def test_crossentropy():
predictions = np.array([0.1, 0.5, 0.7, 0.4])
probabilities = softmax(predictions)
for i in range(len(predictions)):
ce = crossentropy(logits=predictions, label=i)
assert ce == approx(-np.log(probabilities[i]))
def test_softmax():
predictions = np.array([0.1, 0.5, 0.7, 0.4])
probabilities = softmax(predictions)
assert not np.sum(predictions) == approx(1.)
assert np.sum(probabilities) == approx(1.)
def score(Its):
Its = np.stack(Its)
Its = unnormalize(Its)
batch_logits, _ = a.batch_predictions(Its, strict=False)
scores = [softmax(logits)[cI] for logits in batch_logits]
return scores