mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-13 21:26:58 +03:00
46 lines
1.9 KiB
Python
46 lines
1.9 KiB
Python
from thinc.api import Model, chain, reduce_mean, Linear, list2ragged, Logistic
|
|
from thinc.api import SparseLinear, Softmax
|
|
|
|
from ...attrs import ORTH
|
|
from ...util import registry
|
|
from ..extract_ngrams import extract_ngrams
|
|
|
|
|
|
@registry.architectures.register("spacy.TextCatCNN.v1")
|
|
def build_simple_cnn_text_classifier(tok2vec, exclusive_classes, nO=None):
|
|
"""
|
|
Build a simple CNN text classifier, given a token-to-vector model as inputs.
|
|
If exclusive_classes=True, a softmax non-linearity is applied, so that the
|
|
outputs sum to 1. If exclusive_classes=False, a logistic non-linearity
|
|
is applied instead, so that outputs are in the range [0, 1].
|
|
"""
|
|
with Model.define_operators({">>": chain}):
|
|
if exclusive_classes:
|
|
output_layer = Softmax(nO=nO, nI=tok2vec.get_dim("nO"))
|
|
model = tok2vec >> list2ragged() >> reduce_mean() >> output_layer
|
|
model.set_ref("output_layer", output_layer)
|
|
else:
|
|
# TODO: experiment with init_w=zero_init
|
|
linear_layer = Linear(nO=nO, nI=tok2vec.get_dim("nO"))
|
|
model = (
|
|
tok2vec >> list2ragged() >> reduce_mean() >> linear_layer >> Logistic()
|
|
)
|
|
model.set_ref("output_layer", linear_layer)
|
|
model.set_ref("tok2vec", tok2vec)
|
|
model.set_dim("nO", nO)
|
|
return model
|
|
|
|
|
|
@registry.architectures.register("spacy.TextCatBOW.v1")
|
|
def build_bow_text_classifier(exclusive_classes, ngram_size, no_output_layer, nO=None):
|
|
# Note: original defaults were ngram_size=1 and no_output_layer=False
|
|
with Model.define_operators({">>": chain}):
|
|
model = extract_ngrams(ngram_size, attr=ORTH) >> SparseLinear(nO)
|
|
model.to_cpu()
|
|
if not no_output_layer:
|
|
output_layer = Softmax(nO) if exclusive_classes else Logistic(nO)
|
|
output_layer.to_cpu()
|
|
model = model >> output_layer
|
|
model.set_ref("output_layer", output_layer)
|
|
return model
|