mirror of
https://github.com/explosion/spaCy.git
synced 2024-09-21 19:39:13 +03:00
5847be6022
* avoid changing original config * fix elif structure, batch with just int crashes otherwise * tok2vec example with doc2feats, encode and embed architectures * further clean up MultiHashEmbed * further generalize Tok2Vec to work with extract-embed-encode parts * avoid initializing the charembed layer with Docs (for now ?) * small fixes for bilstm config (still does not run) * rename to core layer * move new configs * walk model to set nI instead of using core ref * fix senter overfitting test to be more similar to the training data (avoid flakey behaviour)
55 lines
1.7 KiB
Python
55 lines
1.7 KiB
Python
from thinc.api import Model
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def CharacterEmbed(nM, nC):
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# nM: Number of dimensions per character. nC: Number of characters.
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nO = nM * nC if (nM is not None and nC is not None) else None
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return Model(
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"charembed",
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forward,
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init=init,
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dims={"nM": nM, "nC": nC, "nO": nO, "nV": 256},
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params={"E": None},
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).initialize()
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def init(model, X=None, Y=None):
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vectors_table = model.ops.alloc3f(
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model.get_dim("nC"), model.get_dim("nV"), model.get_dim("nM")
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)
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model.set_param("E", vectors_table)
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def forward(model, docs, is_train):
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if docs is None:
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return []
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ids = []
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output = []
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E = model.get_param("E")
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nC = model.get_dim("nC")
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nM = model.get_dim("nM")
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nO = model.get_dim("nO")
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# This assists in indexing; it's like looping over this dimension.
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# Still consider this weird witch craft...But thanks to Mark Neumann
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# for the tip.
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nCv = model.ops.xp.arange(nC)
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for doc in docs:
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doc_ids = doc.to_utf8_array(nr_char=nC)
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doc_vectors = model.ops.alloc3f(len(doc), nC, nM)
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# Let's say I have a 2d array of indices, and a 3d table of data. What numpy
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# incantation do I chant to get
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# output[i, j, k] == data[j, ids[i, j], k]?
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doc_vectors[:, nCv] = E[nCv, doc_ids[:, nCv]]
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output.append(doc_vectors.reshape((len(doc), nO)))
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ids.append(doc_ids)
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def backprop(d_output):
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dE = model.ops.alloc(E.shape, dtype=E.dtype)
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for doc_ids, d_doc_vectors in zip(ids, d_output):
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d_doc_vectors = d_doc_vectors.reshape((len(doc_ids), nC, nM))
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dE[nCv, doc_ids[:, nCv]] += d_doc_vectors[:, nCv]
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model.inc_grad("E", dE)
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return []
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return output, backprop
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