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nn_char_batch.py
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/
nn_char_batch.py
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import sys
import time
import math
import random
import io_utils
import numpy as np
import theano
import theano.tensor as T
from nn_utils import sample_weights, sample_norm_dist, build_shared_zeros, relu
from optimizers import sgd, ada_grad
theano.config.floatX = 'float32'
class Model(object):
def __init__(self, x, c, y, n_words, batch_size, lr, init_emb, vocab_w_size, w_emb_dim, w_hidden_dim,
c_emb_dim, c_hidden_dim, output_dim, vocab_c_size, window, opt):
assert window % 2 == 1, 'Window size must be odd'
""" input """
self.x = x # 1D: n_words * batch_size, 2D: window; elem=word id
self.x_v = x.flatten() # 1D: n_words * batch_size * window; elem=word id
self.c = c # 1D: n_words * batch_size, 2D: window, 3D: max_len_char, 4D: window; elem=char id
self.y = y
self.batch_size = batch_size
self.n_words = n_words
self.lr = lr
n_phi = (w_emb_dim + c_hidden_dim) * window
max_len_char = T.cast(self.c.shape[2], 'int32')
""" params """
if init_emb is not None:
self.emb = theano.shared(init_emb)
else:
self.emb = theano.shared(sample_weights(vocab_w_size, w_emb_dim))
self.pad = build_shared_zeros((1, c_emb_dim))
self.e_c = theano.shared(sample_norm_dist(vocab_c_size - 1, c_emb_dim))
self.emb_c = T.concatenate([self.pad, self.e_c], 0)
self.W_in = theano.shared(sample_weights(n_phi, w_hidden_dim))
self.W_c = theano.shared(sample_weights(c_emb_dim * window, c_hidden_dim))
self.W_out = theano.shared(sample_weights(w_hidden_dim, output_dim))
self.b_in = theano.shared(sample_weights(w_hidden_dim))
self.b_c = theano.shared(sample_weights(c_hidden_dim))
self.b_y = theano.shared(sample_weights(output_dim))
self.params = [self.e_c, self.W_in, self.W_c, self.W_out, self.b_in, self.b_c, self.b_y]
""" look up embedding """
self.x_emb = self.emb[self.x_v] # 1D: batch_size*n_words * window, 2D: emb_dim
self.c_emb = self.emb_c[self.c] # 1D: batch_size*n_words, 2D: window, 3D: max_len_char, 4D: window, 5D: n_c_emb
self.x_emb_r = self.x_emb.reshape((x.shape[0], x.shape[1], -1))
""" convolution """
self.c_phi = T.max(T.dot(self.c_emb.reshape((batch_size * n_words, window, max_len_char, -1)), self.W_c) + self.b_c, 2) # 1D: n_words, 2D: window, 3D: n_h_c
self.x_phi = T.concatenate([self.x_emb_r, self.c_phi], axis=2)
""" forward """
self.h = relu(T.dot(self.x_phi.reshape((batch_size * n_words, n_phi)), self.W_in) + self.b_in)
self.o = T.dot(self.h, self.W_out) + self.b_y
self.p_y_given_x = T.nnet.softmax(self.o)
""" predict """
self.y_pred = T.argmax(self.o, axis=1)
self.result = T.eq(self.y_pred, self.y)
""" loss """
self.log_p = T.log(self.p_y_given_x)[T.arange(batch_size * n_words), self.y]
self.nll = -T.sum(self.log_p)
self.cost = self.nll
if opt == 'sgd':
self.updates = sgd(self.cost, self.params, self.emb, self.x_emb, self.lr)
else:
self.updates = ada_grad(self.cost, self.params, self.emb, self.x_emb, self.x, self.lr)
def convert_into_ids(corpus, vocab_word, vocab_char, vocab_tag, max_char_len):
id_corpus_w = []
id_corpus_c = []
id_corpus_b = []
id_corpus_t = []
for sent in corpus:
w_ids = []
c_ids = []
bs = []
t_ids = []
b = 0
for w, t in sent:
w_id = vocab_word.get_id(w.lower())
t_id = vocab_tag.get_id(t)
if w_id is None:
w_id = vocab_word.get_id(io_utils.UNK)
assert w_id is not None
assert t_id is not None
w_ids.append(w_id)
t_ids.append(t_id)
c_ids.append(zero_pad_char([vocab_char.get_id(c) for c in w], max_char_len))
b += len(w)
bs.append(b)
id_corpus_w.append(w_ids)
id_corpus_c.append(c_ids)
id_corpus_b.append(bs)
id_corpus_t.append(t_ids)
assert len(id_corpus_w) == len(id_corpus_c) == len(id_corpus_b) == len(id_corpus_t)
return id_corpus_w, id_corpus_c, id_corpus_b, id_corpus_t
def zero_pad_char(char_ids, max_char_len, window=5):
p = window / 2
pad = [0 for i in xrange(p)]
char_ids = pad + char_ids + [0 for i in xrange(max_char_len-len(char_ids))] + pad
return [char_ids[i: i+window] for i in xrange(max_char_len)]
def set_minibatch(id_x, id_c, id_y, max_char_len, batch_size, window=5):
samples_x = []
samples_c = []
samples_y = []
batch_indices = []
p = window / 2
zero_pad_w = [0 for i in xrange(p)]
zero_char = [[0 for i in xrange(window)] for i in xrange(max_char_len)]
zero_pad_c = [zero_char for i in xrange(p)]
prev_sent_len = -1
b_index = 0
bob = 0
eob = 0
for i in xrange(len(id_x)):
sent_x = id_x[i] # sent_w: 1D: n_words; elem=word id
sent_len = len(sent_x)
sent_x = zero_pad_w + sent_x + zero_pad_w
sent_c = id_c[i] # 1D: n_words, 2D: max_n_chars, 3D: window
sent_c = zero_pad_c + sent_c + zero_pad_c
assert len(sent_x) == len(sent_c)
for j in xrange(sent_len):
sample_x = sent_x[j: j + window]
sample_c = sent_c[j: j + window]
samples_x.append(sample_x)
samples_c.append(sample_c)
assert len(sample_x) == len(sample_c)
samples_y.extend(id_y[i])
if b_index < batch_size and (sent_len == prev_sent_len or prev_sent_len < 0):
pass
else:
batch_indices.append((bob, eob, prev_sent_len, b_index))
bob = eob
b_index = 0
prev_sent_len = sent_len
eob += sent_len
b_index += 1
batch_indices.append((bob, eob, prev_sent_len, b_index))
return samples_x, samples_c, samples_y, batch_indices
def shared_samples(samples_x, samples_c, samples_y):
def shared(samples):
return theano.shared(np.asarray(samples, dtype='int32'))
return shared(samples_x), shared(samples_c), shared(samples_y)
def train(args):
print '\nNEURAL POS TAGGER START\n'
print '\tINITIAL EMBEDDING\t%s %s' % (args.word_list, args.emb_list)
print '\tWORD\t\t\tEmb Dim: %d Hidden Dim: %d' % (args.w_emb_dim, args.w_hidden_dim)
print '\tCHARACTER\t\tEmb Dim: %d Hidden Dim: %d' % (args.c_emb_dim, args.c_hidden_dim)
print '\tOPTIMIZATION\t\tMethod: %s Learning Rate: %f\n' % (args.opt, args.lr)
print '\tMINI-BATCH: %d\n' % args.batch_size
""" load data """
print 'Loading data sets...\n'
train_corpus, vocab_word, vocab_char, vocab_tag, max_char_len = io_utils.load_conll(args.train_data)
""" limit data set """
train_corpus = train_corpus[:args.data_size]
train_corpus.sort(key=lambda a: len(a))
dev_corpus = None
if args.dev_data:
dev_corpus, dev_vocab_word, dev_vocab_char, dev_vocab_tag, max_char_len_dev = io_utils.load_conll(args.dev_data)
for w in dev_vocab_word.i2w:
if args.vocab_size is None or vocab_word.size() < args.vocab_size:
vocab_word.add_word(w)
for c in dev_vocab_char.i2w:
vocab_char.add_word(c)
for t in dev_vocab_tag.i2w:
vocab_tag.add_word(t)
if args.save:
io_utils.dump_data(vocab_word, 'vocab_word')
io_utils.dump_data(vocab_char, 'vocab_char')
io_utils.dump_data(vocab_tag, 'vocab_tag')
""" load pre-trained embeddings """
init_w_emb = None
if args.emb_list:
print '\tLoading word embeddings...\n'
init_w_emb = io_utils.load_init_emb(args.emb_list, args.word_list, vocab_word)
w_emb_dim = init_w_emb.shape[1]
else:
w_emb_dim = args.w_emb_dim
""" converting into ids """
print '\nConverting into IDs...\n'
tr_x, tr_c, tr_b, tr_y = convert_into_ids(train_corpus, vocab_word, vocab_char, vocab_tag, max_char_len)
tr_x, tr_c, tr_y, tr_b = set_minibatch(tr_x, tr_c, tr_y, max_char_len, args.batch_size)
tr_x, tr_c, tr_y = shared_samples(tr_x, tr_c, tr_y)
if args.dev_data:
dev_x, dev_c, dev_b, dev_y = convert_into_ids(dev_corpus, vocab_word, vocab_char, vocab_tag, max_char_len_dev)
dev_x, dev_c, dev_y, dev_b = set_minibatch(dev_x, dev_c, dev_y, max_char_len_dev, 1)
dev_x, dev_c, dev_y = shared_samples(dev_x, dev_c, dev_y)
print '\tTrain Sentences: %d Dev Sentences: %d' % (len(train_corpus), len(dev_corpus))
else:
print '\tTrain Sentences: %d' % len(train_corpus)
print '\tWord size: %d Char size: %d' % (vocab_word.size(), vocab_char.size())
""" set model parameters """
w_hidden_dim = args.w_hidden_dim
c_emb_dim = args.c_emb_dim
c_hidden_dim = args.c_hidden_dim
output_dim = vocab_tag.size()
window = args.window
opt = args.opt
""" symbol definition """
print '\tCompiling Theano Code...'
bos = T.iscalar('bos')
eos = T.iscalar('eos')
n_words = T.iscalar('n_words')
batch_size = T.iscalar('batch_size')
x = T.imatrix('x')
c = T.itensor4('c')
y = T.ivector('y')
lr = T.fscalar('lr')
""" tagger set up """
tagger = Model(x=x, c=c, y=y, n_words=n_words, batch_size=batch_size, lr=lr, init_emb=init_w_emb,
vocab_w_size=vocab_word.size(), w_emb_dim=w_emb_dim, w_hidden_dim=w_hidden_dim,
c_emb_dim=c_emb_dim, c_hidden_dim=c_hidden_dim, output_dim=output_dim,
vocab_c_size=vocab_char.size(), window=window, opt=opt)
train_f = theano.function(
inputs=[bos, eos, n_words, batch_size, lr],
outputs=[tagger.nll, tagger.result],
updates=tagger.updates,
givens={
x: tr_x[bos: eos],
c: tr_c[bos: eos],
y: tr_y[bos: eos]
},
mode='FAST_RUN'
)
dev_f = theano.function(
inputs=[bos, eos, n_words, batch_size],
outputs=tagger.result,
givens={
x: dev_x[bos: eos],
c: dev_c[bos: eos],
y: dev_y[bos: eos]
},
mode='FAST_RUN'
)
def _train():
for epoch in xrange(args.epoch):
_lr = args.lr / float(epoch+1)
indices = range(len(tr_b))
random.shuffle(indices)
print '\nEpoch: %d' % (epoch + 1)
print '\tBatch Index: ',
start = time.time()
total = 0.0
correct = 0
losses = 0.0
for i, index in enumerate(indices):
if i % 100 == 0 and i != 0:
print i,
sys.stdout.flush()
boundary = tr_b[index]
loss, corrects = train_f(boundary[0], boundary[1], boundary[2],boundary[3], _lr)
assert math.isnan(loss) is False, i
total += len(corrects)
correct += np.sum(corrects)
losses += loss
end = time.time()
print '\tTime: %f seconds' % (end - start)
print '\tNegative Log Likelihood: %f' % losses
print '\tAccuracy:%f Total:%d Correct:%d' % ((correct / total), total, correct)
_dev(dev_f)
def _dev(model):
print '\tBatch Index: ',
start = time.time()
total = 0.0
correct = 0
for index in xrange(len(dev_b)):
if index % 100 == 0 and index != 0:
print index,
sys.stdout.flush()
boundary = dev_b[index]
corrects = model(boundary[0], boundary[1], boundary[2], boundary[3])
total += len(corrects)
correct += np.sum(corrects)
end = time.time()
print '\tTime: %f seconds' % (end - start)
print '\tAccuracy:%f Total:%d Correct:%d' % ((correct / total), total, correct)
_train()
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(description='Train NN tagger.')
""" Mode """
parser.add_argument('-mode', default='train', help='train/test')
""" Model """
parser.add_argument('--model', default='char', help='word/char')
parser.add_argument('--save', type=bool, default=True, help='save model')
parser.add_argument('--load', type=str, default=None, help='load model')
""" Data """
parser.add_argument('--train_data', help='path to training data')
parser.add_argument('--dev_data', help='path to development data')
parser.add_argument('--test_data', help='path to test data')
parser.add_argument('--data_size', type=int, default=100000)
parser.add_argument('--vocab_size', type=int, default=100000000)
""" Neural Architectures """
parser.add_argument('--emb_list', default=None, help='initial embedding list file (word2vec output)')
parser.add_argument('--word_list', default=None, help='initial word list file (word2vec output)')
parser.add_argument('--w_emb_dim', type=int, default=100, help='dimension of word embeddings')
parser.add_argument('--c_emb_dim', type=int, default=10, help='dimension of char embeddings')
parser.add_argument('--w_hidden_dim', type=int, default=300, help='dimension of word hidden layer')
parser.add_argument('--c_hidden_dim', type=int, default=50, help='dimension of char hidden layer')
parser.add_argument('--window', type=int, default=5, help='window size for convolution')
parser.add_argument('--batch_size', type=int, default=32, help='mini batch size')
""" Training Parameters """
parser.add_argument('--opt', default='sgd', help='optimization method')
parser.add_argument('--epoch', type=int, default=10, help='number of epochs to train')
parser.add_argument('--lr', type=float, default=0.0075, help='learning rate')
args = parser.parse_args()
train(args=args)