def __loss(self):
print("****** Compute Loss ******")
self.loss_nll = mix_logistic_loss(self.x,
self.mix_logistic_params,
masks=self.masks,
output_mean=False)
self.lam = 0.0
if self.reg_type is None:
self.loss_reg = 0
elif self.reg_type == 'kld':
self.kld = compute_gaussian_kld(self.z_mu, self.z_log_sigma_sq)
self.loss_reg = self.beta * tf.maximum(self.lam, self.kld)
elif self.reg_type == 'mmd':
# self.mmd = estimate_mmd(tf.random_normal(int_shape(self.z)), self.z)
self.mmd = estimate_mmd(
tf.random_normal(tf.stack([256, self.z_dim])), self.z)
self.loss_reg = self.beta * tf.maximum(self.lam, self.mmd)
elif self.reg_type == 'tc':
self.mi, self.tc, self.dwkld = estimate_mi_tc_dwkld(
self.z, self.z_mu, self.z_log_sigma_sq, N=10000)
self.loss_reg = self.mi + self.beta * self.tc + self.dwkld
self.bits_per_dim = tf.reduce_mean(
bits_per_dim_tf(nll=self.loss_nll,
dim=tf.reduce_sum(1 - self.masks, axis=[1, 2]) *
3))
self.loss_nll = tf.reduce_mean(self.loss_nll)
self.loss = self.loss_nll + self.loss_reg
def __loss(self, reg):
print("****** Compute Loss ******")
self.mmd, self.kld, self.mi, self.tc, self.dwkld = [
None for i in range(5)
]
self.gamma, self.dwmmd = 1e3, None ## hard coded, experimental
self.mmdtc = None
self.loss_ae = mix_logistic_loss(self.x,
self.mix_logistic_params,
masks=self.masks)
if reg is None:
self.loss_reg = 0
elif reg == 'kld':
self.kld = compute_gaussian_kld(self.z_mu, self.z_log_sigma_sq)
self.loss_reg = self.beta * tf.maximum(self.lam, self.kld)
elif reg == 'mmd':
# self.mmd = estimate_mmd(tf.random_normal(int_shape(self.z)), self.z)
self.mmd = estimate_mmd(
tf.random_normal(tf.stack([256, self.z_dim])), self.z)
self.loss_reg = self.beta * tf.maximum(self.lam, self.mmd)
elif reg == 'tc':
self.mi, self.tc, self.dwkld = estimate_mi_tc_dwkld(
self.z, self.z_mu, self.z_log_sigma_sq, N=self.N)
self.loss_reg = self.mi + self.beta * self.tc + self.dwkld
elif reg == 'info-tc':
self.mi, self.tc, self.dwkld = estimate_mi_tc_dwkld(
self.z, self.z_mu, self.z_log_sigma_sq, N=self.N)
self.loss_reg = self.beta * self.tc + self.dwkld
elif reg == 'tc-dwmmd':
self.mi, self.tc, self.dwkld = estimate_mi_tc_dwkld(
self.z, self.z_mu, self.z_log_sigma_sq, N=self.N)
self.dwmmd = estimate_mmd(tf.random_normal(int_shape(self.z)),
self.z,
is_dimention_wise=True)
self.loss_reg = self.beta * self.tc + self.dwmmd * self.gamma
elif reg == 'mmd-tc':
self.mmd = estimate_mmd(tf.random_normal(int_shape(self.z)),
self.z)
self.mmdtc = estimate_mmdtc(self.z, self.random_indices)
self.loss_reg = (self.mmd + self.beta * self.mmdtc) * 1e5
self.mi = estimate_mi(self.z, self.z_mu, self.z_log_sigma_sq, N=200000)
print("reg:{0}, beta:{1}, lam:{2}".format(self.reg, self.beta,
self.lam))
self.loss = self.loss_ae + self.loss_reg
def __loss(self, reg):
print("****** Compute Loss ******")
self.mmd, self.kld = [None, None]
self.loss_ae = mix_logistic_loss(self.x,
self.mix_logistic_params,
masks=self.masks)
if reg is None:
self.loss_reg = 0
elif reg == 'kld':
self.kld = compute_gaussian_kld(self.z_mu, self.z_log_sigma_sq)
self.loss_reg = self.beta * tf.maximum(self.lam, self.kld)
elif reg == 'mmd':
# self.mmd = estimate_mmd(tf.random_normal(int_shape(self.z)), self.z)
self.mmd = estimate_mmd(
tf.random_normal(tf.stack([256, self.z_dim])), self.z)
self.loss_reg = self.beta * tf.maximum(self.lam, self.mmd)
print("reg:{0}, beta:{1}, lam:{2}".format(self.reg, self.beta,
self.lam))
self.loss = self.loss_ae + self.loss_reg
def __loss(self, reg):
print("****** Compute Loss ******")
self.mmd, self.kld, self.mi, self.tc, self.dwkld = [
None for i in range(5)
]
self.loss_ae = gaussian_recons_loss(self.x, self.x_hat)
if reg is None:
self.loss_reg = 0
elif reg == 'kld':
self.kld = compute_gaussian_kld(self.z_mu, self.z_log_sigma_sq)
self.loss_reg = self.beta * tf.maximum(self.lam, self.kld)
elif reg == 'mmd':
self.mmd = estimate_mmd(tf.random_normal(int_shape(self.z)),
self.z)
self.loss_reg = self.beta * tf.maximum(self.lam, self.mmd)
elif reg == 'tc':
self.mi, self.tc, self.dwkld = estimate_mi_tc_dwkld(
self.z, self.z_mu, self.z_log_sigma_sq, N=self.N)
self.loss_reg = self.mi + self.beta * self.tc + self.dwkld
print("reg:{0}, beta:{1}, lam:{2}".format(self.reg, self.beta,
self.lam))
self.loss = self.loss_ae + self.loss_reg