def from_params(cls, vocab: Vocabulary, params: Params) -> 'SentenceClassifier':
embedder_params = params.pop("text_field_embedder")
text_field_embedder = TextFieldEmbedder.from_params(vocab, embedder_params)
question_encoder = Seq2VecEncoder.from_params(params.pop("question_encoder"))
initializer = InitializerApplicator.from_params(params.pop('initializer', []))
regularizer = RegularizerApplicator.from_params(params.pop('regularizer', []))
return cls(vocab=vocab,
text_field_embedder=text_field_embedder,
question_encoder=question_encoder,
initializer=initializer,
regularizer=regularizer)
def test_from_params(self):
params = Params({"regularizers": [("conv", "l1"), ("linear", {"type": "l2", "alpha": 10})]})
regularizer_applicator = RegularizerApplicator.from_params(params.pop("regularizers"))
regularizers = regularizer_applicator._regularizers # pylint: disable=protected-access
conv = linear = None
for regex, regularizer in regularizers:
if regex == "conv":
conv = regularizer
elif regex == "linear":
linear = regularizer
assert isinstance(conv, L1Regularizer)
assert isinstance(linear, L2Regularizer)
assert linear.alpha == 10
def from_params(cls, vocab: Vocabulary, params: Params) -> 'ToxicModel':
embedder_params = params.pop("text_field_embedder")
text_field_embedder = TextFieldEmbedder.from_params(vocab, embedder_params)
encoder = Seq2VecEncoder.from_params(params.pop("encoder"))
classifier_feedforward = FeedForward.from_params(params.pop("classifier_feedforward"))
initializer = InitializerApplicator.from_params(params.pop('initializer', []))
regularizer = RegularizerApplicator.from_params(params.pop('regularizer', []))
return cls(vocab=vocab,
text_field_embedder=text_field_embedder,
encoder=encoder,
classifier_feedforward=classifier_feedforward,
initializer=initializer,
regularizer=regularizer)
