| 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 |