tf.compat.v1.estimator.experimental

Function builder for a dnn logit_fn.

tf.compat.v1.estimator.experimental.dnn_logit_fn_builder(
    units, hidden_units, feature_columns, activation_fn, dropout,
    input_layer_partitioner, batch_norm
)

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.

Returns
A logit_fn (see below).

Raises
`ValueError`	If units is not an int.

TensorFlow

tf.compat / v1 / v1.estimator.experimental.Overview

Args

Returns

Raises

TensorFlow

tf

tf.audio

tf.autograph

tf.bitwise

tf.compat

tf.config

tf.data

tf.debugging

tf.distribute

tf.dtypes

tf.errors

tf.estimator

tf.experimental

tf.feature_column

tf.graph_util

tf.image

tf.initializers

tf.io

tf.keras

tf.linalg

tf.lite

tf.lookup

tf.losses

tf.math

tf.metrics

tf.nest

tf.nn

tf.optimizers

tf.quantization

tf.queue

tf.ragged

tf.random

tf.raw_ops

tf.saved_model

tf.sets

tf.signal

tf.sparse

tf.strings

tf.summary

tf.sysconfig

tf.test

tf.tpu

tf.train

tf.version

tf.xla

tf.compat / v1 / v1.estimator.experimental.Overview

Args

Returns

Raises