Represents real valued or numerical features.
tf.feature_column.numeric_column(
key, shape=(1,), default_value=None, dtype=tf.dtypes.float32, normalizer_fn=None
)
Example:
price = numeric_column('price')
columns = [price, ...]
features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
dense_tensor = input_layer(features, columns)
# or
bucketized_price = bucketized_column(price, boundaries=[...])
columns = [bucketized_price, ...]
features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
linear_prediction = linear_model(features, columns)
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.
|
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.
|
TensorFlow 2 version
View source on GitHub