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ba80248 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | from sklearn.metrics import f1_score
import numpy as np
#TODO: implement as nn.Module subclass
def f1(y_hat, y):
per_class_score = f1_score(y.cpu().detach().numpy().astype('int'),
(sigmoid(y_hat.cpu().detach().numpy()) > 0.5).astype('int'),
average='samples', zero_division=1)
return np.mean(per_class_score)
def accuracy(outputs, labels):
# y_hat = (sigmoid(outputs.cpu().detach().numpy()).flatten() > 0.5).astype('int')
# y = labels.cpu().detach().numpy().flatten().astype('int')
y_hat = (sigmoid(outputs.cpu().detach().numpy()) > 0.5).astype('int')
y = labels.cpu().detach().numpy().astype('int')
batch_size = y.shape[0]
per_class_acc = np.sum(y == y_hat, axis=0) / batch_size
# total = float(len(y))
# correct = float(np.sum(y == y_hat))
# return correct / total
return np.mean(per_class_acc)
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