def build_mean_max_reducer(hidden_size: int) -> Model[Ragged, Floats2d]:
"""Reduce sequences by concatenating their mean and max pooled vectors,
and then combine the concatenated vectors with a hidden layer.
"""
return chain(
concatenate(reduce_last(), reduce_first(), reduce_mean(),
reduce_max()),
Maxout(nO=hidden_size, normalize=True, dropout=0.0),
)
def test_reduce_first(Xs):
model = reduce_first()
lengths = model.ops.asarray([x.shape[0] for x in Xs], dtype="i")
X = Ragged(model.ops.flatten(Xs), lengths)
Y, backprop = model(X, is_train=True)
assert isinstance(Y, numpy.ndarray)
assert Y.shape == (len(Xs), Xs[0].shape[1])
assert Y.dtype == Xs[0].dtype
assert list(Y[0]) == list(Xs[0][0])
assert list(Y[1]) == list(Xs[1][0])
dX = backprop(Y)
assert dX.dataXd.shape == X.dataXd.shape
def test_init_reduce_first():
model = reduce_first()