def test_add_edge_cases():
data = numpy.asarray([[1, 2, 3, 4]], dtype="f")
with pytest.raises(TypeError):
add()
model = add(Linear(), Linear())
model._layers = []
Y, backprop = model(data, is_train=True)
assert numpy.array_equal(data, Y)
dX = backprop(Y)
assert numpy.array_equal(dX, data)
def test_add():
data = numpy.asarray([[1, 2, 3, 4]], dtype="f")
model = add(Linear(), Linear())
model.initialize(data, data)
Y, backprop = model(data, is_train=True)
Y2 = sum(layer.predict(data) for layer in model.layers)
assert numpy.array_equal(Y, Y2)
dX = backprop(Y)
assert dX.shape == data.shape
# Test that nesting works
model2 = add(model, Linear())
assert len(model2.layers) == 3
model.initialize(data, data)
Y = model2.predict(data)
Y2 = sum(layer.predict(data) for layer in model2.layers)
assert numpy.array_equal(Y, Y2)
from thinc.api import chain, ReLu, reduce_max, Softmax, add bad_model = chain(ReLu(10), reduce_max(), Softmax()) bad_model2 = add(ReLu(10), reduce_max(), Softmax())
from thinc.api import chain, Relu, reduce_max, Softmax, add good_model = chain(Relu(10), Relu(10), Softmax()) reveal_type(good_model) good_model2 = add(Relu(10), Relu(10), Softmax()) reveal_type(good_model2) bad_model_undetected = chain(Relu(10), Relu(10), reduce_max(), Softmax()) reveal_type(bad_model_undetected) bad_model_undetected2 = add(Relu(10), Relu(10), reduce_max(), Softmax()) reveal_type(bad_model_undetected2)
from thinc.api import chain, Relu, reduce_max, Softmax, add, concatenate
bad_model = chain(Relu(10), reduce_max(), Softmax())
bad_model2 = add(Relu(10), reduce_max(), Softmax())
bad_model_only_plugin = chain(Relu(10), Relu(10), Relu(10), Relu(10),
reduce_max(), Softmax())
bad_model_only_plugin2 = add(Relu(10), Relu(10), Relu(10), Relu(10),
reduce_max(), Softmax())
reveal_type(bad_model_only_plugin2)
bad_model_only_plugin3 = concatenate(Relu(10), Relu(10), Relu(10), Relu(10),
reduce_max(), Softmax())
reveal_type(bad_model_only_plugin3)
from typing import Any, TypeVar
from thinc.api import chain, ReLu, reduce_max, Softmax, add, Model
good_model = chain(ReLu(10), ReLu(10), Softmax())
reveal_type(good_model)
good_model2 = add(ReLu(10), ReLu(10), Softmax())
reveal_type(good_model2)
bad_model_undetected = chain(ReLu(10), ReLu(10), ReLu(10), ReLu(10), Softmax())
reveal_type(bad_model_undetected)
bad_model_undetected2 = add(ReLu(10), ReLu(10), ReLu(10), ReLu(10), Softmax())
reveal_type(bad_model_undetected2)
def forward() -> None:
pass
OtherType = TypeVar("OtherType")
def other_function(layer1: Model, layer2: Model,
*layers: Model) -> Model[Any, OtherType]:
return Model("some_model", forward)
non_combinator_model = other_function(Model("x", forward), Model("y", forward),
Model("z", forward))