Module: tf.feature_column
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Public API for tf.feature_column namespace.
Functions
bucketized_column(...): Represents discretized dense input.
categorical_column_with_hash_bucket(...): Represents sparse feature where ids are set by hashing.
categorical_column_with_identity(...): A CategoricalColumn that returns identity values.
categorical_column_with_vocabulary_file(...): A CategoricalColumn with a vocabulary file.
categorical_column_with_vocabulary_list(...): A CategoricalColumn with in-memory vocabulary.
crossed_column(...): Returns a column for performing crosses of categorical features.
embedding_column(...): DenseColumn that converts from sparse, categorical input.
indicator_column(...): Represents multi-hot representation of given categorical column.
make_parse_example_spec(...): Creates parsing spec dictionary from input feature_columns.
numeric_column(...): Represents real valued or numerical features.
sequence_categorical_column_with_hash_bucket(...): A sequence of categorical terms where ids are set by hashing.
sequence_categorical_column_with_identity(...): Returns a feature column that represents sequences of integers.
sequence_categorical_column_with_vocabulary_file(...): A sequence of categorical terms where ids use a vocabulary file.
sequence_categorical_column_with_vocabulary_list(...): A sequence of categorical terms where ids use an in-memory list.
sequence_numeric_column(...): Returns a feature column that represents sequences of numeric data.
shared_embeddings(...): List of dense columns that convert from sparse, categorical input.
weighted_categorical_column(...): Applies weight values to a CategoricalColumn.
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Last updated 2020-10-01 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2020-10-01 UTC."],[],[],null,["# Module: tf.feature_column\n\n\u003cbr /\u003e\n\n|---------------------------------------------------------------------------|\n| [TensorFlow 1 version](/versions/r1.15/api_docs/python/tf/feature_column) |\n\nPublic API for tf.feature_column namespace.\n\nFunctions\n---------\n\n[`bucketized_column(...)`](../tf/feature_column/bucketized_column): Represents discretized dense input.\n\n[`categorical_column_with_hash_bucket(...)`](../tf/feature_column/categorical_column_with_hash_bucket): Represents sparse feature where ids are set by hashing.\n\n[`categorical_column_with_identity(...)`](../tf/feature_column/categorical_column_with_identity): A `CategoricalColumn` that returns identity values.\n\n[`categorical_column_with_vocabulary_file(...)`](../tf/feature_column/categorical_column_with_vocabulary_file): A `CategoricalColumn` with a vocabulary file.\n\n[`categorical_column_with_vocabulary_list(...)`](../tf/feature_column/categorical_column_with_vocabulary_list): A `CategoricalColumn` with in-memory vocabulary.\n\n[`crossed_column(...)`](../tf/feature_column/crossed_column): Returns a column for performing crosses of categorical features.\n\n[`embedding_column(...)`](../tf/feature_column/embedding_column): `DenseColumn` that converts from sparse, categorical input.\n\n[`indicator_column(...)`](../tf/feature_column/indicator_column): Represents multi-hot representation of given categorical column.\n\n[`make_parse_example_spec(...)`](../tf/feature_column/make_parse_example_spec): Creates parsing spec dictionary from input feature_columns.\n\n[`numeric_column(...)`](../tf/feature_column/numeric_column): Represents real valued or numerical features.\n\n[`sequence_categorical_column_with_hash_bucket(...)`](../tf/feature_column/sequence_categorical_column_with_hash_bucket): A sequence of categorical terms where ids are set by hashing.\n\n[`sequence_categorical_column_with_identity(...)`](../tf/feature_column/sequence_categorical_column_with_identity): Returns a feature column that represents sequences of integers.\n\n[`sequence_categorical_column_with_vocabulary_file(...)`](../tf/feature_column/sequence_categorical_column_with_vocabulary_file): A sequence of categorical terms where ids use a vocabulary file.\n\n[`sequence_categorical_column_with_vocabulary_list(...)`](../tf/feature_column/sequence_categorical_column_with_vocabulary_list): A sequence of categorical terms where ids use an in-memory list.\n\n[`sequence_numeric_column(...)`](../tf/feature_column/sequence_numeric_column): Returns a feature column that represents sequences of numeric data.\n\n[`shared_embeddings(...)`](../tf/feature_column/shared_embeddings): List of dense columns that convert from sparse, categorical input.\n\n[`weighted_categorical_column(...)`](../tf/feature_column/weighted_categorical_column): Applies weight values to a `CategoricalColumn`."]]
TensorFlow 1 version