def test_forward_gives_correct_output(self):
params = Params({
'input_dim': 2,
'output_dims': 3,
'pool_sizes': 4,
'dropout': 0.0,
'num_layers': 2
})
maxout = Maxout.from_params(params)
constant_init = Initializer.from_params(
Params({
"type": "constant",
"val": 1.
}))
initializer = InitializerApplicator([(".*", constant_init)])
initializer(maxout)
input_tensor = torch.FloatTensor([[-3, 1]])
output = maxout(input_tensor).data.numpy()
assert output.shape == (1, 3)
# This output was checked by hand
# The output of the first maxout layer is [-1, -1, -1], since the
# matrix multiply gives us [-2]*12. Reshaping and maxing
# produces [-2, -2, -2] and the bias increments these values.
# The second layer output is [-2, -2, -2], since the matrix
# matrix multiply gives us [-3]*12. Reshaping and maxing
# produces [-3, -3, -3] and the bias increments these values.
assert_almost_equal(output, [[-2, -2, -2]])
def from_params(cls, vocab: Vocabulary, params: Params) -> 'BiattentiveClassificationNetwork':
embedder_params = params.pop("text_field_embedder")
text_field_embedder = TextFieldEmbedder.from_params(vocab, embedder_params)
embedding_dropout = params.pop("embedding_dropout")
pre_encode_feedforward = FeedForward.from_params(params.pop("pre_encode_feedforward"))
encoder = Seq2SeqEncoder.from_params(params.pop("encoder"))
integrator = Seq2SeqEncoder.from_params(params.pop("integrator"))
integrator_dropout = params.pop("integrator_dropout")
output_layer_params = params.pop("output_layer")
if "activations" in output_layer_params:
output_layer = FeedForward.from_params(output_layer_params)
else:
output_layer = Maxout.from_params(output_layer_params)
initializer = InitializerApplicator.from_params(params.pop('initializer', []))
regularizer = RegularizerApplicator.from_params(params.pop('regularizer', []))
return cls(vocab=vocab,
text_field_embedder=text_field_embedder,
embedding_dropout=embedding_dropout,
pre_encode_feedforward=pre_encode_feedforward,
encoder=encoder,
integrator=integrator,
integrator_dropout=integrator_dropout,
output_layer=output_layer,
initializer=initializer,
regularizer=regularizer)
def test_forward_gives_correct_output(self):
params = Params({
'input_dim': 2,
'output_dims': 3,
'pool_sizes': 4,
'dropout': 0.0,
'num_layers': 2
})
maxout = Maxout.from_params(params)
constant_init = Initializer.from_params(Params({"type": "constant", "val": 1.}))
initializer = InitializerApplicator([(".*", constant_init)])
initializer(maxout)
input_tensor = torch.FloatTensor([[-3, 1]])
output = maxout(input_tensor).data.numpy()
assert output.shape == (1, 3)
# This output was checked by hand
# The output of the first maxout layer is [-1, -1, -1], since the
# matrix multiply gives us [-2]*12. Reshaping and maxing
# produces [-2, -2, -2] and the bias increments these values.
# The second layer output is [-2, -2, -2], since the matrix
# matrix multiply gives us [-3]*12. Reshaping and maxing
# produces [-3, -3, -3] and the bias increments these values.
assert_almost_equal(output, [[-2, -2, -2]])
def from_params( # type: ignore
cls, vocab: Vocabulary,
params: Params) -> "BiattentiveClassificationNetwork":
embedder_params = params.pop("text_field_embedder")
text_field_embedder = TextFieldEmbedder.from_params(
vocab=vocab, params=embedder_params)
embedding_dropout = params.pop("embedding_dropout")
pre_encode_feedforward = FeedForward.from_params(
params.pop("pre_encode_feedforward"))
encoder = Seq2SeqEncoder.from_params(params.pop("encoder"))
integrator = Seq2SeqEncoder.from_params(params.pop("integrator"))
integrator_dropout = params.pop("integrator_dropout")
output_layer_params = params.pop("output_layer")
if "activations" in output_layer_params:
output_layer = FeedForward.from_params(output_layer_params)
else:
output_layer = Maxout.from_params(output_layer_params)
elmo = params.pop("elmo", None)
if elmo is not None:
elmo = Elmo.from_params(elmo)
use_input_elmo = params.pop_bool("use_input_elmo", False)
use_integrator_output_elmo = params.pop_bool(
"use_integrator_output_elmo", False)
initializer = InitializerApplicator.from_params(
params.pop("initializer", []))
regularizer = RegularizerApplicator.from_params(
params.pop("regularizer", []))
params.assert_empty(cls.__name__)
return cls(
vocab=vocab,
text_field_embedder=text_field_embedder,
embedding_dropout=embedding_dropout,
pre_encode_feedforward=pre_encode_feedforward,
encoder=encoder,
integrator=integrator,
integrator_dropout=integrator_dropout,
output_layer=output_layer,
elmo=elmo,
use_input_elmo=use_input_elmo,
use_integrator_output_elmo=use_integrator_output_elmo,
initializer=initializer,
regularizer=regularizer,
)
def from_params(
cls, vocab: Vocabulary,
params: Params) -> 'SemEvalClassifierAttention': # type: ignore
# pylint: disable=arguments-differ
embedder_params = params.pop("text_field_embedder")
text_field_embedder = TextFieldEmbedder.from_params(
vocab=vocab, params=embedder_params)
embedding_dropout = params.pop("embedding_dropout")
encoder = Seq2SeqEncoder.from_params(params.pop("encoder"))
integrator = Seq2SeqEncoder.from_params(params.pop("integrator"))
integrator_dropout = params.pop("integrator_dropout")
output_layer_params = params.pop("output_layer")
if "activations" in output_layer_params:
output_layer = FeedForward.from_params(output_layer_params)
else:
output_layer = Maxout.from_params(output_layer_params)
elmo = params.pop("elmo", None)
if elmo is not None:
elmo = Elmo.from_params(elmo)
use_input_elmo = params.pop_bool("use_input_elmo", False)
use_integrator_output_elmo = params.pop_bool(
"use_integrator_output_elmo", False)
initializer = InitializerApplicator.from_params(
params.pop('initializer', []))
regularizer = RegularizerApplicator.from_params(
params.pop('regularizer', []))
params.assert_empty(cls.__name__)
return cls(vocab=vocab,
text_field_embedder=text_field_embedder,
embedding_dropout=embedding_dropout,
encoder=encoder,
integrator=integrator,
integrator_dropout=integrator_dropout,
output_layer=output_layer,
initializer=initializer,
regularizer=regularizer)
def from_params(cls, vocab: Vocabulary, params: Params) -> 'BiattentiveClassificationNetwork': # type: ignore
# pylint: disable=arguments-differ
embedder_params = params.pop("text_field_embedder")
text_field_embedder = TextFieldEmbedder.from_params(vocab=vocab, params=embedder_params)
embedding_dropout = params.pop("embedding_dropout")
pre_encode_feedforward = FeedForward.from_params(params.pop("pre_encode_feedforward"))
encoder = Seq2SeqEncoder.from_params(params.pop("encoder"))
integrator = Seq2SeqEncoder.from_params(params.pop("integrator"))
integrator_dropout = params.pop("integrator_dropout")
output_layer_params = params.pop("output_layer")
if "activations" in output_layer_params:
output_layer = FeedForward.from_params(output_layer_params)
else:
output_layer = Maxout.from_params(output_layer_params)
elmo = params.pop("elmo", None)
if elmo is not None:
elmo = Elmo.from_params(elmo)
use_input_elmo = params.pop_bool("use_input_elmo", False)
use_integrator_output_elmo = params.pop_bool("use_integrator_output_elmo", False)
initializer = InitializerApplicator.from_params(params.pop('initializer', []))
regularizer = RegularizerApplicator.from_params(params.pop('regularizer', []))
params.assert_empty(cls.__name__)
return cls(vocab=vocab,
text_field_embedder=text_field_embedder,
embedding_dropout=embedding_dropout,
pre_encode_feedforward=pre_encode_feedforward,
encoder=encoder,
integrator=integrator,
integrator_dropout=integrator_dropout,
output_layer=output_layer,
elmo=elmo,
use_input_elmo=use_input_elmo,
use_integrator_output_elmo=use_integrator_output_elmo,
initializer=initializer,
regularizer=regularizer)
def from_params(cls, vocab , params ) : # type: ignore
# pylint: disable=arguments-differ
embedder_params = params.pop(u"text_field_embedder")
text_field_embedder = TextFieldEmbedder.from_params(vocab=vocab, params=embedder_params)
embedding_dropout = params.pop(u"embedding_dropout")
pre_encode_feedforward = FeedForward.from_params(params.pop(u"pre_encode_feedforward"))
encoder = Seq2SeqEncoder.from_params(params.pop(u"encoder"))
integrator = Seq2SeqEncoder.from_params(params.pop(u"integrator"))
integrator_dropout = params.pop(u"integrator_dropout")
output_layer_params = params.pop(u"output_layer")
if u"activations" in output_layer_params:
output_layer = FeedForward.from_params(output_layer_params)
else:
output_layer = Maxout.from_params(output_layer_params)
elmo = params.pop(u"elmo", None)
if elmo is not None:
elmo = Elmo.from_params(elmo)
use_input_elmo = params.pop_bool(u"use_input_elmo", False)
use_integrator_output_elmo = params.pop_bool(u"use_integrator_output_elmo", False)
initializer = InitializerApplicator.from_params(params.pop(u'initializer', []))
regularizer = RegularizerApplicator.from_params(params.pop(u'regularizer', []))
params.assert_empty(cls.__name__)
return cls(vocab=vocab,
text_field_embedder=text_field_embedder,
embedding_dropout=embedding_dropout,
pre_encode_feedforward=pre_encode_feedforward,
encoder=encoder,
integrator=integrator,
integrator_dropout=integrator_dropout,
output_layer=output_layer,
elmo=elmo,
use_input_elmo=use_input_elmo,
use_integrator_output_elmo=use_integrator_output_elmo,
initializer=initializer,
regularizer=regularizer)