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Fix deep learning example code
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@ -7,6 +7,7 @@ import cytoolz
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import numpy
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from keras.models import Sequential, model_from_json
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from keras.layers import LSTM, Dense, Embedding, Dropout, Bidirectional
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from keras.layers import TimeDistributed
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from keras.optimizers import Adam
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import cPickle as pickle
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@ -48,10 +49,16 @@ class SentimentAnalyser(object):
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def get_features(docs, max_length):
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Xs = numpy.zeros((len(list(docs)), max_length), dtype='int32')
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docs = list(docs)
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Xs = numpy.zeros((len(docs), max_length), dtype='int32')
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for i, doc in enumerate(docs):
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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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j = 0
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for token in doc:
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if token.has_vector and not token.is_punct and not token.is_space:
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Xs[i, j] = token.rank + 1
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j += 1
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if j >= max_length:
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break
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return Xs
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@ -75,9 +82,12 @@ def compile_lstm(embeddings, shape, settings):
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embeddings.shape[1],
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input_length=shape['max_length'],
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trainable=False,
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weights=[embeddings]
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weights=[embeddings],
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mask_zero=True
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)
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)
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model.add(TimeDistributed(Dense(shape['nr_hidden'] * 2)))
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model.add(Dropout(settings['dropout']))
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model.add(Bidirectional(LSTM(shape['nr_hidden'])))
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model.add(Dropout(settings['dropout']))
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model.add(Dense(shape['nr_class'], activation='sigmoid'))
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@ -87,11 +97,11 @@ def compile_lstm(embeddings, shape, settings):
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def get_embeddings(vocab):
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max_rank = max(lex.rank for lex in vocab if lex.has_vector)
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max_rank = max(lex.rank+1 for lex in vocab if lex.has_vector)
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vectors = numpy.ndarray((max_rank+1, vocab.vectors_length), dtype='float32')
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for lex in vocab:
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if lex.has_vector:
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vectors[lex.rank] = lex.vector
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vectors[lex.rank + 1] = lex.vector
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return vectors
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