tf.io.RaggedFeature

Configuration for passing a RaggedTensor input feature.

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.io.RaggedFeature

tf.io.RaggedFeature(
    dtype, value_key=None, partitions=(), row_splits_dtype=tf.dtypes.int32,
    validate=False
)

value_key specifies the feature key for a variable-length list of values; and partitions specifies zero or more feature keys for partitioning those values into higher dimensions. Each element of partitions must be one of the following:

• tf.io.RaggedFeature.RowSplits(key: string)
• tf.io.RaggedFeature.RowLengths(key: string)
• tf.io.RaggedFeature.RowStarts(key: string)
• tf.io.RaggedFeature.RowLimits(key: string)
• tf.io.RaggedFeature.ValueRowIds(key: string)
• tf.io.RaggedFeature.UniformRowLength(length: int).

Where key is a feature key whose values are used to partition the values. Partitions are listed from outermost to innermost.

• If len(partitions) == 0 (the default), then:

  • A feature from a single tf.Example is parsed into a 1D tf.Tensor.
  • A feature from a batch of tf.Examples is parsed into a 2D tf.RaggedTensor, where the outer dimension is the batch dimension, and the inner (ragged) dimension is the feature length in each example.

• If len(partitions) == 1, then:

  • A feature from a single tf.Example is parsed into a 2D tf.RaggedTensor, where the values taken from the value_key are separated into rows using the partition key.

  • A feature from a batch of tf.Examples is parsed into a 3D tf.RaggedTensor, where the outer dimension is the batch dimension, the two inner dimensions are formed by separating the value_key values from each example into rows using that example's partition key.

• If len(partitions) > 1, then:

  • A feature from a single tf.Example is parsed into a tf.RaggedTensor whose rank is len(partitions)+1, and whose ragged_rank is len(partitions).

  • A feature from a batch of tf.Examples is parsed into a tf.RaggedTensor whose rank is len(partitions)+2 and whose ragged_rank is len(partitions)+1, where the outer dimension is the batch dimension.

There is one exception: if the final (i.e., innermost) element(s) of partitions are UniformRowLengths, then the values are simply reshaped (as a higher-dimensional tf.Tensor), rather than being wrapped in a tf.RaggedTensor.

Examples

import google.protobuf.text_format as pbtext
example_batch = [
  pbtext.Merge(r'''
    features {
      feature {key: "v" value {int64_list {value: [3, 1, 4, 1, 5, 9]} } }
      feature {key: "s1" value {int64_list {value: [0, 2, 3, 3, 6]} } }
      feature {key: "s2" value {int64_list {value: [0, 2, 3, 4]} } }
    }''', tf.train.Example()).SerializeToString(),
  pbtext.Merge(r'''
    features {
      feature {key: "v" value {int64_list {value: [2, 7, 1, 8, 2, 8, 1]} } }
      feature {key: "s1" value {int64_list {value: [0, 3, 4, 5, 7]} } }
      feature {key: "s2" value {int64_list {value: [0, 1, 1, 4]} } }
    }''', tf.train.Example()).SerializeToString()]

features = {
    # Zero partitions: returns 1D tf.Tensor for each Example.
    'f1': tf.io.RaggedFeature(value_key="v", dtype=tf.int64),
    # One partition: returns 2D tf.RaggedTensor for each Example.
    'f2': tf.io.RaggedFeature(value_key="v", dtype=tf.int64, partitions=[
        tf.io.RaggedFeature.RowSplits("s1")]),
    # Two partitions: returns 3D tf.RaggedTensor for each Example.
    'f3': tf.io.RaggedFeature(value_key="v", dtype=tf.int64, partitions=[
        tf.io.RaggedFeature.RowSplits("s2"),
        tf.io.RaggedFeature.RowSplits("s1")])
}

feature_dict = tf.io.parse_single_example(example_batch[0], features)
for (name, val) in sorted(feature_dict.items()):
  print('%s: %s' % (name, val))
f1: tf.Tensor([3 1 4 1 5 9], shape=(6,), dtype=int64)
f2: <tf.RaggedTensor [[3, 1], [4], [], [1, 5, 9]]>
f3: <tf.RaggedTensor [[[3, 1], [4]], [[]], [[1, 5, 9]]]>

feature_dict = tf.io.parse_example(example_batch, features)
for (name, val) in sorted(feature_dict.items()):
  print('%s: %s' % (name, val))
f1: <tf.RaggedTensor [[3, 1, 4, 1, 5, 9],
                      [2, 7, 1, 8, 2, 8, 1]]>
f2: <tf.RaggedTensor [[[3, 1], [4], [], [1, 5, 9]],
                      [[2, 7, 1], [8], [2], [8, 1]]]>
f3: <tf.RaggedTensor [[[[3, 1], [4]], [[]], [[1, 5, 9]]],
                      [[[2, 7, 1]], [], [[8], [2], [8, 1]]]]>

Fields:

• dtype: Data type of the RaggedTensor. Must be one of: tf.dtypes.int64, tf.dtypes.float32, tf.dtypes.string.
• value_key: (Optional.) Key for a Feature in the input Example, whose parsed Tensor will be the resulting RaggedTensor.flat_values. If not specified, then it defaults to the key for this RaggedFeature.
• partitions: (Optional.) A list of objects specifying the row-partitioning tensors (from outermost to innermost). Each entry in this list must be one of:
  • tf.io.RaggedFeature.RowSplits(key: string)
  • tf.io.RaggedFeature.RowLengths(key: string)
  • tf.io.RaggedFeature.RowStarts(key: string)
  • tf.io.RaggedFeature.RowLimits(key: string)
  • tf.io.RaggedFeature.ValueRowIds(key: string)
  • tf.io.RaggedFeature.UniformRowLength(length: int). Where key is a key for a Feature in the input Example, whose parsed Tensor will be the resulting row-partitioning tensor.
• row_splits_dtype: (Optional.) Data type for the row-partitioning tensor(s). One of int32 or int64. Defaults to int32.
• validate: (Optional.) Boolean indicating whether or not to validate that the input values form a valid RaggedTensor. Defaults to False.

Attributes
dtype
value_key
partitions
row_splits_dtype
validate

Child Classes

class RowLengths

class RowLimits

class RowSplits

class RowStarts

class UniformRowLength

class ValueRowIds