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pythonnumpysoftmax
user42493
user42493

Reputation: 1113

Compute softmax activation function using python

I was trying to write a method to compute the SoftMax activation function that takes either a matrix or an array as input and apply the softmax function to each rows.

Here is what I tried:

import numpy as np
def softmaxSingle(x):
    e_x = np.exp(x - np.max(x))
    return e_x / e_x.sum()    

def softmax( x):
    if np.shape(x)[0]>1:
        result=[[]]*np.shape(x)[0]
        for i in range(len(result)):
            result[i]=list(softmaxSingle(x[i]))
        return list(result)
    e_x = np.exp(x - np.max(x))
    return e_x / e_x.sum()

When I tried SoftMax(x) where x is a matrix, It runs(although I don't know if it produces correct answer). When x is just a list, it doesn't work

Upvotes: 0

Views: 241

Answers (1)

pissall
pissall

Reputation: 7419

You can simply make a list to np.array conversion:

import numpy as np

def softmax(x):
    """Compute softmax values for each sets of scores in x."""
    if isinstance(x, list):
        x = np.array(x)
    e_x = np.exp(x - np.max(x))
    return e_x / e_x.sum()

Upvotes: 1

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