101 lines
3.0 KiB
Python
101 lines
3.0 KiB
Python
import numpy
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import pytest
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from thinc.api import Optimizer, registry
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def _test_schedule_valid():
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while True:
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yield 0.456
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def _test_schedule_invalid():
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yield from []
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@pytest.fixture(
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params=[
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(lambda: 0.123, 0.123, 0.123, 0.123),
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(lambda: _test_schedule_valid(), 0.456, 0.456, 0.456),
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(lambda: (i for i in [0.2, 0.1, 0.4, 0.5, 0.6, 0.7, 0.8]), 0.2, 0.1, 0.4),
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(lambda: (i for i in [0.333, 0.666]), 0.333, 0.666, 0.666),
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(lambda: [0.9, 0.8, 0.7], 0.9, 0.8, 0.7),
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(lambda: [0.0, 0.123], 0.0, 0.123, 0.123),
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],
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scope="function",
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)
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def schedule_valid(request):
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# Use lambda to prevent iterator from being consumed by first test
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r_func, r1, r2, r3 = request.param
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return r_func(), r1, r2, r3
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@pytest.fixture(
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params=[
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(lambda: "hello"),
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(lambda: _test_schedule_invalid()),
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(lambda: (_ for _ in [])),
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(lambda: []),
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],
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scope="function",
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)
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def schedule_invalid(request):
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# Use lambda to prevent iterator from being consumed by first test
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r_func = request.param
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return r_func()
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@pytest.mark.parametrize("name", ["RAdam.v1", "Adam.v1", "SGD.v1"])
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def test_optimizers_from_config(name):
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learn_rate = 0.123
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cfg = {"@optimizers": name, "learn_rate": learn_rate}
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optimizer = registry.resolve({"config": cfg})["config"]
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assert optimizer.learn_rate == learn_rate
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def test_optimizer_schedules_from_config(schedule_valid):
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lr, lr_next1, lr_next2, lr_next3 = schedule_valid
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cfg = {"@optimizers": "Adam.v1", "learn_rate": lr}
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optimizer = registry.resolve({"cfg": cfg})["cfg"]
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assert optimizer.learn_rate == lr_next1
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optimizer.step_schedules()
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assert optimizer.learn_rate == lr_next2
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optimizer.step_schedules()
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assert optimizer.learn_rate == lr_next3
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optimizer.learn_rate = 1.0
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assert optimizer.learn_rate == 1.0
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def test_optimizer_schedules_valid(schedule_valid):
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lr, lr_next1, lr_next2, lr_next3 = schedule_valid
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optimizer = Optimizer(learn_rate=lr)
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assert optimizer.learn_rate == lr_next1
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optimizer.step_schedules()
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assert optimizer.learn_rate == lr_next2
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optimizer.step_schedules()
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assert optimizer.learn_rate == lr_next3
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optimizer.learn_rate = 1.0
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assert optimizer.learn_rate == 1.0
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def test_optimizer_schedules_invalid(schedule_invalid):
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with pytest.raises(ValueError):
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Optimizer(learn_rate=schedule_invalid)
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def test_optimizer_init():
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optimizer = Optimizer(
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learn_rate=0.123,
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use_averages=False,
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use_radam=True,
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L2=0.1,
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L2_is_weight_decay=False,
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)
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_, gradient = optimizer((0, "x"), numpy.zeros((1, 2)), numpy.zeros(0))
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assert numpy.array_equal(gradient, numpy.zeros(0))
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W = numpy.asarray([1.0, 0.0, 0.0, 1.0], dtype="f").reshape((4,))
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dW = numpy.asarray([[-1.0, 0.0, 0.0, 1.0]], dtype="f").reshape((4,))
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optimizer((0, "x"), W, dW)
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optimizer = Optimizer(learn_rate=0.123, beta1=0.1, beta2=0.1)
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optimizer((1, "x"), W, dW)
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