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tf.contrib.layers.transform_features

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Returns transformed features based on features columns passed in.

tf.contrib.layers.transform_features(
    features,
    feature_columns
)

Example:

columns_to_tensor = transform_features(features=features,
                                       feature_columns=feature_columns)

# Where my_features are:
# Define features and transformations
sparse_feature_a = sparse_column_with_keys(
    column_name="sparse_feature_a", keys=["AB", "CD", ...])

embedding_feature_a = embedding_column(
    sparse_id_column=sparse_feature_a, dimension=3, combiner="sum")

sparse_feature_b = sparse_column_with_hash_bucket(
    column_name="sparse_feature_b", hash_bucket_size=1000)

embedding_feature_b = embedding_column(
    sparse_id_column=sparse_feature_b, dimension=16, combiner="sum")

crossed_feature_a_x_b = crossed_column(
    columns=[sparse_feature_a, sparse_feature_b], hash_bucket_size=10000)

real_feature = real_valued_column("real_feature")
real_feature_buckets = bucketized_column(
    source_column=real_feature, boundaries=[...])

feature_columns = [embedding_feature_b,
                   real_feature_buckets,
                   embedding_feature_a]

Args:

  • features: A dictionary of features.
  • feature_columns: An iterable containing all the feature columns. All items should be instances of classes derived from _FeatureColumn.

Returns:

A dict mapping FeatureColumn to Tensor and SparseTensor values.