/TensorFlow 2.4


Applies op to the .values tensor of one or more SparseTensors.

Replaces any SparseTensor in args or kwargs with its values tensor (which contains the non-default values for the SparseTensor), and then calls op. Returns a SparseTensor that is constructed from the input SparseTensors' indices, dense_shape, and the value returned by the op.

If the input arguments contain multiple SparseTensors, then they must have equal indices and dense shapes.


s = tf.sparse.from_dense([[1, 2, 0],
                          [0, 4, 0],
                          [1, 0, 0]])
tf.sparse.to_dense(tf.sparse.map_values(tf.ones_like, s)).numpy()
array([[1, 1, 0],
       [0, 1, 0],
       [1, 0, 0]], dtype=int32)
tf.sparse.to_dense(tf.sparse.map_values(tf.multiply, s, s)).numpy()
array([[ 1,  4,  0],
       [ 0, 16,  0],
       [ 1,  0,  0]], dtype=int32)
tf.sparse.to_dense(tf.sparse.map_values(tf.add, s, 5)).numpy()
array([[6, 7, 0],
       [0, 9, 0],
       [6, 0, 0]], dtype=int32)
Note: even though tf.add(0, 5) != 0, implicit zeros will remain unchanged. However, if the sparse tensor contains any explict zeros, these will be affected by the mapping!
op The operation that should be applied to the SparseTensor values. op is typically an element-wise operation (such as math_ops.add), but any operation that preserves the shape can be used.
*args Arguments for op.
**kwargs Keyword arguments for op.
A SparseTensor whose indices and dense_shape matches the indices and dense_shape of all input SparseTensors.
ValueError If args contains no SparseTensor, or if the indices or dense_shapes of the input SparseTensors are not equal.

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Code samples licensed under the Apache 2.0 License.