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Update Keras deep learning tutorial
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@ -14,7 +14,7 @@ class SentimentAnalyser(object):
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def __call__(self, doc):
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X = get_features([doc], self.max_length)
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y = self._keras_model.predict(X)
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y = self._model.predict(X)
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self.set_sentiment(doc, y)
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def pipe(self, docs, batch_size=1000, n_threads=2):
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@ -28,6 +28,13 @@ class SentimentAnalyser(object):
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doc.user_data['sentiment'] = y
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def get_features(docs, max_length):
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Xs = numpy.zeros(len(docs), max_length, dtype='int32')
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for i, doc in enumerate(minibatch):
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for j, token in enumerate(doc[:max_length]):
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Xs[i, j] = token.rank if token.has_vector else 0
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return Xs
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def compile_lstm(embeddings, shape, settings, optimizer):
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model = Sequential()
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model.add(
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@ -59,14 +66,6 @@ def get_embeddings(vocab):
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return vectors
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def get_features(docs, max_length):
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Xs = numpy.zeros(len(docs), max_length, dtype='int32')
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for i, doc in enumerate(minibatch):
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for j, token in enumerate(doc[:max_length]):
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Xs[i, j] = token.rank if token.has_vector else 0
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return Xs
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def train(train_texts, train_labels, dev_texts, dev_labels,
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lstm_shape, lstm_settings, lstm_optimizer, batch_size=100, nb_epoch=5):
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nlp = spacy.load('en', parser=False, tagger=False, entity=False)
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