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.
|