# tf.estimator.LogisticRegressionHead

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/estimator/tree/master/tensorflow_estimator/python/estimator/head/regression_head.py#L499-L583) |

Creates a `Head` for logistic regression. (deprecated)

Inherits From: [`RegressionHead`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/estimator/RegressionHead), [`Head`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/estimator/Head)

#### View aliases


**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.estimator.LogisticRegressionHead`](https://www.tensorflow.org/api_docs/python/tf/estimator/LogisticRegressionHead)

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    tf.estimator.LogisticRegressionHead(
        weight_column=None,
        loss_reduction=tf.losses.Reduction.SUM_OVER_BATCH_SIZE,
        name=None
    )

> [!WARNING]
> **Deprecated:** THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use tf.keras instead.

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)`), whereas `BinaryClassHead` also returns probabilities, classes, and class_ids.
- Export output defaults to `RegressionOutput`, whereas `BinaryClassHead` defaults to `PredictOutput`.

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)

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| ## Args ||
|---|---|
| `weight_column` | A string or a `NumericColumn` created by [`tf.feature_column.numeric_column`](https://www.tensorflow.org/versions/r2.13/api_docs/python/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`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/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. |

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| ## Attributes ||
|---|---|
| `logits_dimension` | See `base_head.Head` for details. |
| `loss_reduction` | See `base_head.Head` for details. |
| `name` | See `base_head.Head` for details. |

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## Methods

### `create_estimator_spec`

[View source](https://github.com/tensorflow/estimator/tree/master/tensorflow_estimator/python/estimator/head/base_head.py#L224-L292)

    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.

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| 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`](https://www.tensorflow.org/versions/r2.13/api_docs/python/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`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/estimator/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. |

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| Returns ||
|---|---|
| `EstimatorSpec`. ||

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### `loss`

[View source](https://github.com/tensorflow/estimator/tree/master/tensorflow_estimator/python/estimator/head/regression_head.py#L203-L226)

    loss(
        labels, logits, features=None, mode=None, regularization_losses=None
    )

Return predictions based on keys. See `base_head.Head` for details.

### `metrics`

[View source](https://github.com/tensorflow/estimator/tree/master/tensorflow_estimator/python/estimator/head/regression_head.py#L254-L269)

    metrics(
        regularization_losses=None
    )

Creates metrics. See `base_head.Head` for details.

### `predictions`

[View source](https://github.com/tensorflow/estimator/tree/master/tensorflow_estimator/python/estimator/head/regression_head.py#L228-L252)

    predictions(
        logits
    )

Return predictions based on keys.

See `base_head.Head` for details.

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| Args ||
|---|---|
| `logits` | logits `Tensor` with shape `[D0, D1, ... DN, logits_dimension]`. For many applications, the shape is `[batch_size, logits_dimension]`. |

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| Returns ||
|---|---|
| A dict of predictions. ||

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### `update_metrics`

[View source](https://github.com/tensorflow/estimator/tree/master/tensorflow_estimator/python/estimator/head/regression_head.py#L271-L297)

    update_metrics(
        eval_metrics, features, logits, labels, regularization_losses=None
    )

Updates eval metrics. See `base_head.Head` for details.