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Actually, np.argmax returns indexes of maximum values along some axis. On the other side, np.amax returns maximum values along some axis but if you want to get indexes of maximum values with those maximum values it is necessary to go through the array again. For example:
import numpy as np
data = np.array([[0.02079073, 0.97920927], [0.00487725, 0.99512275], [0.00849596, 0.99150404], [0.96402552, 0.03597448], [0.00711506, 0.99288494]])
indexes = np.argmax(data, axis=-1)
maximum_values = data[np.arange(data.shape[0]), indexes]
print(indexes)
print(maximum_values)
print(np.amax(data, axis=-1))
>> [1 1 1 0 1]
>> [0.97920927 0.99512275 0.99150404 0.96402552 0.99288494]
>> [0.97920927 0.99512275 0.99150404 0.96402552 0.99288494]Even with np.arange, go through the array again consumes time (specially on huge arrays). So, np.argmax and np.argmin could return indexes and values at the same time on one look (maybe with an extra parameter in order to make compatible previous versions). For 1D just indexing would be fine, but that not work for arrays with 2 or more axis because it gets the rows elements:
import numpy as np
data = np.array([0.3, 0.7, 0.5])
indexes = np.argmax(data, axis=-1)
print(data[indexes])
>> 0.7import numpy as np
data = np.array([[0.02079073, 0.97920927], [0.00487725, 0.99512275], [0.00849596, 0.99150404], [0.96402552, 0.03597448], [0.00711506, 0.99288494]])
indexes = np.argmax(data, axis=-1)
print(data[indexes])
>> [[0.00487725 0.99512275]
[0.00487725 0.99512275]
[0.00487725 0.99512275]
[0.02079073 0.97920927]
[0.00487725 0.99512275]]Numpy version: '1.17.5'
Python version: '3.6.9'
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