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tf.feature_column.numeric_column

Represents real valued or numerical features.

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)

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