tf.feature_column.numeric_column

Represents real valued or numerical features. (deprecated)

View aliases

Compat aliases for migration

tf.compat.v1.feature_column.numeric_column

tf.feature_column.numeric_column(
    key,
    shape=(1,),
    default_value=None,
    dtype=tf.dtypes.float32,
    normalizer_fn=None
)

Used in the notebooks

Used in the guide	Used in the tutorials
Migrate `tf.feature_column`s to Keras preprocessing layers Estimators Migration examples: Canned Estimators Migrate checkpoint saving Migrate evaluation	Classify structured data with feature columns Build a linear model with Estimators Premade Estimators Getting started End to end example for BigQuery TensorFlow reader

Example:

Assume we have data with two features a and b.

data = {'a': [15, 9, 17, 19, 21, 18, 25, 30],
   'b': [5.0, 6.4, 10.5, 13.6, 15.7, 19.9, 20.3 , 0.0]}

Let us represent the features a and b as numerical features.

a = tf.feature_column.numeric_column('a')
b = tf.feature_column.numeric_column('b')

Feature column describe a set of transformations to the inputs.

For example, to "bucketize" feature a, wrap the a column in a feature_column.bucketized_column. Providing 5 bucket boundaries, the bucketized_column api will bucket this feature in total of 6 buckets.

a_buckets = tf.feature_column.bucketized_column(a,
   boundaries=[10, 15, 20, 25, 30])

Create a DenseFeatures layer which will apply the transformations described by the set of tf.feature_column objects:

feature_layer = tf.keras.layers.DenseFeatures([a_buckets, b])
print(feature_layer(data))
tf.Tensor(
[[ 0.   0.   1.   0.   0.   0.   5. ]
 [ 1.   0.   0.   0.   0.   0.   6.4]
 [ 0.   0.   1.   0.   0.   0.  10.5]
 [ 0.   0.   1.   0.   0.   0.  13.6]
 [ 0.   0.   0.   1.   0.   0.  15.7]
 [ 0.   0.   1.   0.   0.   0.  19.9]
 [ 0.   0.   0.   0.   1.   0.  20.3]
 [ 0.   0.   0.   0.   0.   1.   0. ]], shape=(8, 7), dtype=float32)

Args
`key`	A unique string identifying the input feature. It is used as the column name and the dictionary key for feature parsing configs, feature `Tensor` objects, and feature columns.
`shape`	An iterable of integers specifies the shape of the `Tensor`. An integer can be given which means a single dimension `Tensor` with given width. The `Tensor` representing the column will have the shape of [batch_size] + `shape`.
`default_value`	A single value compatible with `dtype` or an iterable of values compatible with `dtype` which the column takes on during `tf.Example` parsing if data is missing. A default value of `None` will cause `tf.io.parse_example` to fail if an example does not contain this column. If a single value is provided, the same value will be applied as the default value for every item. If an iterable of values is provided, the shape of the `default_value` should be equal to the given `shape`.
`dtype`	defines the type of values. Default value is `tf.float32`. Must be a non-quantized, real integer or floating point type.
`normalizer_fn`	If not `None`, a function that can be used to normalize the value of the tensor after `default_value` is applied for parsing. Normalizer function takes the input `Tensor` as its argument, and returns the output `Tensor`. (e.g. lambda x: (x - 3.0) / 4.2). Please note that even though the most common use case of this function is normalization, it can be used for any kind of Tensorflow transformations.

Returns
A `NumericColumn`.

Raises
`TypeError`	if any dimension in shape is not an int
`ValueError`	if any dimension in shape is not a positive integer
`TypeError`	if `default_value` is an iterable but not compatible with `shape`
`TypeError`	if `default_value` is not compatible with `dtype`.
`ValueError`	if `dtype` is not convertible to `tf.float32`.

© 2022 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 4.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/feature_column/numeric_column