def create_estimator_spec(
self, features, mode, logits, labels=None, train_op_fn=None):
"""See `Head`."""
with ops.name_scope('head'):
logits = head_lib._check_logits(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:
return model_fn.EstimatorSpec(
mode=model_fn.ModeKeys.PREDICT,
predictions=predictions,
export_outputs={
'': export_output.ClassificationOutput(scores=probabilities)
})
# Eval.
unweighted_loss, processed_labels = self.create_loss(
features=features, mode=mode, logits=logits, labels=labels)
# Averages loss over classes.
per_example_loss = math_ops.reduce_mean(
unweighted_loss, axis=-1, keep_dims=True)
weights = head_lib._weights(features, self._weight_column) # pylint:disable=protected-access
training_loss = losses.compute_weighted_loss(
per_example_loss, weights=weights, reduction=losses.Reduction.SUM)
if mode == model_fn.ModeKeys.EVAL:
return model_fn.EstimatorSpec(
mode=model_fn.ModeKeys.EVAL,
predictions=predictions,
loss=training_loss,
eval_metric_ops=self._eval_metric_ops(
labels=processed_labels,
probabilities=probabilities,
weights=weights,
per_example_loss=per_example_loss))
# 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
training_loss)
summary.scalar(
head_lib._summary_key( # pylint:disable=protected-access
self._name, metric_keys.MetricKeys.LOSS_MEAN),
losses.compute_weighted_loss(
unweighted_loss, weights=weights,
reduction=losses.Reduction.MEAN))
return model_fn.EstimatorSpec(
mode=model_fn.ModeKeys.TRAIN,
predictions=predictions,
loss=training_loss,
train_op=train_op_fn(training_loss))
def create_loss(self, features, mode, logits, labels):
"""See `Head`."""
del mode # Unused for this head.
processed_labels = self._process_labels(labels)
if self._loss_fn:
unweighted_loss = _call_loss_fn(loss_fn=self._loss_fn,
labels=processed_labels,
logits=logits,
features=features)
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._weights(features, self._weight_column) # pylint:disable=protected-access,
weighted_sum_loss = losses.compute_weighted_loss(
unweighted_loss, weights=weights, reduction=losses.Reduction.SUM)
# _weights() can return 1.
example_weight_sum = math_ops.reduce_sum(
weights * array_ops.ones_like(unweighted_loss))
return head_lib.LossSpec(weighted_sum_loss=weighted_sum_loss,
example_weight_sum=example_weight_sum,
processed_labels=processed_labels)
def create_loss(self, features, mode, logits, labels):
"""See `Head`."""
del mode # Unused for this head.
processed_labels = self._process_labels(labels)
if self._loss_fn:
unweighted_loss = _call_loss_fn(
loss_fn=self._loss_fn, labels=processed_labels, logits=logits,
features=features)
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._weights(features, self._weight_column) # pylint:disable=protected-access,
weighted_sum_loss = losses.compute_weighted_loss(
unweighted_loss, weights=weights, reduction=losses.Reduction.SUM)
# _weights() can return 1.
example_weight_sum = math_ops.reduce_sum(
weights * array_ops.ones_like(unweighted_loss))
return head_lib.LossSpec(
weighted_sum_loss=weighted_sum_loss,
example_weight_sum=example_weight_sum,
processed_labels=processed_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(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:
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=head_lib._weights(features,
self._weight_column), # pylint:disable=protected-access,
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(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:
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=head_lib._weights(features, self._weight_column), # pylint:disable=protected-access,
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))
