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 allows us to create a diagonal matrix from a one-dimensional array or list with the function diag(), 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.]
 [ 0.  1.  0.]
 [ 0.  0.  1.]]
print(sess.run(A))
[[ 0.96751703  0.11397751 -0.3438891 ]
 [-0.10132604 -0.8432678   0.29810596]]
print(sess.run(B))
[[ 5.  5.  5.]
 [ 5.  5.  5.]]
print(sess.run(C))
[[ 0.33184157  0.08907614]
 [ 0.53189191  0.67605299]
 [ 0.95889051  0.67061249]]
print(sess.run(D))
[[ 1.  2.  3.]
 [-3. -7. -1.]
 [ 0.  5. -2.]]

    Note

    Note that if we were to run sess.run(C) again, we would reinitialize the random variables and end up with different random values.

  2. Addition and subtraction uses the following function: 
    print(sess.run(A+B))
[[ 4.61596632  5.39771316  4.4325695 ]
 [ 3.26702736  5.14477345  4.98265553]]
print(sess.run(B-B))
[[ 0.  0.  0.]
 [ 0.  0.  0.]]
Multiplication
print(sess.run(tf.matmul(B, identity_matrix)))
[[ 5.  5.  5.]
 [ 5.  5.  5.]]
  3. Also, the function matmul() has arguments that specify whether or not to transpose the arguments before multiplication or whether each matrix is sparse.
  4. Transpose the arguments as follows: 
    print(sess.run(tf.transpose(C)))
[[ 0.67124544  0.26766731  0.99068872]
 [ 0.25006068  0.86560275  0.58411312]]
  5. Again, it is worth mentioning the reinitializing that gives us different values than before.
  6. For the determinant, use the following: 
    print(sess.run(tf.matrix_determinant(D)))
-38.0
    • Inverse: 
      print(sess.run(tf.matrix_inverse(D)))
[[-0.5        -0.5        -0.5       ]
 [ 0.15789474  0.05263158  0.21052632]
 [ 0.39473684  0.13157895  0.02631579]]

    Note

    Note that the inverse method is based on the Cholesky decomposition if the matrix is symmetric positive definite or the LU decomposition otherwise.

  7. Decompositions:
    • For the Cholesky decomposition, use the following: 
      print(sess.run(tf.cholesky(identity_matrix)))
[[ 1.  0.  1.]
 [ 0.  1.  0.]
 [ 0.  0.  1.]]
  8. For Eigenvalues and eigenvectors, use the following code: 
    print(sess.run(tf.self_adjoint_eig(D))
[[-10.65907521  -0.22750691   2.88658212]
 [  0.21749542   0.63250104  -0.74339638]
 [  0.84526515   0.2587998    0.46749277]
 [ -0.4880805    0.73004459   0.47834331]]

Note that the function self_adjoint_eig() outputs the eigenvalues in the first row and the subsequent vectors in the remaining vectors. In mathematics, this is known as the Eigen decomposition of a matrix.

How it works…

TensorFlow provides all the tools for us to get started with numerical computations and adding such computations to our graphs. This notation might seem quite heavy for simple matrix operations. Remember that we are adding these operations to the graph and telling TensorFlow what tensors to run through those operations. While this might seem verbose now, it helps to understand the notations in later chapters, when this way of computation will make it easier to accomplish our goals.

上一章目录下一章