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e4a6421 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | import numpy as np
def softmax(x):
max = np.max(
x, axis=1, keepdims=True
) # returns max of each row and keeps same dims
e_x = np.exp(x - max) # subtracts each row with its max value
sum = np.sum(
e_x, axis=1, keepdims=True
) # returns sum of each row and keeps same dims
f_x = e_x / sum
return f_x
def calc_acc(outputs_eval, label_eval):
if len(label_eval.shape) == 2:
label_eval = np.argmax(label_eval, 1)
acc = np.array(np.argmax(outputs_eval, 1) == label_eval).mean()
return acc
def to_onehot(arr, n_class):
return np.eye(n_class)[arr]
def label2onehot(label, n_class=None):
if n_class is None:
n_class = int(label.max() + 1)
if len(label.shape) == 1:
label = to_onehot(label, n_class)
if len(label.shape) == 2 and label.shape[1] == 1:
label = to_onehot(label[:, 0], n_class)
return label |