# W3cubDocs

/TensorFlow Python

# Math

Note: Functions taking `Tensor` arguments can also take anything accepted by `tf.convert_to_tensor`.

## Arithmetic Operators

TensorFlow provides several operations that you can use to add basic arithmetic operators to your graph.

## Basic Math Functions

TensorFlow provides several operations that you can use to add basic mathematical functions to your graph.

## Matrix Math Functions

TensorFlow provides several operations that you can use to add linear algebra functions on matrices to your graph.

## Tensor Math Function

TensorFlow provides operations that you can use to add tensor functions to your graph.

## Complex Number Functions

TensorFlow provides several operations that you can use to add complex number functions to your graph.

## Reduction

TensorFlow provides several operations that you can use to perform common math computations that reduce various dimensions of a tensor.

## Scan

TensorFlow provides several operations that you can use to perform scans (running totals) across one axis of a tensor.

## Segmentation

TensorFlow provides several operations that you can use to perform common math computations on tensor segments. Here a segmentation is a partitioning of a tensor along the first dimension, i.e. it defines a mapping from the first dimension onto `segment_ids`. The `segment_ids` tensor should be the size of the first dimension, `d0`, with consecutive IDs in the range `0` to `k`, where `k<d0`. In particular, a segmentation of a matrix tensor is a mapping of rows to segments.

For example:

```c = tf.constant([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]])
tf.segment_sum(c, tf.constant([0, 0, 1]))
==>  [[0 0 0 0]
[5 6 7 8]]
```

## Sequence Comparison and Indexing

TensorFlow provides several operations that you can use to add sequence comparison and index extraction to your graph. You can use these operations to determine sequence differences and determine the indexes of specific values in a tensor.