TensorFlow 1 version | View source on GitHub |
Creates a Head
for logistic regression.
Inherits From: RegressionHead
tf.estimator.LogisticRegressionHead(
weight_column=None, loss_reduction=losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE,
name=None
)
Uses sigmoid_cross_entropy_with_logits
loss, which is the same as
BinaryClassHead
. The differences compared to BinaryClassHead
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)
), whereasBinaryClassHead
also returns probabilities, classes, and class_ids. - Export output defaults to
RegressionOutput
, whereasBinaryClassHead
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.estimator.LogisticRegressionHead()
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.estimator.LogisticRegressionHead()
logits = tf.keras.Model(...)(features)
return my_head.create_estimator_spec(
features=features,
mode=mode,
labels=labels,
optimizer=tf.keras.optimizers.Adagrad(lr=0.1),
logits=logits)
my_estimator = tf.estimator.Estimator(model_fn=_my_model_fn)
Args | |
---|---|
weight_column
|
A string or a NumericColumn created by
tf.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 of tf.losses.Reduction except NONE . Decides how to
reduce training loss over batch and label dimension. Defaults to
SUM_OVER_BATCH_SIZE , namely weighted sum of losses divided by
batch size * label_dimension .
|
name
|
name of the head. If provided, summary and metrics keys will be
suffixed by "/" + name . Also used as name_scope when creating ops.
|
Attributes | |
---|---|
logits_dimension
|
See base_head.Head for details.
|
loss_reduction
|
See base_head.Head for details.
|
name
|
See base_head.Head for details.
|
Methods
create_estimator_spec
create_estimator_spec(
features, mode, logits, labels=None, optimizer=None, trainable_variables=None,
train_op_fn=None, update_ops=None, regularization_losses=None
)
Returns EstimatorSpec
that a model_fn can return.
It is recommended to pass all args via name.
Args | |
---|---|
features
|
Input dict mapping string feature names to Tensor or
SparseTensor objects containing the values for that feature in a
minibatch. Often to be used to fetch example-weight tensor.
|
mode
|
Estimator's ModeKeys .
|
logits
|
Logits Tensor to be used by the head.
|
labels
|
Labels Tensor , or dict mapping string label names to Tensor
objects of the label values.
|
optimizer
|
An tf.keras.optimizers.Optimizer instance to optimize the
loss in TRAIN mode. Namely, sets train_op = optimizer.get_updates(loss,
trainable_variables) , which updates variables to minimize loss .
|
trainable_variables
|
A list or tuple of Variable objects to update to
minimize loss . In Tensorflow 1.x, by default these are the list of
variables collected in the graph under the key
GraphKeys.TRAINABLE_VARIABLES . As Tensorflow 2.x doesn't have
collections and GraphKeys, trainable_variables need to be passed
explicitly here.
|
train_op_fn
|
Function that takes a scalar loss Tensor and returns an op
to optimize the model with the loss in TRAIN mode. Used if optimizer
is None . Exactly one of train_op_fn and optimizer must be set in
TRAIN mode. By default, it is None in other modes. If you want to
optimize loss yourself, you can pass lambda _: tf.no_op() and then use
EstimatorSpec.loss to compute and apply gradients.
|
update_ops
|
A list or tuple of update ops to be run at training time. For example, layers such as BatchNormalization create mean and variance update ops that need to be run at training time. In Tensorflow 1.x, these are thrown into an UPDATE_OPS collection. As Tensorflow 2.x doesn't have collections, update_ops need to be passed explicitly here. |
regularization_losses
|
A list of additional scalar losses to be added to the training loss, such as regularization losses. |
Returns | |
---|---|
EstimatorSpec .
|
loss
loss(
labels, logits, features=None, mode=None, regularization_losses=None
)
Return predictions based on keys. See base_head.Head
for details.
metrics
metrics(
regularization_losses=None
)
Creates metrics. See base_head.Head
for details.
predictions
predictions(
logits
)
Return predictions based on keys. See base_head.Head
for details.
Args | |
---|---|
logits
|
logits Tensor with shape [D0, D1, ... DN, logits_dimension] .
For many applications, the shape is [batch_size, logits_dimension] .
|
Returns | |
---|---|
A dict of predictions. |
update_metrics
update_metrics(
eval_metrics, features, logits, labels, regularization_losses=None
)
Updates eval metrics. See base_head.Head
for details.