TensorFlow For Dummies
Book image
Explore Book Buy On Amazon
Machine learning applications store a great deal of data in vectors (one-dimensional tensors) and matrices (two-dimensional tensors). To process this data, TensorFlow provides many functions that operate on vectors and matrices. The following table lists these functions and provides a description of each.

Vector and Matrix Operations

Function Description
tensordot(a, b, axes, name=None) Returns the sum of products for the elements in the given axes
cross(a, b, name=None) Returns the element-wise cross product
diag(diagonal, name=None) Returns a matrix with the given diagonal values, other values set to zero
trace(x, name=None) Returns the sum of the diagonal elements
transpose(x, perm=None,


Switches rows and columns
eye(num_rows, num_columns=None,




Creates an identity matrix with the given shape and data type
matmul(a, b,








Returns the product of the two input matrices





Returns the norm of the given axis of the input tensor with the specified order
matrix_solve(A, b,



Returns the tensor x, such that Ax = b, where A is a matrix, and b is a vector
qr(input, full_matrices=None,


Returns the eigenvectors and eigenvalues of the given matrix or matrices




Factors the matrix into a unitary matrix, a diagonal matrix, and the conjugate transpose of the unitary matrix
einsum(equation, *inputs) Executes a custom mathematical operation
Of these functions, the two most common are tensordot and matmul. tensordot returns the dot product of one or more axes of two input tensors. That is, tensordot multiplies the corresponding elements of both tensors' dimensions and returns the sum of the products.

The axes parameter tells tensordot which dimensions to process. If you set this parameter to a scalar, N, the function will access the last N axes of the first tensor and the first N axes of the second tensor. If you set axes equal to a list or tensor, the first row identifies axes of the first tensor, and the second row identifies axes of the second tensor.

I frequently call tensordot to compute the dot product of two one-dimensional tensors. The following code shows what this looks like:

t1 = tf.constant([4., 3., 2.])

t2 = tf.constant([3., 2., 1.])

dot = tf.tensordot(t1, t2, 1)

<strong># 4*3 + 3*2 + 2*1 = 20</strong>

matmul performs traditional matrix multiplication. That is, it multiplies rows of the first tensor by columns of the second tensor and returns a matrix containing the sums. The following code shows how this can be used:
t1 = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])

t2 = tf.constant([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])

dot = tf.matmul(t1, t2)

<strong># [[ 22. 28.], [ 49. 64.]]</strong>

My favorite function in the table is einsum, which makes it possible to create and execute custom mathematical operations. The first parameter is a string that identifies the operation using a special format called the Einstein summation convention. This convention has a number of characteristics, including the following:
  • The operation is assumed to have one or two inputs. If you provide two inputs, you must separate them with a comma.
  • Dimensions of input and output matrices are represented by subscripts (usually i, j, and k). Input subscripts must be separated from output subscripts with the → symbol.
  • If an input's subscript is repeated and no output subscripts are given, the operation performs addition. Therefore, einsum('i,i', t1, t2) computes the dot product of tensors t1 and t2.
  • If an input's subscript is repeated and output subscripts are given, the operation performs multiplication. Therefore, einsum('i,i->i', t1, t2) computes the element-wise product of tensors t1 and t2.
The following code calls einsum to transpose a matrix and multiply two matrices together:
m1 = tf.constant([[1, 2], [3, 4]])

m2 = tf.constant([[5, 6], [7, 8]])

e1 = tf.einsum'ij-<ji', m1) # [[1, 3], [2, 4]]

e2 = tf.einsum('ij,jk-<ik', m1, m2) # [[19, 22], [43, 50]]

For a more complete discussion of the Einstein summation convention, try Samuel Prime's presentation.

About This Article

This article is from the book:

About the book author:

Matthew Scarpino has been a programmer and engineer for more than 20 years. He has worked extensively with machine learning applications, especially those involving financial analysis, cognitive modeling, and image recognition. Matthew is a Google Certified Data Engineer and blogs about TensorFlow at tfblog.com.

This article can be found in the category: