def test_weighted_negative_log_likelihood_vs_softmax_cross_entropy(
data: st.DataObject, labels_as_tensor: bool):
s = data.draw(
hnp.arrays(
shape=hnp.array_shapes(min_side=1,
max_side=10,
min_dims=2,
max_dims=2),
dtype=float,
elements=st.floats(-100, 100),
))
y_true = data.draw(
hnp.arrays(
shape=(s.shape[0], ),
dtype=hnp.integer_dtypes(),
elements=st.integers(min_value=0, max_value=s.shape[1] - 1),
).map(Tensor if labels_as_tensor else lambda x: x))
weights = data.draw(
hnp.arrays(
shape=(s.shape[1], ),
dtype=float,
elements=st.floats(1e-8, 100),
))
scores = Tensor(s)
weights = Tensor(weights)
for score, y in zip(scores, y_true):
score = mg.log(mg.nnet.softmax(score.reshape(1, -1)))
y = y.reshape(-1)
nll = negative_log_likelihood(score, y)
weighted_nll = negative_log_likelihood(score, y, weights=weights)
assert np.isclose(weighted_nll.data, weights[y.data].data * nll.data)
def test_negative_log_likelihood_vs_softmax_cross_entropy(
data: st.DataObject, labels_as_tensor: bool):
s = data.draw(
hnp.arrays(
shape=hnp.array_shapes(max_side=10, min_dims=2, max_dims=2),
dtype=float,
elements=st.floats(-100, 100),
))
y_true = data.draw(
hnp.arrays(
shape=(s.shape[0], ),
dtype=hnp.integer_dtypes(),
elements=st.integers(min_value=0, max_value=s.shape[1] - 1),
).map(Tensor if labels_as_tensor else lambda x: x))
scores = Tensor(s)
nll = negative_log_likelihood(mg.log(mg.nnet.softmax(scores)), y_true)
nll.backward()
cross_entropy_scores = Tensor(s)
ce = softmax_crossentropy(cross_entropy_scores, y_true)
ce.backward()
assert_allclose(nll.data, ce.data, atol=1e-5, rtol=1e-5)
assert_allclose(scores.grad,
cross_entropy_scores.grad,
atol=1e-5,
rtol=1e-5)
def test_input_validation(data, labels, weights):
with raises((ValueError, TypeError)):
negative_log_likelihood(data, labels, weights=weights)