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