Layering nested operations
In this recipe, we will learn how to put multiple operations on the same computational graph.
Getting ready
It's important to know how to chain operations together. This will set up layered operations in the computational graph. For a demonstration, we will multiply a placeholder by two matrices and then perform addition. We will feed in two matrices in the form of a three-dimensional NumPy array:
import tensorflow as tf sess = tf.Session()
How to do it...
It is also important to note how the data will change shape as it passes through. We will feed in two NumPy arrays of size 3 x 5. We will multiply each matrix by a constant of size 5 x 1, which will result in a matrix of size 3 x 1. We will then multiply this by a 1 x 1 matrix resulting in a 3 x 1 matrix again. Finally, we add a 3 x 1 matrix at the end, as follows:
- First, we create the data to feed in and the corresponding placeholder:
my_array = np.array([[1., 3., 5., 7., 9.],
[-2., 0., 2., 4...
