def test_rnn():
for final_dense, gru, nn in [
(True, False,
rnn(10, (20, 30), normalise=True, gru=False, final_dense=True)),
(False, True,
rnn(10, (20, 30), normalise=True, gru=True, final_dense=False)),
]:
vs = Vars(np.float32)
nn.initialise(5, vs)
x = B.randn(2, 3, 5)
# Check number of weights and width.
assert B.length(vs.get_vector()) == nn.num_weights(5)
assert nn.width == 10
# Test batch consistency.
check_batch_consistency(nn, x)
# Check composition.
assert len(nn.layers) == 9 if final_dense else 7
assert type(nn.layers[0]) == Recurrent
assert type(nn.layers[0].cell) == GRU if gru else Elman
assert nn.layers[0].width == 20
assert type(nn.layers[1]) == Activation
assert nn.layers[1].width == 20
assert type(nn.layers[2]) == Normalise
assert nn.layers[2].width == 20
assert type(nn.layers[3]) == Recurrent
assert type(nn.layers[3].cell) == GRU if gru else Elman
assert nn.layers[3].width == 30
assert type(nn.layers[4]) == Activation
assert nn.layers[4].width == 30
assert type(nn.layers[5]) == Normalise
assert nn.layers[5].width == 30
if final_dense:
assert type(nn.layers[6]) == Linear
assert nn.layers[6].width == 10
assert type(nn.layers[7]) == Activation
assert nn.layers[7].width == 10
assert type(nn.layers[8]) == Linear
assert nn.layers[8].width == 10
else:
assert type(nn.layers[6]) == Linear
assert nn.layers[6].width == 10
# Check that normalisation layers disappear.
assert (len(
rnn(10, (20, 30), normalise=False, gru=True,
final_dense=False).layers) == 5)
def test_ff():
vs = Vars(np.float32)
nn = ff(10, (20, 30), normalise=True)
nn.initialise(5, vs)
x = B.randn(2, 3, 5)
# Check number of weights and width.
assert B.length(vs.get_vector()) == nn.num_weights(5)
assert nn.width == 10
# Test batch consistency.
check_batch_consistency(nn, x)
# Check composition.
assert len(nn.layers) == 7
assert type(nn.layers[0]) == Linear
assert nn.layers[0].A.shape[0] == 5
assert nn.layers[0].width == 20
assert type(nn.layers[1]) == Activation
assert nn.layers[1].width == 20
assert type(nn.layers[2]) == Normalise
assert nn.layers[2].width == 20
assert type(nn.layers[3]) == Linear
assert nn.layers[3].width == 30
assert type(nn.layers[4]) == Activation
assert nn.layers[4].width == 30
assert type(nn.layers[5]) == Normalise
assert nn.layers[5].width == 30
assert type(nn.layers[6]) == Linear
assert nn.layers[6].width == 10
# Check that one-dimensional calls are okay.
vs = Vars(np.float32)
nn.initialise(1, vs)
approx(nn(B.linspace(0, 1, 10)), nn(B.linspace(0, 1, 10)[:, None]))
# Check that zero-dimensional calls fail.
with pytest.raises(ValueError):
nn(0)
# Check normalisation layers disappear.
assert len(ff(10, (20, 30), normalise=False).layers) == 5
def test_vec_to_tril_and_back_correctness(offset, batch_shape, check_lazy_shapes):
n = B.length(B.tril_to_vec(B.ones(7, 7), offset=offset))
for vec in Tensor(*batch_shape, n).forms():
mat = B.vec_to_tril(vec, offset=offset)
approx(B.tril_to_vec(mat, offset=offset), vec)
def __init__(self, *objs):
self._shapes = [B.shape(obj) for obj in objs]
self._lengths = [B.length(obj) for obj in objs]
def test_vec_to_tril(offset, batch_shape, check_lazy_shapes):
n = B.length(B.tril_to_vec(B.ones(7, 7), offset=offset))
check_function(B.vec_to_tril, (Tensor(*batch_shape, n),), {"offset": Value(offset)})