tf.RaggedTensor

Represents a ragged tensor.

Used in the notebooks

Used in the guide Used in the tutorials

A RaggedTensor is a tensor with one or more ragged dimensions, which are dimensions whose slices may have different lengths. For example, the inner (column) dimension of rt=[[3, 1, 4, 1], [], [5, 9, 2], [6], []] is ragged, since the column slices (rt[0, :], ..., rt[4, :]) have different lengths. Dimensions whose slices all have the same length are called uniform dimensions. The outermost dimension of a RaggedTensor is always uniform, since it consists of a single slice (and so there is no possibility for differing slice lengths).

The total number of dimensions in a RaggedTensor is called its rank, and the number of ragged dimensions in a RaggedTensor is called its ragged-rank. A RaggedTensor's ragged-rank is fixed at graph creation time: it can't depend on the runtime values of Tensors, and can't vary dynamically for different session runs.

Potentially Ragged Tensors

Many ops support both Tensors and RaggedTensors. The term "potentially ragged tensor" may be used to refer to a tensor that might be either a Tensor or a RaggedTensor. The ragged-rank of a Tensor is zero.

Documenting RaggedTensor Shapes

When documenting the shape of a RaggedTensor, ragged dimensions can be indicated by enclosing them in parentheses. For example, the shape of a 3-D RaggedTensor that stores the fixed-size word embedding for each word in a sentence, for each sentence in a batch, could be written as [num_sentences, (num_words), embedding_size]. The parentheses around (num_words) indicate that dimension is ragged, and that the length of each element list in that dimension may vary for each item.

Component Tensors

Internally, a RaggedTensor consists of a concatenated list of values that are partitioned into variable-length rows. In particular, each RaggedTensor consists of:

  • A values tensor, which concatenates the variable-length rows into a flattened list. For example, the values tensor for [[3, 1, 4, 1], [], [5, 9, 2], [6], []] is [3, 1, 4, 1, 5, 9, 2, 6].

  • A row_splits vector, which indicates how those flattened values are divided into rows. In particular, the values for row rt[i] are stored in the slice rt.values[rt.row_splits[i]:rt.row_splits[i+1]].

Example:

print(tf.RaggedTensor.from_row_splits(
      values=[3, 1, 4, 1, 5, 9, 2, 6],
      row_splits=[0, 4, 4, 7, 8, 8]))
<tf.RaggedTensor [[3, 1, 4, 1], [], [5, 9, 2], [6], []]>

Alternative Row-Partitioning Schemes

In addition to row_splits, ragged tensors provide support for five other row-partitioning schemes:

  • row_lengths: a vector with shape [nrows], which specifies the length of each row.

  • value_rowids and nrows: value_rowids is a vector with shape [nvals], corresponding one-to-one with values, which specifies each value's row index. In particular, the row rt[row] consists of the values rt.values[j] where value_rowids[j]==row. nrows is an integer scalar that specifies the number of rows in the RaggedTensor. (nrows is used to indicate trailing empty rows.)

  • row_starts: a vector with shape [nrows], which specifies the start offset of each row. Equivalent to row_splits[:-1].

  • row_limits: a vector with shape [nrows]<