tensorflow_privacy/privacy/estimators/multi_class_head.py [41:142]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - name=name) def loss(self, labels, logits, features=None, mode=None, regularization_losses=None): """Returns regularized training loss. See `base_head.Head` for details.""" del mode # Unused for this head. with tf.compat.v1.name_scope( 'losses', values=(logits, labels, regularization_losses, features)): logits = base_head.check_logits_final_dim(logits, self.logits_dimension) labels = self._processed_labels(logits, labels) unweighted_loss, weights = self._unweighted_loss_and_weights( logits, labels, features) vector_training_loss = losses_utils.compute_weighted_loss( unweighted_loss, sample_weight=weights, reduction=tf.keras.losses.Reduction.NONE) regularization_loss = tf.math.add_n( regularization_losses) if regularization_losses is not None else None vector_regularized_training_loss = ( tf.add(vector_training_loss, regularization_loss) if regularization_loss is not None else vector_training_loss) return vector_regularized_training_loss def _create_tpu_estimator_spec(self, features, mode, logits, labels=None, optimizer=None, trainable_variables=None, train_op_fn=None, update_ops=None, regularization_losses=None): """See superclass for description.""" with tf.compat.v1.name_scope(self._name, 'head'): # Predict. pred_keys = prediction_keys.PredictionKeys predictions = self.predictions(logits) if mode == ModeKeys.PREDICT: probabilities = predictions[pred_keys.PROBABILITIES] classifier_output = base_head.classification_output( scores=probabilities, n_classes=self._n_classes, label_vocabulary=self._label_vocabulary) return model_fn._TPUEstimatorSpec( # pylint: disable=protected-access mode=ModeKeys.PREDICT, predictions=predictions, export_outputs={ base_head.DEFAULT_SERVING_KEY: classifier_output, base_head.CLASSIFY_SERVING_KEY: classifier_output, base_head.PREDICT_SERVING_KEY: export_output.PredictOutput(predictions) }) regularized_training_loss = self.loss( logits=logits, labels=labels, features=features, mode=mode, regularization_losses=regularization_losses) scalar_loss = tf.reduce_mean(regularized_training_loss) # Eval. if mode == ModeKeys.EVAL: eval_metrics = self.metrics(regularization_losses=regularization_losses) return model_fn._TPUEstimatorSpec( # pylint: disable=protected-access mode=ModeKeys.EVAL, predictions=predictions, loss=scalar_loss, eval_metrics=base_head.create_eval_metrics_tuple( self.update_metrics, { 'eval_metrics': eval_metrics, 'features': features, 'logits': logits, 'labels': labels, 'regularization_losses': regularization_losses })) # Train. train_op = base_head.create_estimator_spec_train_op( head_name=self._name, optimizer=optimizer, train_op_fn=train_op_fn, update_ops=update_ops, trainable_variables=trainable_variables, regularized_training_loss=regularized_training_loss, loss_reduction=self._loss_reduction) # Create summary. base_head.create_estimator_spec_summary( regularized_training_loss=scalar_loss, regularization_losses=regularization_losses, summary_key_fn=self._summary_key) return model_fn._TPUEstimatorSpec( # pylint: disable=protected-access mode=ModeKeys.TRAIN, predictions=predictions, loss=scalar_loss, train_op=train_op) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - tensorflow_privacy/privacy/estimators/multi_label_head.py [47:148]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - name=name) def loss(self, labels, logits, features=None, mode=None, regularization_losses=None): """Returns regularized training loss. See `base_head.Head` for details.""" del mode # Unused for this head. with tf.compat.v1.name_scope( 'losses', values=(logits, labels, regularization_losses, features)): logits = base_head.check_logits_final_dim(logits, self.logits_dimension) labels = self._processed_labels(logits, labels) unweighted_loss, weights = self._unweighted_loss_and_weights( logits, labels, features) vector_training_loss = losses_utils.compute_weighted_loss( unweighted_loss, sample_weight=weights, reduction=tf.keras.losses.Reduction.NONE) regularization_loss = tf.math.add_n( regularization_losses) if regularization_losses is not None else None vector_regularized_training_loss = ( tf.add(vector_training_loss, regularization_loss) if regularization_loss is not None else vector_training_loss) return vector_regularized_training_loss def _create_tpu_estimator_spec(self, features, mode, logits, labels=None, optimizer=None, trainable_variables=None, train_op_fn=None, update_ops=None, regularization_losses=None): """See superclass for description.""" with tf.compat.v1.name_scope(self._name, 'head'): # Predict. pred_keys = prediction_keys.PredictionKeys predictions = self.predictions(logits) if mode == ModeKeys.PREDICT: probabilities = predictions[pred_keys.PROBABILITIES] classifier_output = base_head.classification_output( scores=probabilities, n_classes=self._n_classes, label_vocabulary=self._label_vocabulary) return model_fn._TPUEstimatorSpec( # pylint: disable=protected-access mode=ModeKeys.PREDICT, predictions=predictions, export_outputs={ base_head.DEFAULT_SERVING_KEY: classifier_output, base_head.CLASSIFY_SERVING_KEY: classifier_output, base_head.PREDICT_SERVING_KEY: export_output.PredictOutput(predictions) }) regularized_training_loss = self.loss( logits=logits, labels=labels, features=features, mode=mode, regularization_losses=regularization_losses) scalar_loss = tf.reduce_mean(regularized_training_loss) # Eval. if mode == ModeKeys.EVAL: eval_metrics = self.metrics(regularization_losses=regularization_losses) return model_fn._TPUEstimatorSpec( # pylint: disable=protected-access mode=ModeKeys.EVAL, predictions=predictions, loss=scalar_loss, eval_metrics=base_head.create_eval_metrics_tuple( self.update_metrics, { 'eval_metrics': eval_metrics, 'features': features, 'logits': logits, 'labels': labels, 'regularization_losses': regularization_losses })) # Train. train_op = base_head.create_estimator_spec_train_op( head_name=self._name, optimizer=optimizer, train_op_fn=train_op_fn, update_ops=update_ops, trainable_variables=trainable_variables, regularized_training_loss=regularized_training_loss, loss_reduction=self._loss_reduction) # Create summary. base_head.create_estimator_spec_summary( regularized_training_loss=scalar_loss, regularization_losses=regularization_losses, summary_key_fn=self._summary_key) return model_fn._TPUEstimatorSpec( # pylint: disable=protected-access mode=ModeKeys.TRAIN, predictions=predictions, loss=scalar_loss, train_op=train_op) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -