SYMBOL INDEX (50 symbols across 7 files)

FILE: began.py
  function sample_z (line 14) | def sample_z(m, n):
  class BEGAN (line 17) | class BEGAN():
    method __init__ (line 18) | def __init__(self, generator, discriminator, data):
    method train (line 62) | def train(self, sample_dir, training_epoches = 500000, batch_size = 16):

FILE: dcgan.py
  function sample_z (line 14) | def sample_z(m, n):
  class DCGAN (line 17) | class DCGAN():
    method __init__ (line 18) | def __init__(self, generator, discriminator, data):
    method train (line 51) | def train(self, sample_dir, training_epoches = 500000, batch_size = 32):

FILE: ebgan.py
  function sample_z (line 14) | def sample_z(m, n):
  class EBGAN (line 17) | class EBGAN():
    method __init__ (line 18) | def __init__(self, generator, discriminator, data):
    method train (line 59) | def train(self, sample_dir, training_epoches = 500000, batch_size = 32):

FILE: utils/datas.py
  function get_img (line 15) | def get_img(img_path, is_crop=True, crop_h=256, resize_h=64):
  class celebA (line 29) | class celebA():
    method __init__ (line 30) | def __init__(self):
    method __call__ (line 39) | def __call__(self,batch_size):
    method data2fig (line 53) | def data2fig(self, samples):
  class cifar (line 67) | class cifar():
    method __init__ (line 68) | def __init__(self):
    method __call__ (line 77) | def __call__(self,batch_size):
    method data2fig (line 91) | def data2fig(self, samples):
  class mnist (line 106) | class mnist():
    method __init__ (line 107) | def __init__(self):
    method __call__ (line 114) | def __call__(self,batch_size):
    method data2fig (line 125) | def data2fig(self, samples):

FILE: utils/nets.py
  function lrelu (line 5) | def lrelu(x, leak=0.2, name="lrelu"):
  class G_conv (line 11) | class G_conv(object):
    method __init__ (line 12) | def __init__(self, channel=3, name='G_conv'):
    method __call__ (line 17) | def __call__(self, z):
    method vars (line 32) | def vars(self):
  class D_conv (line 36) | class D_conv(object):
    method __init__ (line 37) | def __init__(self, name='D_conv'):
    method __call__ (line 40) | def __call__(self, x, reuse=False):
    method vars (line 60) | def vars(self):
  class D_autoencoder (line 64) | class D_autoencoder(object):
    method __init__ (line 65) | def __init__(self, n_hidden=256, name='D_autoencoder'):
    method __call__ (line 69) | def __call__(self, x, reuse=False):
    method vars (line 101) | def vars(self):
  class D_vae (line 105) | class D_vae(object):
    method __init__ (line 106) | def __init__(self, name='D_vae'):
    method __call__ (line 109) | def __call__(self, x, reuse=False):
    method vars (line 130) | def vars(self):

FILE: vae.py
  function sample_z (line 14) | def sample_z(m, n):
  class VAE (line 17) | class VAE():
    method __init__ (line 18) | def __init__(self, generator, discriminator, data):
    method train (line 57) | def train(self, sample_dir, training_epoches = 500000, batch_size = 32):

FILE: wgan.py
  function sample_z (line 14) | def sample_z(m, n):
  class WGAN (line 17) | class WGAN():
    method __init__ (line 18) | def __init__(self, generator, discriminator, data):
    method train (line 54) | def train(self, sample_dir, training_epoches = 500000, batch_size = 32):