# How to Create Basic Math Operations in TensorFlow

Machine learning applications are fundamentally mathematical, and TensorFlow provides a wealth of routines for performing mathematical operations on tensors. Each routine is represented by a function of the `tf `

package, and each function returns a tensor. When it comes to TensorFlow operations, its best to start simple. The following table lists 12 functions that perform basic math operations.

Basic Math Operations

Function | Description |

`add(x, y, name=None)` |
Adds two tensors |

`subtract(x, y, name=None)` |
Subtracts two tensors |

`multiply(x, y, name=None)` |
Multiplies two tensors |

`divide(x, y, name=None)` |
Divides the elements of two tensors |

`div(x, y, name=None)` |
Divides the elements of two tensors |

`add_n(inputs, name=None)` |
Adds multiple tensors |

`scalar_mul(scalar, x)` |
Scales a tensor by a scalar value |

`mod(x, y, name=None)` |
Performs the modulo operation |

`abs(x, name=None)` |
Computes the absolute value |

`negative(x, name=None)` |
Negates the tensor’s elements |

`sign(x, name=None)` |
Extracts the signs of the tensorâ€™s element |

`reciprocal(x, name=None)` |
Computes the reciprocals |

The first four functions perform element-wise arithmetic. The following code demonstrates how they work:

a = tf.constant([3., 3., 3.]) b = tf.constant([2., 2., 2.]) sum = tf.add(a, b) # [ 5. 5. 5. ] diff = tf.subtract(a, b) # [ 1. 1. 1. ] prod = tf.multiply(a, b) # [ 6. 6. 6. ] quot = tf.divide(a, b) # [ 1.5 1.5 1.5 ]

Applications can perform identical operations by using regular Python operators, such as +, -, *, /, and //. For example, the following two lines of code create the same tensor:

total = tf.add(a, b) # [ 5. 5. 5. ] total2 = a + b # [ 5. 5. 5. ]

When operating on floating-point values, `div `

and `divide `

produce the same result. But for integer division, `divide `

returns a floating-point result, and `div `

returns an integer result. The following code demonstrates the difference between them:

a = tf.constant([3, 3, 3]) b = tf.constant([2, 2, 2]) div1 = tf.divide(a, b) # [ 1.5 1.5 1.5 ] div2 = a / b # [ 1.5 1.5 1.5 ] div3 = tf.div(a, b) # [ 1 1 1 ] div4 = a // b # [ 1 1 1 ]

The `div `

function and the `/`

operator both perform element-wise division. In contrast, the `divide `

function performs Python-style division.