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Parses tf.Examples to extract tensors for given feature_columns.
tf.contrib.layers.parse_feature_columns_from_examples(
serialized,
feature_columns,
name=None,
example_names=None
)
This is a wrapper of 'tf.io.parse_example'.
Example:
columns_to_tensor = parse_feature_columns_from_examples(
serialized=my_data,
feature_columns=my_features)
# 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=[...])
my_features = [embedding_feature_b, real_feature_buckets, embedding_feature_a]
Args:
serialized
: A vector (1-D Tensor) of strings, a batch of binary serializedExample
protos.feature_columns
: An iterable containing all the feature columns. All items should be instances of classes derived from _FeatureColumn.name
: A name for this operation (optional).example_names
: A vector (1-D Tensor) of strings (optional), the names of the serialized protos in the batch.
Returns:
A dict
mapping FeatureColumn to Tensor
and SparseTensor
values.