Understanding how TensorFlow works with matrices is very important to understanding the flow of data through computational graphs.
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()
numpyarrays 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
truncated_normal(), and so on. TensorFlow also ...