Preparing data for tensorflow is often easiest when done as a two step process. In machine learning, you often get into trying to plot points, calculate tangents, and a lot of basic algebra. Working out equations kinda’ reminds me of being in in-school suspension in high school. Except now we’re writing code to solve the problems rather than solving them ourselves.
I never liked solving for a matrix… But NumPy is a great little framework to import that does a lot of N-dimensional array work. A few basic tasks in the following script includes a number of functions across norms, matrix products, vector products, decompose, and eigenvalues. Remove/comment what you don’t need:
import numpy as np from numpy import linalg as LA array = [[-1,4,2],[1,-1,3]] array2 = [[6,2,1],[7,1,-3]] array = np.asarray(array) converted = np.fliplr(array) #ifsquare >> cholesky = LA.cholesky(array) #ifsquare >> inv = LA.inv(array) #ifsquare >> determinant = LA.det(array) #ifsquare >> signlog = LA.slogdet(array) print print 'ONE ARRAY' print 'Sum: ', np.trace(converted) print 'Elements: ', np.diagonal(converted) print 'Solved: ', LA.norm(array) print 'qr factorization: ' print np.linalg.qr(array) print 'Hermitian: ' print LA.svd(array) print print 'TWO ARRAYS' print 'vdot: ',np.vdot(array,array2) print 'Inner: ' print np.inner(array,array2) print 'Outer: ' print np.outer(array,array2) print 'Tensor dot product: ',np.tensordot(array,array2) print 'Kronecker product: ' print np.kron(array,array2)