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# 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.

Note that the __init__ constructor is private. Please use one of the following methods to construct a RaggedTensor:

### Potentially Ragged Tensors

Many ops support both Tensors and RaggedTensors (see tf.ragged for a full listing). 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], which specifies the stop offset of each row. Equivalent to row_splits[1:].

• uniform_row_length: A scalar tensor, specifying the length of every row. This row-partitioning scheme may only be used if all rows have the same length.

Example: The following ragged tensors are equivalent, and all represent the nested list [[3, 1, 4, 1], [], [5, 9, 2], [6], []].

values = [3, 1, 4, 1, 5, 9, 2, 6]
rt1 = RaggedTensor.from_row_splits(values, row_splits=[0, 4, 4, 7, 8, 8])
rt2 = RaggedTensor.from_row_lengths(values, row_lengths=[4, 0, 3, 1, 0])
rt3 = RaggedTensor.from_value_rowids(
    values, value_rowids=[0, 0, 0, 0, 2, 2, 2, 3], nrows=5)
rt4 = RaggedTensor.from_row_starts(values, row_starts=[0, 4, 4, 7, 8])
rt5 = RaggedTensor.from_row_limits(values, row_limits=[4, 4, 7, 8, 8])


### Multiple Ragged Dimensions

RaggedTensors with multiple ragged dimensions can be defined by using a nested RaggedTensor for the values tensor. Each nested RaggedTensor adds a single ragged dimension.

inner_rt = RaggedTensor.from_row_splits(  # =rt1 from above
    values=[3, 1, 4, 1, 5, 9, 2, 6], row_splits=[0, 4, 4, 7, 8, 8])
outer_rt = RaggedTensor.from_row_splits(
    values=inner_rt, row_splits=[0, 3, 3, 5])
print(outer_rt.to_list())
[[[3, 1, 4, 1], [], [5, 9, 2]], [], [[6], []]]
print(outer_rt.ragged_rank)
2


The factory function RaggedTensor.from_nested_row_splits may be used to construct a RaggedTensor with multiple ragged dimensions directly, by providing a list of row_splits tensors: