TensorFlow Machine Learning Cookbook - Second Edition

How to do it...

We will proceed with the recipe as follows:

1. Creating matrices: We can create two-dimensional matrices from NumPy arrays or nested lists, as we described in the Creating and using tensors recipe. 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 allows us to create a diagonal matrix from a one-dimensional array or list with the diag() function, as follows:

identity_matrix = tf.diag([1.0, 1.0, 1.0]) 
A = tf.truncated_normal([2, 3]) 
B = tf.fill([2,3], 5.0) 
C = tf.random_uniform([3,2]) 
D = tf.convert_to_tensor(np.array([[1., 2., 3.],[-3., -7., -1.],[0., 5., -2.]])) 
print(sess.run(identity_matrix)) 
[[ 1. 0. 0.] ...