def loss(self, predictions, targets):
"""Compute the necessary loss for training.
Args:
Returns:
"""
self.reproduction_loss = l1_loss(
predictions, targets) # l2_loss(predictions, targets)
# self.prior_loss = vae_loss(self.z_mean, self.z_logvar, prior_weight = 0.0001)
# return [self.reproduction_loss, self.prior_loss]
return self.reproduction_loss
def loss(self, predictions, flow_motion, flow_mask, targets, lambda_motion,
lambda_mask):
"""Compute the necessary loss for training.
Args:
Returns:
"""
# corrected regularized loss
self.reproduction_loss = l1_loss(predictions, targets) \
# + lambda_motion * l1_regularizer(flow_motion) \
# + lambda_mask * l1_regularizer(flow_mask)
return self.reproduction_loss
def l1loss(self, predictions, targets):
self.reproduction_loss = l1_loss(predictions, targets)
return self.reproduction_loss