Feature Columns Document For Free

Features of Tensorflow a. Responsive Construct. ... b. Flexible. ... c. Easily Trainable. ... d. Parallel Neural Network Training. ... e. Large Community. ... f. Open Source. g. Feature Columns. ... h. Availability of Statistical Distributions.

Each feature, or column, represents a measurable piece of data that can be used for analysis: Name, Age, Sex, Fare, and so on. Features are also sometimes referred to as ?variables? or ?attributes.? Depending on what you're trying to analyze, the features you include in your dataset can vary widely.

A feature column can be a plain mapping to some input column (e.g. column_numeric() for a column of numerical data), or a transformation of other feature columns (e.g. column_crossed() to define a new column as the cross of two other feature columns). The following feature columns are available: Feature Column.

# bucketized_column converts numerical feature to a categorical one.

This document details feature columns and how they can be used as inputs to neural networks using TensorFlow. Feature columns are very rich, enabling you to transform a diverse range of raw data into formats that neural networks can use, allowing easy experimentation.

COLUMNS AND FEATURES Column is usually a writing printed under the same topic while features have always different topics. 2. Features do not have a specific place (except in Magazines), while columns are printed on a specific place.

Think of feature columns as the intermediaries between raw data and Estimators. Feature columns are very rich, enabling you to transform a diverse range of raw data into formats that Estimators can use, allowing easy experimentation.

Functions. bucketizedcolumn(...) : Represents discretized dense input bucketed by boundaries . categoricalcolumnwithhashbucket(...) : Represents sparse feature where ids are set by hashing.

Classify structured data with feature columns Numeric columns. Bucketized columns. Categorical columns. Embedding columns. Hashed feature columns. Crossed feature columns.

Feature columns are used to specify how Tensors received from the input function should be combined and transformed before entering the model.