Represents real valued or numerical features. (deprecated)
View aliases
Compat aliases for migration
See Migration guide for more details.
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 |
---|---|
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 .
|