TensorFlow Math

Getting the input is great, but now you need to use it. You're going to use basic math functions that everyone knows and loves - add, subtract, multiply, and divide - with tensors. (There's many more math functions you can check out in the documentation .)

Addition

x = tf.add(5, 2)  # 7

You’ll start with the add function. The tf.add() function does exactly what you expect it to do. It takes in two numbers, two tensors, or one of each, and returns their sum as a tensor.

Subtraction and Multiplication

Here’s an example with subtraction and multiplication.

x = tf.subtract(10, 4) # 6
y = tf.multiply(2, 5)  # 10

The x tensor will evaluate to 6 , because 10 - 4 = 6 . The y tensor will evaluate to 10 , because 2 * 5 = 10 . That was easy!

Converting types

It may be necessary to convert between types to make certain operators work together. For example, if you tried the following, it would fail with an exception:

tf.subtract(tf.constant(2.0),tf.constant(1))  # Fails with ValueError: Tensor conversion requested dtype float32 for Tensor with dtype int32: 

That's because the constant 1 is an integer but the constant 2.0 is a floating point value and subtract expects them to match.

In cases like these, you can either make sure your data is all of the same type, or you can cast a value to another type. In this case, converting the 2.0 to an integer before subtracting, like so, will give the correct result:

tf.subtract(tf.cast(tf.constant(2.0), tf.int32), tf.constant(1))   # 1

Quiz

Let's apply what you learned to convert an algorithm to TensorFlow. The code below is a simple algorithm using division and subtraction. Convert the following algorithm in regular Python to TensorFlow and print the results of the session. You can use tf.constant() for the values 10 , 2 , and 1 .