tf.contrib.estimator.logistic_regression_head(
weight_column=None,
loss_reduction=losses.Reduction.SUM_OVER_BATCH_SIZE,
name=None
)
Creates a _Head
for logistic regression.
Uses sigmoid_cross_entropy_with_logits
loss, which is the same as
binary_classification_head
. The differences compared to
binary_classification_head
are:
- Does not support
label_vocabulary
. Instead, labels must be float in the range [0, 1]. - Does not calculate some metrics that do not make sense, such as AUC.
- In
PREDICT
mode, only returns logits and predictions (=tf.sigmoid(logits)
), whereasbinary_classification_head
also returns probabilities, classes, and class_ids. - Export output defaults to
RegressionOutput
, whereasbinary_classification_head
defaults toPredictOutput
.
The head expects logits
with shape [D0, D1, ... DN, 1]
.
In many applications, the shape is [batch_size, 1]
.
The labels
shape must match logits
, namely
[D0, D1, ... DN]
or [D0, D1, ... DN, 1]
.
If weight_column
is specified, weights must be of shape
[D0, D1, ... DN]
or [D0, D1, ... DN, 1]
.
This is implemented as a generalized linear model, see https://en.wikipedia.org/wiki/Generalized_linear_model.
The head can be used with a canned estimator. Example:
my_head = tf.contrib.estimator.logistic_regression_head()
my_estimator = tf.estimator.DNNEstimator(
head=my_head,
hidden_units=...,
feature_columns=...)
It can also be used with a custom model_fn
. Example:
def _my_model_fn(features, labels, mode):
my_head = tf.contrib.estimator.logistic_regression_head()
logits = tf.keras.Model(...)(features)
return my_head.create_estimator_spec(
features=features,
mode=mode,
labels=labels,
optimizer=tf.AdagradOptimizer(learning_rate=0.1),
logits=logits)
my_estimator = tf.estimator.Estimator(model_fn=_my_model_fn)
Args:
weight_column
: A string or a_NumericColumn
created bytf.feature_column.numeric_column
defining feature column representing weights. It is used to down weight or boost examples during training. It will be multiplied by the loss of the example.loss_reduction
: One oftf.losses.Reduction
exceptNONE
. Describes how to reduce training loss over batch and label dimension. Defaults toSUM_OVER_BATCH_SIZE
, namely weighted sum of losses divided bybatch size * label_dimension
. Seetf.losses.Reduction
.name
: name of the head. If provided, summary and metrics keys will be suffixed by"/" + name
. Also used asname_scope
when creating ops.
Returns:
An instance of _Head
for logistic regression.
Raises:
ValueError
: Ifloss_reduction
is invalid.