mirror of
https://github.com/explosion/spaCy.git
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ca491722ad
* moving syntax folder to _parser_internals * moving nn_parser and transition_system * move nn_parser and transition_system out of internals folder * moving nn_parser code into transition_system file * rename transition_system to transition_parser * moving parser_model and _state to ml * move _state back to internals * The Parser now inherits from Pipe! * small code fixes * removing unnecessary imports * remove link_vectors_to_models * transition_system to internals folder * little bit more cleanup * newlines
88 lines
2.8 KiB
Python
88 lines
2.8 KiB
Python
from thinc.api import Model, noop, use_ops, Linear
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from .parser_model import ParserStepModel
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def TransitionModel(tok2vec, lower, upper, dropout=0.2, unseen_classes=set()):
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"""Set up a stepwise transition-based model"""
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if upper is None:
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has_upper = False
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upper = noop()
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else:
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has_upper = True
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# don't define nO for this object, because we can't dynamically change it
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return Model(
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name="parser_model",
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forward=forward,
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dims={"nI": tok2vec.get_dim("nI") if tok2vec.has_dim("nI") else None},
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layers=[tok2vec, lower, upper],
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refs={"tok2vec": tok2vec, "lower": lower, "upper": upper},
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init=init,
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attrs={
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"has_upper": has_upper,
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"unseen_classes": set(unseen_classes),
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"resize_output": resize_output,
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},
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)
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def forward(model, X, is_train):
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step_model = ParserStepModel(
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X,
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model.layers,
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unseen_classes=model.attrs["unseen_classes"],
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train=is_train,
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has_upper=model.attrs["has_upper"],
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)
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return step_model, step_model.finish_steps
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def init(model, X=None, Y=None):
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model.get_ref("tok2vec").initialize(X=X)
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lower = model.get_ref("lower")
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lower.initialize()
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if model.attrs["has_upper"]:
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statevecs = model.ops.alloc2f(2, lower.get_dim("nO"))
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model.get_ref("upper").initialize(X=statevecs)
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def resize_output(model, new_nO):
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tok2vec = model.get_ref("tok2vec")
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lower = model.get_ref("lower")
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upper = model.get_ref("upper")
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if not model.attrs["has_upper"]:
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if lower.has_dim("nO") is None:
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lower.set_dim("nO", new_nO)
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return
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elif upper.has_dim("nO") is None:
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upper.set_dim("nO", new_nO)
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return
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elif new_nO == upper.get_dim("nO"):
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return
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smaller = upper
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nI = None
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if smaller.has_dim("nI"):
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nI = smaller.get_dim("nI")
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with use_ops("numpy"):
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larger = Linear(nO=new_nO, nI=nI)
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larger.init = smaller.init
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# it could be that the model is not initialized yet, then skip this bit
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if nI:
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larger_W = larger.ops.alloc2f(new_nO, nI)
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larger_b = larger.ops.alloc1f(new_nO)
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smaller_W = smaller.get_param("W")
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smaller_b = smaller.get_param("b")
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# Weights are stored in (nr_out, nr_in) format, so we're basically
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# just adding rows here.
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if smaller.has_dim("nO"):
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larger_W[: smaller.get_dim("nO")] = smaller_W
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larger_b[: smaller.get_dim("nO")] = smaller_b
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for i in range(smaller.get_dim("nO"), new_nO):
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model.attrs["unseen_classes"].add(i)
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larger.set_param("W", larger_W)
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larger.set_param("b", larger_b)
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model._layers[-1] = larger
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model.set_ref("upper", larger)
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return model
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