# tf.contrib.estimator.dnn_logit_fn_builder

<br />

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

Function builder for a dnn logit_fn.

#### View aliases


**Main aliases**

\`tf.estimator.experimental.dnn_logit_fn_builder\`
**Compat aliases for migration**

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

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

<br />

    tf.contrib.estimator.dnn_logit_fn_builder(
        units, hidden_units, feature_columns, activation_fn, dropout,
        input_layer_partitioner, batch_norm
    )

<br />

<br />

<br />

| ## Args ||
|---|---|
| `units` | An int indicating the dimension of the logit layer. In the MultiHead case, this should be the sum of all component Heads' logit dimensions. |
| `hidden_units` | Iterable of integer number of hidden units per layer. |
| `feature_columns` | Iterable of `feature_column._FeatureColumn` model inputs. |
| `activation_fn` | Activation function applied to each layer. |
| `dropout` | When not `None`, the probability we will drop out a given coordinate. |
| `input_layer_partitioner` | Partitioner for input layer. |
| `batch_norm` | Whether to use batch normalization after each hidden layer. |

<br />

<br />

<br />

<br />

| ## Returns ||
|---|---|
| A logit_fn (see below). ||

<br />

<br />

<br />

<br />

| ## Raises ||
|---|---|
| `ValueError` | If units is not an int. |

<br />