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Interface for the head/top of a model. (deprecated)
tf.estimator.Head(
*args, **kwargs
)
Head sits on top of the model network and handles computing the outputs of the network. Given logits (or output of a hidden layer), a Head knows how to compute predictions, loss, train_op, metrics and export outputs. It is meant to:
- Simplify writing model_fn and to make model_fn more configurable for Estimator.
- Simpilfy creating loss and metrics for the train and test loop in Eager execution.
- Support wide range of machine learning models. Since most heads can work with logits, they can support DNN, RNN, Wide, Wide&Deep, Global objectives, Gradient boosted trees and many other types of machine learning models.
Common usage:
Here is simplified model_fn to build a DNN regression model.
def _my_dnn_model_fn(features, labels, mode, params, config=None):
# Optionally your callers can pass head to model_fn as a param.
head = tf.estimator.RegressionHead(...)
feature_columns = tf.feature_column.numeric_column(...)
feature_layer = tf.keras.layers.DenseFeatures(feature_columns)
inputs = feature_layer(features)
# Compute logits with tf.keras.layers API
hidden_layer0 = tf.keras.layers.Dense(
units=1000, activation="relu")(inputs)
hidden_layer1 = tf.keras.layers.Dense(
units=500, activation="relu")(hidden_layer0)
logits = tf.keras.layers.Dense(
units=head.logits_dimension, activation=None)(hidden_layer1)
# Or use Keras model for logits computation
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(units=1000, activation="relu"))
model.add(tf.keras.layers.Dense(units=500, activation="relu"))
model.add(tf.keras.layers.Dense(
units=head.logits_dimension, activation=None))
logits = model(inputs)
return head.create_estimator_spec(
features=features,
labels=labels,
mode=mode,
logits=logits,
optimizer=optimizer)
Attributes | |
---|---|
logits_dimension
|
Size of the last dimension of the logits Tensor .
Often is the number of classes, labels, or real values to be predicted.
Typically, logits is of shape |
loss_reduction
|
One of tf.losses.Reduction .
Describes how to reduce training loss over batch, such as mean or sum. |
name
|
The name of this head. |
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
@abc.abstractmethod
loss( labels, logits, features=None, mode=None, regularization_losses=None )
Returns a loss Tensor
from provided arguments.
Note that, the args of features
and mode
are most likely not used, but
some Head implementations may require them.
Args | |
---|---|
labels
|
Labels Tensor , or dict mapping string label names to Tensor
objects of the label values.
|
logits
|
Logits Tensor to be used for loss construction.
|
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 . To be used in case loss calculation is
different in Train and Eval mode.
|
regularization_losses
|
A list of additional scalar losses to be added to the training loss, such as regularization losses. |
Returns | |
---|---|
A scalar Tensor representing regularized training loss used in train and
eval.
|
metrics
@abc.abstractmethod
metrics( regularization_losses=None )
Returns a dict
of metric objects.
Args | |
---|---|
regularization_losses
|
A list of additional scalar losses to be added to the training loss, such as regularization losses. |
Returns | |
---|---|
A dict of metrics keyed by string name. The value is an instance of
Metric class.
|
predictions
@abc.abstractmethod
predictions( logits, keys=None )
Returns a dict
of predictions from provided logits.
Args | |
---|---|
logits
|
Logits Tensor to be used for prediction construction.
|
keys
|
A list of string for prediction keys. Defaults to None , meaning
if not specified, predictions will be created for all the pre-defined
valid keys in the head.
|
Returns | |
---|---|
A dict of predicted Tensor keyed by prediction name.
|
update_metrics
@abc.abstractmethod
update_metrics( eval_metrics, features, logits, labels, mode=None, regularization_losses=None )
Updates metric objects and returns a dict
of the updated metrics.
Args | |
---|---|
eval_metrics
|
A dict of metrics to be updated.
|
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.
|
logits
|
logits Tensor to be used for metrics update.
|
labels
|
Labels Tensor , or dict mapping string label names to Tensor
objects of the label values.
|
mode
|
Estimator's ModeKeys . In most cases, this arg is not used and can
be removed in the method implementation.
|
regularization_losses
|
A list of additional scalar losses to be added to
the training and evaluation loss, such as regularization losses. Note
that, the mode arg is not used in the tf.estimator.*Head . If the
update of the metrics doesn't rely on mode , it can be safely ignored
in the method signature.
|
Returns | |
---|---|
A dict of updated metrics keyed by name. The value is an instance of
Metric class.
|