tf.feature_column.indicator

TensorFlow 1 version

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Represents real valued or numerical features.

View aliases

Compat aliases for migration

See Migration guide for more details.

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
Estimators	Classify structured data with feature columns Gradient Boosted Trees: Model understanding Boosted trees using Estimators Build a linear model with Estimators Premade Estimators

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.

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`.

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tf.feature_column / indicator_column

View aliases

Used in the notebooks

Example:

Args

Returns

Raises