def _get_loss(features, labels, logits, params):
ctcvr_label = tf.to_float(labels['convert_label'])
ctr_label = tf.to_float(labels['click_label'])
ctr_logits, ctcvr_logits = logits['ctr_logits'], logits['ctcvr_logits']
# unweighted loss
unweighted_ctcvr_loss = tf.nn.sigmoid_cross_entropy_with_logits(
labels=ctcvr_label, logits=ctcvr_logits, name='ctcvr_loss')
unweighted_ctr_loss = tf.nn.sigmoid_cross_entropy_with_logits(
labels=ctr_label, logits=ctr_logits, name='ctr_loss')
unweighted_loss = tf.add(unweighted_ctr_loss,
unweighted_ctcvr_loss,
name='total_loss')
# weighted loss
ctr_weight_column = params.ctr_weight_column
ctcvr_weight_column = params.ctcvr_weight_column
ctr_weights = _get_weights_and_check_match_logits(
features=features, weight_column=ctr_weight_column, logits=ctr_logits)
ctr_weighted_loss = tf.losses.compute_weighted_loss(
unweighted_ctr_loss,
weights=ctr_weights,
reduction=tf.losses.Reduction.MEAN)
ctcvr_weights = _get_weights_and_check_match_logits(
features=features,
weight_column=ctcvr_weight_column,
logits=ctcvr_logits)
ctcvr_weighted_loss = tf.losses.compute_weighted_loss(
unweighted_ctcvr_loss,
weights=ctcvr_weights,
reduction=tf.losses.Reduction.MEAN)
weighted_loss = tf.add(ctr_weighted_loss, ctcvr_weighted_loss)
labels = {'convert_label': ctcvr_label, 'click_label': ctr_label}
weights = {'convert_weight': ctcvr_weights, 'click_weight': ctr_weights}
return weighted_loss, unweighted_loss, weights, labels
def create_loss(self, features, mode, logits, labels):
"""See `Head`."""
del mode # Unused for this head.
logits = ops.convert_to_tensor(logits)
processed_labels = self._process_labels(labels)
processed_labels = head_lib._check_dense_labels_match_logits_and_reshape( # pylint:disable=protected-access
labels=processed_labels, logits=logits,
expected_labels_dimension=self.logits_dimension)
if self._loss_fn:
unweighted_loss = head_lib._call_loss_fn( # pylint:disable=protected-access
loss_fn=self._loss_fn, labels=processed_labels, logits=logits,
features=features, expected_loss_dim=1)
else:
unweighted_loss = losses.sigmoid_cross_entropy(
multi_class_labels=processed_labels, logits=logits,
reduction=losses.Reduction.NONE)
# Averages loss over classes.
unweighted_loss = math_ops.reduce_mean(
unweighted_loss, axis=-1, keepdims=True)
weights = head_lib._get_weights_and_check_match_logits( # pylint:disable=protected-access,
features=features, weight_column=self._weight_column, logits=logits)
training_loss = losses.compute_weighted_loss(
unweighted_loss, weights=weights, reduction=self._loss_reduction)
return head_lib.LossSpec(
training_loss=training_loss,
unreduced_loss=unweighted_loss,
weights=weights,
processed_labels=processed_labels)
def create_loss(self, features, mode, logits, labels):
"""See `Head`."""
del mode # Unused for this head.
logits = ops.convert_to_tensor(logits)
processed_labels = self._process_labels(labels)
processed_labels = head_lib._check_dense_labels_match_logits_and_reshape( # pylint:disable=protected-access
labels=processed_labels,
logits=logits,
expected_labels_dimension=self.logits_dimension)
if self._loss_fn:
unweighted_loss = head_lib._call_loss_fn( # pylint:disable=protected-access
loss_fn=self._loss_fn,
labels=processed_labels,
logits=logits,
features=features,
expected_loss_dim=1)
else:
unweighted_loss = losses.sigmoid_cross_entropy(
multi_class_labels=processed_labels,
logits=logits,
reduction=losses.Reduction.NONE)
# Averages loss over classes.
unweighted_loss = math_ops.reduce_mean(unweighted_loss,
axis=-1,
keep_dims=True)
weights = head_lib._get_weights_and_check_match_logits( # pylint:disable=protected-access,
features=features,
weight_column=self._weight_column,
logits=logits)
training_loss = losses.compute_weighted_loss(
unweighted_loss, weights=weights, reduction=self._loss_reduction)
return head_lib.LossSpec(training_loss=training_loss,
unreduced_loss=unweighted_loss,
weights=weights,
processed_labels=processed_labels)
def _create_loss(features, params, logits, labels):
# follows the logic of tf.losses.sigmoid_cross_entropy
labels = tf.to_float(labels)
# unweighted loss
unweighted_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels,
logits=logits)
# calc weighted loss (assemble cost-sensitive learning)
weight_column = params.weight_column
if weight_column:
weights = _get_weights_and_check_match_logits(
features=features, weight_column=weight_column,
logits=logits) # get weight col tensor
else:
weights = 1.0
weighted_loss = tf.losses.compute_weighted_loss(
unweighted_loss, weights=weights, reduction=tf.losses.Reduction.MEAN)
return weighted_loss, unweighted_loss, weights, labels
def create_estimator_spec(self,
features,
mode,
logits,
labels=None,
train_op_fn=None):
"""See `Head`."""
with ops.name_scope(self._name, 'head'):
logits = head_lib._check_logits_final_dim(logits,
self.logits_dimension) # pylint:disable=protected-access
# Predict.
pred_keys = prediction_keys.PredictionKeys
with ops.name_scope(None, 'predictions', (logits, )):
probabilities = math_ops.sigmoid(logits,
name=pred_keys.PROBABILITIES)
predictions = {
pred_keys.LOGITS: logits,
pred_keys.PROBABILITIES: probabilities,
}
if mode == model_fn.ModeKeys.PREDICT:
classifier_output = head_lib._classification_output( # pylint:disable=protected-access
scores=probabilities,
n_classes=self._n_classes,
label_vocabulary=self._label_vocabulary)
return model_fn.EstimatorSpec(
mode=model_fn.ModeKeys.PREDICT,
predictions=predictions,
export_outputs={
_DEFAULT_SERVING_KEY:
classifier_output,
head_lib._CLASSIFY_SERVING_KEY:
classifier_output, # pylint:disable=protected-access
head_lib._PREDICT_SERVING_KEY: ( # pylint:disable=protected-access
export_output.PredictOutput(predictions))
})
(weighted_sum_loss, example_weight_sum,
processed_labels) = self.create_loss(features=features,
mode=mode,
logits=logits,
labels=labels)
# Eval.
if mode == model_fn.ModeKeys.EVAL:
weights = head_lib._get_weights_and_check_match_logits( # pylint:disable=protected-access,
features=features,
weight_column=self._weight_column,
logits=logits)
return model_fn.EstimatorSpec(
mode=model_fn.ModeKeys.EVAL,
predictions=predictions,
loss=weighted_sum_loss,
eval_metric_ops=self._eval_metric_ops(
labels=processed_labels,
probabilities=probabilities,
weights=weights,
weighted_sum_loss=weighted_sum_loss,
example_weight_sum=example_weight_sum))
# Train.
if train_op_fn is None:
raise ValueError('train_op_fn can not be None.')
with ops.name_scope(''):
summary.scalar(
head_lib._summary_key(self._name, metric_keys.MetricKeys.LOSS), # pylint:disable=protected-access
weighted_sum_loss)
summary.scalar(
head_lib._summary_key( # pylint:disable=protected-access
self._name, metric_keys.MetricKeys.LOSS_MEAN),
weighted_sum_loss / example_weight_sum)
return model_fn.EstimatorSpec(mode=model_fn.ModeKeys.TRAIN,
predictions=predictions,
loss=weighted_sum_loss,
train_op=train_op_fn(weighted_sum_loss))
def create_estimator_spec(
self, features, mode, logits, labels=None, train_op_fn=None):
"""See `Head`."""
with ops.name_scope(self._name, 'head'):
logits = head_lib._check_logits_final_dim(logits, self.logits_dimension) # pylint:disable=protected-access
# Predict.
pred_keys = prediction_keys.PredictionKeys
with ops.name_scope(None, 'predictions', (logits,)):
probabilities = math_ops.sigmoid(logits, name=pred_keys.PROBABILITIES)
predictions = {
pred_keys.LOGITS: logits,
pred_keys.PROBABILITIES: probabilities,
}
if mode == model_fn.ModeKeys.PREDICT:
classifier_output = head_lib._classification_output( # pylint:disable=protected-access
scores=probabilities, n_classes=self._n_classes,
label_vocabulary=self._label_vocabulary)
return model_fn.EstimatorSpec(
mode=model_fn.ModeKeys.PREDICT,
predictions=predictions,
export_outputs={
_DEFAULT_SERVING_KEY: classifier_output,
head_lib._CLASSIFY_SERVING_KEY: classifier_output, # pylint:disable=protected-access
head_lib._PREDICT_SERVING_KEY: ( # pylint:disable=protected-access
export_output.PredictOutput(predictions))
})
(weighted_sum_loss, example_weight_sum,
processed_labels) = self.create_loss(
features=features, mode=mode, logits=logits, labels=labels)
# Eval.
if mode == model_fn.ModeKeys.EVAL:
weights = head_lib._get_weights_and_check_match_logits( # pylint:disable=protected-access,
features=features, weight_column=self._weight_column, logits=logits)
return model_fn.EstimatorSpec(
mode=model_fn.ModeKeys.EVAL,
predictions=predictions,
loss=weighted_sum_loss,
eval_metric_ops=self._eval_metric_ops(
labels=processed_labels,
probabilities=probabilities,
weights=weights,
weighted_sum_loss=weighted_sum_loss,
example_weight_sum=example_weight_sum))
# Train.
if train_op_fn is None:
raise ValueError('train_op_fn can not be None.')
with ops.name_scope(''):
summary.scalar(
head_lib._summary_key(self._name, metric_keys.MetricKeys.LOSS), # pylint:disable=protected-access
weighted_sum_loss)
summary.scalar(
head_lib._summary_key( # pylint:disable=protected-access
self._name, metric_keys.MetricKeys.LOSS_MEAN),
weighted_sum_loss / example_weight_sum)
return model_fn.EstimatorSpec(
mode=model_fn.ModeKeys.TRAIN,
predictions=predictions,
loss=weighted_sum_loss,
train_op=train_op_fn(weighted_sum_loss))
