mirror of
https://github.com/explosion/spaCy.git
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147 lines
4.3 KiB
Cython
147 lines
4.3 KiB
Cython
"""Feed-forward neural network, using Thenao."""
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import os
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import sys
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import time
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import numpy
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import theano
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import theano.tensor as T
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import plac
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from spacy.gold import read_json_file
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from spacy.gold import GoldParse
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from spacy.en.pos import POS_TEMPLATES, POS_TAGS, setup_model_dir
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def build_model(n_classes, n_vocab, n_hidden, n_word_embed, n_tag_embed):
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# allocate symbolic variables for the data
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words = T.vector('words')
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tags = T.vector('tags')
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word_e = _init_embedding(n_words, n_word_embed)
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tag_e = _init_embedding(n_tags, n_tag_embed)
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label_e = _init_embedding(n_labels, n_label_embed)
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maxent_W, maxent_b = _init_maxent_weights(n_hidden, n_classes)
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hidden_W, hidden_b = _init_hidden_weights(28*28, n_hidden, T.tanh)
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params = [hidden_W, hidden_b, maxent_W, maxent_b, word_e, tag_e, label_e]
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x = T.concatenate([
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T.flatten(word_e[word_indices], outdim=1),
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T.flatten(tag_e[tag_indices], outdim=1)])
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p_y_given_x = feed_layer(
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T.nnet.softmax,
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maxent_W,
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maxent_b,
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feed_layer(
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T.tanh,
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hidden_W,
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hidden_b,
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x))[0]
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guess = T.argmax(p_y_given_x)
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cost = (
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-T.log(p_y_given_x[y])
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+ L1(L1_reg, maxent_W, hidden_W, word_e, tag_e)
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+ L2(L2_reg, maxent_W, hidden_W, wod_e, tag_e)
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)
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train_model = theano.function(
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inputs=[words, tags, y],
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outputs=guess,
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updates=[update(learning_rate, param, cost) for param in params]
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)
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evaluate_model = theano.function(
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inputs=[x, y],
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outputs=T.neq(y, T.argmax(p_y_given_x[0])),
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)
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return train_model, evaluate_model
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def _init_embedding(vocab_size, n_dim):
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embedding = 0.2 * numpy.random.uniform(-1.0, 1.0, (vocab_size+1, n_dim))
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return theano.shared(embedding).astype(theano.config.floatX)
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def _init_maxent_weights(n_hidden, n_out):
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weights = numpy.zeros((n_hidden, 10), dtype=theano.config.floatX)
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bias = numpy.zeros((10,), dtype=theano.config.floatX)
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return (
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theano.shared(name='W', borrow=True, value=weights),
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theano.shared(name='b', borrow=True, value=bias)
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)
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def _init_hidden_weights(n_in, n_out, activation=T.tanh):
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rng = numpy.random.RandomState(1234)
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weights = numpy.asarray(
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rng.uniform(
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low=-numpy.sqrt(6. / (n_in + n_out)),
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high=numpy.sqrt(6. / (n_in + n_out)),
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size=(n_in, n_out)
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),
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dtype=theano.config.floatX
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)
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bias = numpy.zeros((n_out,), dtype=theano.config.floatX)
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return (
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theano.shared(value=weights, name='W', borrow=True),
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theano.shared(value=bias, name='b', borrow=True)
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)
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def feed_layer(activation, weights, bias, input):
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return activation(T.dot(input, weights) + bias)
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def L1(L1_reg, w1, w2):
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return L1_reg * (abs(w1).sum() + abs(w2).sum())
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def L2(L2_reg, w1, w2):
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return L2_reg * ((w1 ** 2).sum() + (w2 ** 2).sum())
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def update(eta, param, cost):
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return (param, param - (eta * T.grad(cost, param)))
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def main(train_loc, eval_loc, model_dir):
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learning_rate = 0.01
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L1_reg = 0.00
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L2_reg = 0.0001
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print "... reading the data"
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gold_train = list(read_json_file(train_loc))
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print '... building the model'
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pos_model_dir = path.join(model_dir, 'pos')
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if path.exists(pos_model_dir):
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shutil.rmtree(pos_model_dir)
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os.mkdir(pos_model_dir)
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setup_model_dir(sorted(POS_TAGS.keys()), POS_TAGS, POS_TEMPLATES, pos_model_dir)
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train_model, evaluate_model = build_model(n_hidden, len(POS_TAGS), learning_rate,
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L1_reg, L2_reg)
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print '... training'
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for epoch in range(1, n_epochs+1):
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for raw_text, sents in gold_tuples:
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for (ids, words, tags, ner, heads, deps), _ in sents:
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tokens = nlp.tokenizer.tokens_from_list(words)
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for t in tokens:
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guess = train_model([t.orth], [t.tag])
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loss += guess != t.tag
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print loss
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# compute zero-one loss on validation set
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#error = numpy.mean([evaluate_model(x, y) for x, y in dev_examples])
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#print('epoch %i, validation error %f %%' % (epoch, error * 100))
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if __name__ == '__main__':
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plac.call(main)
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