TensorFlow Machine Learning Cookbook

Working with Matrices

Understanding how TensorFlow works with matrices is very important to understanding the flow of data through computational graphs.

Getting ready

Many algorithms depend on matrix operations. TensorFlow gives us easy-to-use operations to perform such matrix calculations. For all of the following examples, we can create a graph session by running the following code:

import tensorflow as tf
sess = tf.Session()

How to do it…

1. Creating matrices: We can create two-dimensional matrices from numpy arrays or nested lists, as we described in the earlier section on tensors. We can also use the tensor creation functions and specify a two-dimensional shape for functions such as zeros(), ones(), truncated_normal(), and so on. TensorFlow also ...