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Move get_characters_loss
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14
spacy/_ml.py
14
spacy/_ml.py
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@ -988,3 +988,17 @@ def get_cossim_loss(yh, y, ignore_zeros=False):
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losses[zero_indices] = 0
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losses[zero_indices] = 0
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loss = losses.sum()
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loss = losses.sum()
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return loss, -d_yh
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return loss, -d_yh
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def get_characters_loss(ops, docs, prediction, nr_char=10):
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target_ids = numpy.vstack([doc.to_utf8_array(nr_char=nr_char) for doc in docs])
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target_ids = target_ids.reshape((-1,))
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target = ops.asarray(to_categorical(target_ids, nb_classes=256), dtype="f")
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target = target.reshape((-1, 256*nr_char))
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diff = prediction - target
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loss = (diff**2).sum()
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d_target = diff / float(prediction.shape[0])
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return loss, d_target
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@ -19,7 +19,7 @@ from ..errors import Errors
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from ..tokens import Doc
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from ..tokens import Doc
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from ..attrs import ID, HEAD
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from ..attrs import ID, HEAD
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from .._ml import Tok2Vec, flatten, chain, create_default_optimizer
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from .._ml import Tok2Vec, flatten, chain, create_default_optimizer
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from .._ml import masked_language_model, get_cossim_loss
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from .._ml import masked_language_model, get_cossim_loss, get_characters_loss
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from .._ml import MultiSoftmax
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from .._ml import MultiSoftmax
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from .. import util
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from .. import util
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from .train import _load_pretrained_tok2vec
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from .train import _load_pretrained_tok2vec
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@ -304,17 +304,6 @@ def make_docs(nlp, batch, min_length, max_length):
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return docs, skip_count
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return docs, skip_count
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def get_characters_loss(ops, docs, prediction, nr_char=10):
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target_ids = numpy.vstack([doc.to_utf8_array(nr_char=nr_char) for doc in docs])
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target_ids = target_ids.reshape((-1,))
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target = ops.asarray(to_categorical(target_ids, nb_classes=256), dtype="f")
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target = target.reshape((-1, 256*nr_char))
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diff = prediction - target
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loss = (diff**2).sum()
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d_target = diff / float(prediction.shape[0])
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return loss, d_target
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def get_vectors_loss(ops, docs, prediction, objective="L2"):
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def get_vectors_loss(ops, docs, prediction, objective="L2"):
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"""Compute a mean-squared error loss between the documents' vectors and
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"""Compute a mean-squared error loss between the documents' vectors and
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the prediction.
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the prediction.
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