# tf.sets.difference

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|---|
| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/sets_impl.py#L208-L286) |

Compute set difference of elements in last dimension of `a` and `b`.

#### View aliases


**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.sets.difference`](https://www.tensorflow.org/api_docs/python/tf/sets/difference), [`tf.compat.v1.sets.set_difference`](https://www.tensorflow.org/api_docs/python/tf/sets/difference)

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    tf.sets.difference(
        a, b, aminusb=True, validate_indices=True
    )

All but the last dimension of `a` and `b` must match.

#### Example:

      import tensorflow as tf
      import collections

      # Represent the following array of sets as a sparse tensor:
      # a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]])
      a = collections.OrderedDict([
          ((0, 0, 0), 1),
          ((0, 0, 1), 2),
          ((0, 1, 0), 3),
          ((1, 0, 0), 4),
          ((1, 1, 0), 5),
          ((1, 1, 1), 6),
      ])
      a = tf.sparse.SparseTensor(list(a.keys()), list(a.values()),
                                 dense_shape=[2, 2, 2])

      # np.array([[{1, 3}, {2}], [{4, 5}, {5, 6, 7, 8}]])
      b = collections.OrderedDict([
          ((0, 0, 0), 1),
          ((0, 0, 1), 3),
          ((0, 1, 0), 2),
          ((1, 0, 0), 4),
          ((1, 0, 1), 5),
          ((1, 1, 0), 5),
          ((1, 1, 1), 6),
          ((1, 1, 2), 7),
          ((1, 1, 3), 8),
      ])
      b = tf.sparse.SparseTensor(list(b.keys()), list(b.values()),
                                 dense_shape=[2, 2, 4])

      # `set_difference` is applied to each aligned pair of sets.
      tf.sets.difference(a, b)

      # The result will be equivalent to either of:
      #
      # np.array([[{2}, {3}], [{}, {}]])
      #
      # collections.OrderedDict([
      #     ((0, 0, 0), 2),
      #     ((0, 1, 0), 3),
      # ])

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| ## Args ||
|---|---|
| `a` | `Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices must be sorted in row-major order. |
| `b` | `Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices must be sorted in row-major order. |
| `aminusb` | Whether to subtract `b` from `a`, vs vice versa. |
| `validate_indices` | Whether to validate the order and range of sparse indices in `a` and `b`. |

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| ## Returns ||
|---|---|
| A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but the last dimension the same. Elements along the last dimension contain the differences. ||

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| ## Raises ||
|---|---|
| `TypeError` | If inputs are invalid types, or if `a` and `b` have different types. |
| `ValueError` | If `a` is sparse and `b` is dense. |
| `errors_impl.InvalidArgumentError` | If the shapes of `a` and `b` do not match in any dimension other than the last dimension. |

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