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Add update_tensors flag to Language.update. Experimental, re #1182
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@ -91,7 +91,8 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
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for batch in minibatch(train_docs, size=batch_sizes):
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docs, golds = zip(*batch)
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nlp.update(docs, golds, sgd=optimizer,
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drop=next(dropout_rates), losses=losses)
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drop=next(dropout_rates), losses=losses,
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update_tensors=True)
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pbar.update(sum(len(doc) for doc in docs))
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with nlp.use_params(optimizer.averages):
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