def test_clone_changes_predictions(nH, nI):
model1 = Linear(nH)
model = clone(model1, 10)
ones = numpy.ones((10, nI), dtype="f")
model.initialize(X=ones)
output_from_cloned = model.predict(ones)
output_from_orig = model1.predict(ones)
assert output_from_cloned.sum() != output_from_orig.sum()
def test_predict_weights(X, expected):
W = numpy.asarray([1.0, 0.0, 0.0, 1.0], dtype="f").reshape((2, 2))
bias = numpy.asarray([0.0, 0.0], dtype="f")
model = Linear(W.shape[0], W.shape[1])
model.set_param("W", W)
model.set_param("b", bias)
scores = model.predict(X.reshape((1, -1)))
assert_allclose(scores.ravel(), expected)
def test_predict_extensive(W_b_input):
W, b, input_ = W_b_input
nr_out, nr_in = W.shape
model = Linear(nr_out, nr_in)
model.set_param("W", W)
model.set_param("b", b)
einsummed = numpy.einsum(
"bi,oi->bo",
numpy.asarray(input_, dtype="float32"),
numpy.asarray(W, dtype="float32"),
optimize=False,
)
expected_output = einsummed + b
predicted_output = model.predict(input_)
assert_allclose(predicted_output, expected_output, rtol=1e-04, atol=0.0001)
