How to convert one-hot encodings into integers?

Question

I have a numpy array data set with shape (100,10). Each row is a one-hot encoding. I want to transfer it into a nd-array with shape (100,) such that I transferred each vector row into a integer that denote the index of the nonzero index. Is there a quick way of doing this using numpy or tensorflow?

Answer 1

You can use numpy.argmax or tf.argmax. Example:

import numpy as np   a  = np.array([[0,1,0,0],[1,0,0,0],[0,0,0,1]]) print('np.argmax(a, axis=1): {0}'.format(np.argmax(a, axis=1))) 

output:

np.argmax(a, axis=1): [1 0 3] 

You may also want to look at sklearn.preprocessing.LabelBinarizer.inverse_transform.