tf.estimator.Head

Interface for the head/top of a model. (deprecated)

View aliases

Compat aliases for migration

tf.compat.v1.estimator.Head

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 `[batch_size, logits_dimension]`.
`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.

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 [batch_size, logits_dimension].

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`

View source

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`

View source

@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`

View source

@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`

View source

@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`

View source

@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.