def __init__(self, options):
# line 91 in train.py calls model.GAN(model_options).
# These model_options are the assigned to self.options in this class.
self.options = options
# Creating batch normalization layers (from line 41-44 for generator and 46-49 for discriminator):
# "batch normalization reduces the internal covariance shift"
# It makes the learning of layers in the network more independent of each other.
# The objective of batch norm layer is to make input to the activation layer, unit Gaussian.
# So that neuron does not get saturate in case of sigmoid and tanh.
# It helps in the following:
# Fast convergence of network.
# Allows you to be care free about weight initialization.
# Works as regularization.
# Batch norm layers for generator
self.g_bn0 = ops.batch_norm(name='g_bn0')
self.g_bn1 = ops.batch_norm(name='g_bn1')
self.g_bn2 = ops.batch_norm(name='g_bn2')
self.g_bn3 = ops.batch_norm(name='g_bn3')
# Batch norm layer for descriminator
self.d_bn1 = ops.batch_norm(name='d_bn1')
self.d_bn2 = ops.batch_norm(name='d_bn2')
self.d_bn3 = ops.batch_norm(name='d_bn3')
self.d_bn4 = ops.batch_norm(name='d_bn4')
def __init__(self, options):
self.options = options
self.g_bn0 = ops.batch_norm(name='g_bn0')
self.g_bn1 = ops.batch_norm(name='g_bn1')
self.g_bn2 = ops.batch_norm(name='g_bn2')
self.g_bn3 = ops.batch_norm(name='g_bn3')
self.g_bn4 = ops.batch_norm(name='g_bn4')
self.d_bn1 = ops.batch_norm(name='d_bn1')
self.d_bn2 = ops.batch_norm(name='d_bn2')
self.d_bn3 = ops.batch_norm(name='d_bn3')
self.d_bn4 = ops.batch_norm(name='d_bn4')
def __init__(self, options): self.options = options self.g_bn0 = ops.batch_norm(name='g_bn0') self.g_bn1 = ops.batch_norm(name='g_bn1') self.g_bn2 = ops.batch_norm(name='g_bn2') self.g_bn3 = ops.batch_norm(name='g_bn3') self.d_bn1 = ops.batch_norm(name='d_bn1') self.d_bn2 = ops.batch_norm(name='d_bn2') self.d_bn3 = ops.batch_norm(name='d_bn3') self.d_bn4 = ops.batch_norm(name='d_bn4')
def __init__(self, options):
self.options = options
# Used for sound embedding : No need Now!!
self.g_s_bn0 = ops.batch_norm(name='g_s_bn0')
self.g_s_bn1 = ops.batch_norm(name='g_s_bn1')
self.g_s_bn2 = ops.batch_norm(name='g_s_bn2')
self.d_s_bn0 = ops.batch_norm(name='d_s_bn0')
self.d_s_bn1 = ops.batch_norm(name='d_s_bn1')
self.d_s_bn2 = ops.batch_norm(name='d_s_bn2')
# Used for noise vector
self.g_bn0 = ops.batch_norm(name='g_bn0')
self.g_bn1 = ops.batch_norm(name='g_bn1')
self.g_bn2 = ops.batch_norm(name='g_bn2')
self.g_bn3 = ops.batch_norm(name='g_bn3')
self.d_bn1 = ops.batch_norm(name='d_bn1')
self.d_bn2 = ops.batch_norm(name='d_bn2')
self.d_bn3 = ops.batch_norm(name='d_bn3')
self.d_bn4 = ops.batch_norm(name='d_bn4')
self.d_bn5 = ops.batch_norm(name='d_bn5')
self.down_bn1 = ops.batch_norm(name='down_bn1')
#The batch normalization layers of the residual blocks
self.r_bn0 = ops.batch_norm(name='res_bn0')
self.r_bn1 = ops.batch_norm(name='res_bn1')
self.r_bn3 = ops.batch_norm(name='res_bn3')
self.r_bn4 = ops.batch_norm(name='res_bn4')
self.r_bn6 = ops.batch_norm(name='res_bn6')
self.r_bn7 = ops.batch_norm(name='res_bn7')
self.r_bn9 = ops.batch_norm(name='res_bn9')
self.r_bn10 = ops.batch_norm(name='res_bn10')
self.g2_bn0 = ops.batch_norm(name='g2_bn0')
self.g2_bn1 = ops.batch_norm(name='g2_bn1')
self.g2_bn2 = ops.batch_norm(name='g2_bn2')
self.d2_bn1 = ops.batch_norm(name='d2_bn1')
self.d2_bn2 = ops.batch_norm(name='d2_bn2')
self.d2_bn3 = ops.batch_norm(name='d2_bn3')
self.d2_bn4 = ops.batch_norm(name='d2_bn4')
