80 lines
2.2 KiB
Python
80 lines
2.2 KiB
Python
import math
|
|
|
|
import numpy
|
|
import pytest
|
|
|
|
from thinc.api import SGD, SparseLinear, SparseLinear_v2, to_categorical
|
|
|
|
|
|
@pytest.fixture
|
|
def instances():
|
|
lengths = numpy.asarray([5, 4], dtype="int32")
|
|
keys = numpy.arange(9, dtype="uint64")
|
|
values = numpy.ones(9, dtype="float32")
|
|
X = (keys, values, lengths)
|
|
y = numpy.asarray([0, 2], dtype="int32")
|
|
return X, to_categorical(y, n_classes=3)
|
|
|
|
|
|
@pytest.fixture
|
|
def sgd():
|
|
return SGD(0.001)
|
|
|
|
|
|
def test_basic(instances, sgd):
|
|
X, y = instances
|
|
nr_class = 3
|
|
model = SparseLinear(nr_class).initialize()
|
|
yh, backprop = model.begin_update(X)
|
|
loss1 = ((yh - y) ** 2).sum()
|
|
backprop(yh - y)
|
|
model.finish_update(sgd)
|
|
yh, backprop = model.begin_update(X)
|
|
loss2 = ((yh - y) ** 2).sum()
|
|
assert loss2 < loss1
|
|
|
|
|
|
def test_init():
|
|
model = SparseLinear(3).initialize()
|
|
keys = numpy.ones((5,), dtype="uint64")
|
|
values = numpy.ones((5,), dtype="f")
|
|
lengths = numpy.zeros((2,), dtype="int32")
|
|
lengths[0] = 3
|
|
lengths[1] = 2
|
|
scores, backprop = model.begin_update((keys, values, lengths))
|
|
assert scores.shape == (2, 3)
|
|
d_feats = backprop(scores)
|
|
assert len(d_feats) == 3
|
|
|
|
|
|
def test_distribution():
|
|
n_class = 10
|
|
length = 2**18
|
|
model = SparseLinear_v2(nO=n_class, length=length).initialize()
|
|
|
|
ii64 = numpy.iinfo(numpy.uint64)
|
|
lengths = numpy.zeros((2,), dtype="int32")
|
|
|
|
for p_nonzero in range(1, 12):
|
|
# Clear gradients from the previous iterarion.
|
|
model.set_grad("W", 0.0)
|
|
|
|
n = 2**p_nonzero
|
|
keys = numpy.random.randint(ii64.min, ii64.max, size=(n,), dtype=numpy.uint64)
|
|
values = numpy.ones((n,), dtype="f")
|
|
lengths[0] = n // 2
|
|
lengths[1] = n // 2
|
|
|
|
# Probability that a bit is set (2 because we use 2 hashes).
|
|
p_nonzero = 1 - math.exp(-2 * n / length)
|
|
|
|
Y, backprop = model.begin_update((keys, values, lengths))
|
|
backprop(numpy.ones_like(Y))
|
|
|
|
# Check that for each class we have the expected rate of non-zeros.
|
|
dW = model.get_grad("W").reshape(n_class, -1)
|
|
nonzero_empirical = numpy.count_nonzero(dW, axis=1) / dW.shape[1]
|
|
numpy.testing.assert_allclose(
|
|
nonzero_empirical, p_nonzero, rtol=1e-4, atol=1e-4
|
|
)
|