def _anomaly_log_prob(self, targets, prediction_ops): prediction = prediction_ops["mean"] if self._anomaly_distribution == AnomalyMixtureARModel.GAUSSIAN_ANOMALY: anomaly_variance = prediction_ops["anomaly_params"] anomaly_sigma = math_ops.sqrt( gen_math_ops.maximum(anomaly_variance, 1e-5)) log_prob = math_utils.normal_log_prob(targets, anomaly_sigma, prediction) else: assert self._anomaly_distribution == AnomalyMixtureARModel.CAUCHY_ANOMALY anomaly_scale = prediction_ops["anomaly_params"] log_prob = math_utils.cauchy_log_prob(targets, anomaly_scale, prediction) return log_prob
def _anomaly_log_prob(self, targets, prediction_ops): prediction = prediction_ops["mean"] if self._anomaly_distribution == AnomalyMixtureARModel.GAUSSIAN_ANOMALY: anomaly_variance = prediction_ops["anomaly_params"] anomaly_sigma = math_ops.sqrt( gen_math_ops.maximum(anomaly_variance, 1e-5)) log_prob = math_utils.normal_log_prob(targets, anomaly_sigma, prediction) else: assert self._anomaly_distribution == AnomalyMixtureARModel.CAUCHY_ANOMALY anomaly_scale = prediction_ops["anomaly_params"] log_prob = math_utils.cauchy_log_prob(targets, anomaly_scale, prediction) return log_prob
def loss_op(self, targets, prediction_ops): """Create loss_op.""" prediction = prediction_ops["mean"] if self.loss == ARModel.NORMAL_LIKELIHOOD_LOSS: covariance = prediction_ops["covariance"] sigma = math_ops.sqrt(gen_math_ops.maximum(covariance, 1e-5)) loss_op = -math_ops.reduce_sum( math_utils.normal_log_prob(targets, sigma, prediction)) else: assert self.loss == ARModel.SQUARED_LOSS, self.loss loss_op = math_ops.reduce_sum(math_ops.square(prediction - targets)) loss_op /= math_ops.cast( math_ops.reduce_prod(array_ops.shape(targets)), loss_op.dtype) return loss_op
def loss_op(self, targets, prediction_ops): """Create loss_op.""" prediction = prediction_ops["mean"] if self.loss == ARModel.NORMAL_LIKELIHOOD_LOSS: covariance = prediction_ops["covariance"] sigma = math_ops.sqrt(gen_math_ops.maximum(covariance, 1e-5)) loss_op = -math_ops.reduce_sum( math_utils.normal_log_prob(targets, sigma, prediction)) else: assert self.loss == ARModel.SQUARED_LOSS, self.loss loss_op = math_ops.reduce_sum( math_ops.squared_difference(prediction, targets)) loss_op /= math_ops.cast( math_ops.reduce_prod(array_ops.shape(targets)), loss_op.dtype) return loss_op
def loss_op(self, targets, prediction_ops): """Create loss_op.""" prediction = prediction_ops["mean"] covariance = prediction_ops["covariance"] # Normal data log probability. sigma = math_ops.sqrt(gen_math_ops.maximum(covariance, 1e-5)) log_prob1 = math_utils.normal_log_prob(targets, sigma, prediction) log_prob1 += math_ops.log(1 - self._anomaly_prior_probability) # Anomaly log probability. log_prob2 = self._anomaly_log_prob(targets, prediction_ops) log_prob2 += math_ops.log(self._anomaly_prior_probability) # We need to compute log(exp(log_prob1) + exp(log_prob2). For numerical # stability, we rewrite the expression as below. p1 = gen_math_ops.minimum(log_prob1, log_prob2) p2 = gen_math_ops.maximum(log_prob1, log_prob2) mixed_log_prob = p2 + math_ops.log(1 + gen_math_ops.exp(p1 - p2)) loss_op = -math_ops.reduce_sum(mixed_log_prob) loss_op /= math_ops.cast( math_ops.reduce_prod(array_ops.shape(targets)), self.dtype) return loss_op
def loss_op(self, targets, prediction_ops): """Create loss_op.""" prediction = prediction_ops["mean"] covariance = prediction_ops["covariance"] # Normal data log probability. sigma = math_ops.sqrt(gen_math_ops.maximum(covariance, 1e-5)) log_prob1 = math_utils.normal_log_prob(targets, sigma, prediction) log_prob1 += math_ops.log(1 - self._anomaly_prior_probability) # Anomaly log probability. log_prob2 = self._anomaly_log_prob(targets, prediction_ops) log_prob2 += math_ops.log(self._anomaly_prior_probability) # We need to compute log(exp(log_prob1) + exp(log_prob2). For numerical # stability, we rewrite the expression as below. p1 = gen_math_ops.minimum(log_prob1, log_prob2) p2 = gen_math_ops.maximum(log_prob1, log_prob2) mixed_log_prob = p2 + math_ops.log(1 + gen_math_ops.exp(p1 - p2)) loss_op = -math_ops.reduce_sum(mixed_log_prob) loss_op /= math_ops.cast( math_ops.reduce_prod(array_ops.shape(targets)), self.dtype) return loss_op