | def D(X):
  function reset_grad (line 50) | def reset_grad():

FILE: GAN/boundary_equilibrium_gan/began_tensorflow.py
  function plot (line 22) | def plot(samples):
  function xavier_init (line 38) | def xavier_init(size):
  function sample_z (line 62) | def sample_z(m, n):
  function G (line 66) | def G(z):
  function D (line 73) | def D(X):

FILE: GAN/boundary_seeking_gan/bgan_pytorch.py
  function log (line 24) | def log(x):
  function reset_grad (line 44) | def reset_grad():

FILE: GAN/boundary_seeking_gan/bgan_tensorflow.py
  function plot (line 19) | def plot(samples):
  function xavier_init (line 35) | def xavier_init(size):
  function log (line 41) | def log(x):
  function sample_z (line 62) | def sample_z(m, n):
  function generator (line 66) | def generator(z):
  function discriminator (line 73) | def discriminator(x):

FILE: GAN/conditional_gan/cgan_pytorch.py
  function xavier_init (line 23) | def xavier_init(size):
  function G (line 38) | def G(z, c):
  function D (line 54) | def D(X, c):
  function reset_grad (line 69) | def reset_grad():

FILE: GAN/conditional_gan/cgan_tensorflow.py
  function xavier_init (line 17) | def xavier_init(size):
  function discriminator (line 36) | def discriminator(x, y):
  function generator (line 57) | def generator(z, y):
  function sample_Z (line 66) | def sample_Z(m, n):
  function plot (line 70) | def plot(samples):

FILE: GAN/coupled_gan/cogan_pytorch.py
  function G1 (line 45) | def G1(z):
  function G2 (line 51) | def G2(z):
  function D1 (line 76) | def D1(X):
  function D2 (line 82) | def D2(X):
  function reset_grad (line 95) | def reset_grad():
  function sample_x (line 117) | def sample_x(X, size):

FILE: GAN/coupled_gan/cogan_tensorflow.py
  function plot (line 22) | def plot(samples):
  function xavier_init (line 38) | def xavier_init(size):
  function G (line 58) | def G(z):
  function D (line 75) | def D(X1, X2):
  function sample_X (line 144) | def sample_X(X, size):
  function sample_z (line 149) | def sample_z(m, n):

FILE: GAN/disco_gan/discogan_pytorch.py
  function log (line 25) | def log(x):
  function plot (line 29) | def plot(samples):
  function reset_grad (line 78) | def reset_grad():
  function sample_x (line 102) | def sample_x(X, size):

FILE: GAN/disco_gan/discogan_tensorflow.py
  function plot (line 20) | def plot(samples):
  function xavier_init (line 36) | def xavier_init(size):
  function log (line 42) | def log(x):
  function D_A (line 75) | def D_A(X):
  function D_B (line 80) | def D_B(X):
  function G_AB (line 85) | def G_AB(X):
  function G_BA (line 90) | def G_BA(X):
  function sample_X (line 150) | def sample_X(X, size):

FILE: GAN/dual_gan/dualgan_pytorch.py
  function log (line 28) | def log(x):
  function reset_grad (line 59) | def reset_grad():
  function sample_x (line 84) | def sample_x(X, size):

FILE: GAN/dual_gan/dualgan_tensorflow.py
  function plot (line 23) | def plot(samples):
  function xavier_init (line 39) | def xavier_init(size):
  function G1 (line 60) | def G1(X1, z):
  function G2 (line 66) | def G2(X2, z):
  function D1 (line 83) | def D1(X):
  function D2 (line 88) | def D2(X):
  function sample_X (line 149) | def sample_X(X, size):
  function sample_z (line 154) | def sample_z(m, n):

FILE: GAN/ebgan/ebgan_pytorch.py
  function D (line 42) | def D(X):
  function reset_grad (line 47) | def reset_grad():

FILE: GAN/ebgan/ebgan_tensorflow.py
  function plot (line 19) | def plot(samples):
  function xavier_init (line 35) | def xavier_init(size):
  function sample_z (line 58) | def sample_z(m, n):
  function generator (line 62) | def generator(z):
  function discriminator (line 69) | def discriminator(X):

FILE: GAN/f_gan/f_gan_pytorch.py
  function log (line 24) | def log(x):
  function reset_grad (line 43) | def reset_grad():

FILE: GAN/f_gan/f_gan_tensorflow.py
  function plot (line 19) | def plot(samples):
  function xavier_init (line 35) | def xavier_init(size):
  function sample_z (line 58) | def sample_z(m, n):
  function generator (line 62) | def generator(z):
  function discriminator (line 69) | def discriminator(x):

FILE: GAN/generative_adversarial_parallelization/gap_pytorch.py
  function log (line 26) | def log(x):
  function reset_grad (line 63) | def reset_grad():

FILE: GAN/gibbsnet/gibbsnet_pytorch.py
  function log (line 26) | def log(x):
  function D (line 53) | def D(X, z):
  function reset_grad (line 57) | def reset_grad():

FILE: GAN/improved_wasserstein_gan/wgan_gp_tensorflow.py
  function plot (line 20) | def plot(samples):
  function xavier_init (line 36) | def xavier_init(size):
  function sample_z (line 64) | def sample_z(m, n):
  function G (line 68) | def G(z):
  function D (line 75) | def D(X):

FILE: GAN/infogan/infogan_pytorch.py
  function xavier_init (line 23) | def xavier_init(size):
  function G (line 38) | def G(z, c):
  function D (line 54) | def D(X):
  function Q (line 69) | def Q(X):
  function reset_grad (line 84) | def reset_grad():
  function sample_c (line 96) | def sample_c(size):

FILE: GAN/infogan/infogan_tensorflow.py
  function xavier_init (line 9) | def xavier_init(size):
  function sample_Z (line 47) | def sample_Z(m, n):
  function sample_c (line 51) | def sample_c(m):
  function generator (line 55) | def generator(z, c):
  function discriminator (line 64) | def discriminator(x):
  function Q (line 72) | def Q(x):
  function plot (line 79) | def plot(samples):

FILE: GAN/least_squares_gan/lsgan_pytorch.py
  function reset_grad (line 40) | def reset_grad():

FILE: GAN/least_squares_gan/lsgan_tensorflow.py
  function plot (line 19) | def plot(samples):
  function xavier_init (line 35) | def xavier_init(size):
  function sample_z (line 58) | def sample_z(m, n):
  function generator (line 62) | def generator(z):
  function discriminator (line 69) | def discriminator(x):

FILE: GAN/magan/magan_pytorch.py
  function D (line 45) | def D(X):
  function reset_grad (line 50) | def reset_grad():

FILE: GAN/magan/magan_tensorflow.py
  function plot (line 21) | def plot(samples):
  function xavier_init (line 37) | def xavier_init(size):
  function sample_z (line 61) | def sample_z(m, n):
  function G (line 65) | def G(z):
  function D (line 72) | def D(X):

FILE: GAN/mode_regularized_gan/mode_reg_gan_pytorch.py
  function log (line 26) | def log(x):
  function reset_grad (line 51) | def reset_grad():
  function sample_X (line 57) | def sample_X(size, include_y=False):

FILE: GAN/mode_regularized_gan/mode_reg_gan_tensorflow.py
  function plot (line 19) | def plot(samples):
  function xavier_init (line 35) | def xavier_init(size):
  function log (line 41) | def log(x):
  function sample_z (line 69) | def sample_z(m, n):
  function encoder (line 73) | def encoder(x):
  function generator (line 79) | def generator(z):
  function discriminator (line 86) | def discriminator(x):

FILE: GAN/softmax_gan/softmax_gan_pytorch.py
  function log (line 24) | def log(x):
  function reset_grad (line 43) | def reset_grad():

FILE: GAN/softmax_gan/softmax_gan_tensorflow.py
  function plot (line 19) | def plot(samples):
  function xavier_init (line 35) | def xavier_init(size):
  function log (line 41) | def log(x):
  function sample_z (line 62) | def sample_z(m, n):
  function G (line 66) | def G(z):
  function D (line 73) | def D(X):

FILE: GAN/vanilla_gan/gan_pytorch.py
  function xavier_init (line 23) | def xavier_init(size):
  function G (line 38) | def G(z):
  function D (line 53) | def D(X):
  function reset_grad (line 67) | def reset_grad():

FILE: GAN/vanilla_gan/gan_tensorflow.py
  function xavier_init (line 9) | def xavier_init(size):
  function sample_Z (line 37) | def sample_Z(m, n):
  function generator (line 41) | def generator(z):
  function discriminator (line 49) | def discriminator(x):
  function plot (line 57) | def plot(samples):

FILE: GAN/wasserstein_gan/wgan_pytorch.py
  function reset_grad (line 39) | def reset_grad():

FILE: GAN/wasserstein_gan/wgan_tensorflow.py
  function plot (line 17) | def plot(samples):
  function xavier_init (line 33) | def xavier_init(size):
  function sample_z (line 61) | def sample_z(m, n):
  function generator (line 65) | def generator(z):
  function discriminator (line 72) | def discriminator(x):

FILE: HelmholtzMachine/vanilla_HM/helmholtz.py
  function sigm (line 30) | def sigm(x):
  function infer (line 34) | def infer(X):
  function generate (line 39) | def generate(H):
  function plot (line 90) | def plot(samples, size, name):

FILE: RBM/rbm_binary_cd.py
  function sigm (line 23) | def sigm(x):
  function infer (line 27) | def infer(X):
  function generate (line 32) | def generate(H):
  function plot (line 91) | def plot(samples, size, name):

FILE: RBM/rbm_binary_pcd.py
  function sigm (line 21) | def sigm(x):
  function infer (line 25) | def infer(X):
  function generate (line 30) | def generate(H):
  function plot (line 77) | def plot(samples, size, name):

FILE: VAE/adversarial_autoencoder/aae_pytorch.py
  function reset_grad (line 48) | def reset_grad():
  function sample_X (line 54) | def sample_X(size, include_y=False):

FILE: VAE/adversarial_autoencoder/aae_tensorflow.py
  function plot (line 19) | def plot(samples):
  function xavier_init (line 35) | def xavier_init(size):
  function Q (line 54) | def Q(X):
  function P (line 70) | def P(z):
  function D (line 87) | def D(z):

FILE: VAE/adversarial_vb/avb_pytorch.py
  function log (line 25) | def log(x):
  function reset_grad (line 52) | def reset_grad():
  function sample_X (line 58) | def sample_X(size, include_y=False):

FILE: VAE/adversarial_vb/avb_tensorflow.py
  function log (line 21) | def log(x):
  function plot (line 25) | def plot(samples):
  function xavier_init (line 41) | def xavier_init(size):
  function Q (line 60) | def Q(X, eps):
  function P (line 76) | def P(z):
  function D (line 92) | def D(X, z):

FILE: VAE/conditional_vae/cvae_pytorch.py
  function xavier_init (line 23) | def xavier_init(size):
  function Q (line 41) | def Q(X, c):
  function sample_z (line 49) | def sample_z(mu, log_var):
  function P (line 63) | def P(z, c):

FILE: VAE/conditional_vae/cvae_tensorflow.py
  function plot (line 20) | def plot(samples):
  function xavier_init (line 36) | def xavier_init(size):
  function Q (line 58) | def Q(X, c):
  function sample_z (line 66) | def sample_z(mu, log_var):
  function P (line 80) | def P(z, c):

FILE: VAE/denoising_vae/dvae_pytorch.py
  function xavier_init (line 24) | def xavier_init(size):
  function Q (line 41) | def Q(X):
  function sample_z (line 48) | def sample_z(mu, log_var):
  function P (line 61) | def P(z):

FILE: VAE/denoising_vae/dvae_tensorflow.py
  function plot (line 21) | def plot(samples):
  function xavier_init (line 37) | def xavier_init(size):
  function Q (line 57) | def Q(X):
  function sample_z (line 64) | def sample_z(mu, log_var):
  function P (line 77) | def P(z):

FILE: VAE/vanilla_vae/vae_pytorch.py
  function xavier_init (line 23) | def xavier_init(size):
  function Q (line 41) | def Q(X):
  function sample_z (line 48) | def sample_z(mu, log_var):
  function P (line 62) | def P(z):

FILE: VAE/vanilla_vae/vae_tensorflow.py
  function plot (line 19) | def plot(samples):
  function xavier_init (line 35) | def xavier_init(size):
  function Q (line 56) | def Q(X):
  function sample_z (line 63) | def sample_z(mu, log_var):
  function P (line 77) | def P(z):