How to implement one hot encoding without any ML frames in python?

Question

doing MNIST tutorial without any MLframe, but got stuck in one hot encoding stage

y contains label data of digits images

0, 1, 2, 3, 4, 5, 6, 7, 8, 9

and its size is (10000, )

I want to convert each category numbers to one hot encoding array

0 : 1 0 0 0 0 0 0 0 0 0 0
1 : 0 1 0 0 0 0 0 0 0 0 0
2 : 0 0 1 0 0 0 0 0 0 0 0 and so on

so I made a code

import numpy as np
y_one=np.zeros(y.size, 10)
y_one[np.arange(y.size), y]=1

it says 'data type not understood'

How to implement one hot encoding without sklearn or tf in this case?

could you add the rest of the error? it will help people trying to answer your question — PirateNinjas, Apr 29 '20 at 13:28

score 0 · Accepted Answer · answered Apr 29 '20 at 04:06

0

Pass tuple of ints for shape parameter of np.zeros function

y_one = np.zeros((y.size, 10))

answered Apr 29 '20 at 04:06

Dishin H Goyani

1 Answers1