/TensorFlow Python



Defined in tensorflow/python/feature_column/feature_column.py.

Represents real valued or numerical features.


price = numeric_column('price')
columns = [price, ...]
features = tf.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.parse_example(..., features=make_parse_example_spec(columns))
linear_prediction = linear_model(features, columns)


  • 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.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.


A _NumericColumn.


  • 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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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.