This is the reduction operation for the elementwise tf.sparse.add op.
This Op takes a SparseTensor and is the sparse counterpart to
tf.reduce_sum(). In particular, this Op also returns a dense Tensor
if output_is_sparse is False, or a SparseTensor if output_is_sparse
is True.
Reduces sp_input along the dimensions given in axis. Unless keepdims is
true, the rank of the tensor is reduced by 1 for each entry in axis. If
keepdims is true, the reduced dimensions are retained with length 1.
If axis has no entries, all dimensions are reduced, and a tensor
with a single element is returned. Additionally, the axes can be negative,
similar to the indexing rules in Python.
For example
'x' represents [[1, ?, 1]
[?, 1, ?]]
where ? is implicitly-zero.
x=tf.sparse.SparseTensor([[0,0],[0,2],[1,1]],[1,1,1],[2,3])tf.sparse.reduce_sum(x)<tf.Tensor:shape=(),dtype=int32,numpy=3>tf.sparse.reduce_sum(x,0)<tf.Tensor:shape=(3,),dtype=int32,numpy=array([1,1,1],dtype=int32)>tf.sparse.reduce_sum(x,1)# Can also use -1 as the axis<tf.Tensor:shape=(2,),dtype=int32,numpy=array([2,1],dtype=int32)>tf.sparse.reduce_sum(x,1,keepdims=True)<tf.Tensor:shape=(2,1),dtype=int32,numpy=array([[2],[1]],dtype=int32)>tf.sparse.reduce_sum(x,[0,1])<tf.Tensor:shape=(),dtype=int32,numpy=3>
Args
sp_input
The SparseTensor to reduce. Should have numeric type.
axis
The dimensions to reduce; list or scalar. If None (the
default), reduces all dimensions.
keepdims
If true, retain reduced dimensions with length 1.
output_is_sparse
If true, returns a SparseTensor instead of a dense
Tensor (the default).
name
A name for the operation (optional).
Returns
The reduced Tensor or the reduced SparseTensor if output_is_sparse is
True.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26 UTC."],[],[],null,["# tf.sparse.reduce_sum\n\n\u003cbr /\u003e\n\n|--------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.16.1/tensorflow/python/ops/sparse_ops.py#L1480-L1556) |\n\nComputes [`tf.sparse.add`](../../tf/sparse/add) of elements across dimensions of a SparseTensor. \n\n tf.sparse.reduce_sum(\n sp_input, axis=None, keepdims=None, output_is_sparse=False, name=None\n )\n\n### Used in the notebooks\n\n| Used in the tutorials |\n|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| - [Client-efficient large-model federated learning via \\`federated_select\\` and sparse aggregation](https://www.tensorflow.org/federated/tutorials/sparse_federated_learning) |\n\nThis is the reduction operation for the elementwise [`tf.sparse.add`](../../tf/sparse/add) op.\n\nThis Op takes a SparseTensor and is the sparse counterpart to\n[`tf.reduce_sum()`](../../tf/math/reduce_sum). In particular, this Op also returns a dense `Tensor`\nif `output_is_sparse` is `False`, or a `SparseTensor` if `output_is_sparse`\nis `True`.\n| **Note:** if `output_is_sparse` is True, a gradient is not defined for this function, so it can't be used in training models that need gradient descent.\n\nReduces `sp_input` along the dimensions given in `axis`. Unless `keepdims` is\ntrue, the rank of the tensor is reduced by 1 for each entry in `axis`. If\n`keepdims` is true, the reduced dimensions are retained with length 1.\n\nIf `axis` has no entries, all dimensions are reduced, and a tensor\nwith a single element is returned. Additionally, the axes can be negative,\nsimilar to the indexing rules in Python.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| For example ----------- ||\n|---|---|\n| \u003cbr /\u003e 'x' represents \\[\\[1, ?, 1\\] ============================ \\[?, 1, ?\\]\\] ============= where ? is implicitly-zero. =========================== x = tf.sparse.SparseTensor([[0, 0], [0, 2], [1, 1]], [1, 1, 1], [2, 3]) tf.sparse.reduce_sum(x) \u003ctf.Tensor: shape=(), dtype=int32, numpy=3\u003e tf.sparse.reduce_sum(x, 0) \u003ctf.Tensor: shape=(3,), dtype=int32, numpy=array([1, 1, 1], dtype=int32)\u003e tf.sparse.reduce_sum(x, 1) # Can also use -1 as the axis \u003ctf.Tensor: shape=(2,), dtype=int32, numpy=array([2, 1], dtype=int32)\u003e tf.sparse.reduce_sum(x, 1, keepdims=True) \u003ctf.Tensor: shape=(2, 1), dtype=int32, numpy= array([[2], [1]], dtype=int32)\u003e tf.sparse.reduce_sum(x, [0, 1]) \u003ctf.Tensor: shape=(), dtype=int32, numpy=3\u003e \u003cbr /\u003e ||\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|--------------------|--------------------------------------------------------------------------------------------|\n| `sp_input` | The SparseTensor to reduce. Should have numeric type. |\n| `axis` | The dimensions to reduce; list or scalar. If `None` (the default), reduces all dimensions. |\n| `keepdims` | If true, retain reduced dimensions with length 1. |\n| `output_is_sparse` | If true, returns a `SparseTensor` instead of a dense `Tensor` (the default). |\n| `name` | A name for the operation (optional). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| The reduced Tensor or the reduced SparseTensor if `output_is_sparse` is True. ||\n\n\u003cbr /\u003e"]]
View source on GitHub