TensorFlow 2 version | View source on GitHub |
Creates a Head
for single label binary classification.
Inherits From: Head
tf.estimator.BinaryClassHead(
weight_column=None, thresholds=None, label_vocabulary=None,
loss_reduction=losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE, loss_fn=None,
name=None
)
Uses sigmoid_cross_entropy_with_logits
loss.
The head expects logits
with shape [D0, D1, ... DN, 1]
.
In many applications, the shape is [batch_size, 1]
.
labels
must be a dense Tensor
with shape matching logits
, namely
[D0, D1, ... DN, 1]
. If label_vocabulary
given, labels
must be a string
Tensor
with values from the vocabulary. If label_vocabulary
is not given,
labels
must be float Tensor
with values in the interval [0, 1]
.
If weight_column
is specified, weights must be of shape
[D0, D1, ... DN]
, or [D0, D1, ... DN, 1]
.
The loss is the weighted sum over the input dimensions. Namely, if the input
labels have shape [batch_size, 1]
, the loss is the weighted sum over
batch_size
.
Also supports custom loss_fn
. loss_fn
takes (labels, logits)
or
(labels, logits, features, loss_reduction)
as arguments and returns loss
with shape [D0, D1, ... DN, 1]
. loss_fn
must support float labels
with
shape [D0, D1, ... DN, 1]
. Namely, the head applies label_vocabulary
to
the input labels before passing them to loss_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.
|
thresholds
|
Iterable of floats in the range (0, 1) . For binary
classification metrics such as precision and recall, an eval metric is
generated for each threshold value. This threshold is applied to the
logistic values to determine the binary classification (i.e., above the
threshold is true , below is false .
|
label_vocabulary
|
A list or tuple of strings representing possible label
values. If it is not given, that means labels are already encoded within
[0, 1]. If given, labels must be string type and have any value in
label_vocabulary . Note that errors will be raised if label_vocabulary
is not provided but labels are strings.
|
loss_reduction
|
One of tf.losses.Reduction except NONE . Decides how to
reduce training loss over batch. Defaults to SUM_OVER_BATCH_SIZE , namely
weighted sum of losses divided by batch size * label_dimension .
|
loss_fn
|
Optional loss function. |
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
)
Returns regularized training loss. See base_head.Head
for details.
metrics
metrics(
regularization_losses=None
)
Creates metrics. See base_head.Head
for details.
predictions
predictions(
logits, keys=None
)
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] .
|
keys
|
a list or tuple of prediction keys. Each key can be either the class variable of prediction_keys.PredictionKeys or its string value, such as: prediction_keys.PredictionKeys.CLASSES or 'classes'. If not specified, it will return the predictions for all valid keys. |
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.