45 lines
1.4 KiB
Python
45 lines
1.4 KiB
Python
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import pytest
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from thinc.api import (
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ParametricAttention,
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Relu,
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Softmax,
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chain,
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list2ragged,
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reduce_max,
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reduce_sum,
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with_ragged,
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)
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from thinc.util import DataValidationError, data_validation
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def test_validation():
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model = chain(Relu(10), Relu(10), with_ragged(reduce_max()), Softmax())
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with data_validation(True):
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with pytest.raises(DataValidationError):
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model.initialize(X=model.ops.alloc2f(1, 10), Y=model.ops.alloc2f(1, 10))
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with pytest.raises(DataValidationError):
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model.initialize(X=model.ops.alloc3f(1, 10, 1), Y=model.ops.alloc2f(1, 10))
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with pytest.raises(DataValidationError):
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model.initialize(X=[model.ops.alloc2f(1, 10)], Y=model.ops.alloc2f(1, 10))
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def test_validation_complex():
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good_model = chain(list2ragged(), reduce_sum(), Relu(12, dropout=0.5), Relu(1))
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X = [good_model.ops.xp.zeros((4, 75), dtype="f")]
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Y = good_model.ops.xp.zeros((1,), dtype="f")
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good_model.initialize(X, Y)
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good_model.predict(X)
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bad_model = chain(
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list2ragged(),
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reduce_sum(),
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Relu(12, dropout=0.5),
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# ERROR: Why can't I attach a Relu to an attention layer?
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ParametricAttention(12),
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Relu(1),
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)
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with data_validation(True):
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with pytest.raises(DataValidationError):
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bad_model.initialize(X, Y)
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