idx2word = dict((k, v) for v, k in dic['words2idx'].iteritems())
train_lex, train_ne, train_y = train_set
valid_lex, valid_ne, valid_y = valid_set
test_lex, test_ne, test_y = test_set
vocsize = len(dic['words2idx'])
nclasses = len(dic['labels2idx'])
nsentences = len(train_lex)
# instanciate the model
numpy.random.seed(s['seed'])
random.seed(s['seed'])
rnn = model(nh=s['nhidden'],
nc=nclasses,
ne=vocsize,
de=s['emb_dimension'],
cs=s['win'])
# train with early stopping on validation set
best_f1 = -numpy.inf
s['clr'] = s['lr']
for e in xrange(s['nepochs']):
# shuffle
shuffle([train_lex, train_ne, train_y], s['seed'])
s['ce'] = e
tic = time.time()
for i in xrange(nsentences):
cwords = contextwin(train_lex[i], s['win'])
words = map(lambda x: numpy.asarray(x).astype('int32'),\
minibatch(cwords, s['bs']))
lambda x, y: list(x)+list(y),\
train_lex+valid_lex+test_lex)))
nclasses = len(set(reduce(\
lambda x, y: list(x)+list(y),\
train_y+test_y+valid_y)))
nsentences = len(train_lex)
# instanciate the model
numpy.random.seed(s.seed)
random.seed(s.seed)
rnn = model( nh = s.hidden_size,
nc = nclasses,
ne = vocsize,
de = s.emb_size,
cs = s.win,
memory_size = s.memory_size,
n_memory_slots = s.n_memory_slots )
# train with early stopping on validation set
best_f1 = -numpy.inf
s.clr = s.lr
for e in xrange(s.n_epochs):
# shuffle
shuffle([train_lex, train_ne, train_y], s.seed)
s.ce = e
tic = time.time()
for i in xrange(nsentences):
cwords = contextwin(train_lex[i], s.win)
words = map(lambda x: numpy.asarray(x).astype('int32'),\
idx2label = dict((k,v) for v,k in dic['labels2idx'].iteritems())
idx2word = dict((k,v) for v,k in dic['words2idx'].iteritems())
train_lex, train_y, train_cue = train_set
valid_lex, valid_y, valid_cue = valid_set
vocsize = len(dic['words2idx'])
nclasses = len(dic['labels2idx'])
nsentences = len(train_lex)
# instanciate the model
numpy.random.seed(s['seed'])
random.seed(s['seed'])
rnn = model( nh = s['nhidden'],
nc = nclasses,
ne = vocsize,
de = s['emb_dimension'],
cs = s['win'],
cue = args.c)
# train with early stopping on validation set
best_f1 = -numpy.inf
s['clr'] = s['lr']
for e in xrange(s['nepochs']):
# shuffle
shuffle([train_lex,train_y,train_cue], s['seed'])
print '[learning] epoch %d' % e
s['ce'] = e
tic = time.time()
for i in xrange(nsentences):
# take the context win of both
# merge the results
train_set, valid_set, test_set, dic = load.atisfold(s["fold"])
idx2label = dict((k, v) for v, k in dic["labels2idx"].iteritems())
idx2word = dict((k, v) for v, k in dic["words2idx"].iteritems())
train_lex, train_ne, train_y = train_set
valid_lex, valid_ne, valid_y = valid_set
test_lex, test_ne, test_y = test_set
vocsize = len(dic["words2idx"])
nclasses = len(dic["labels2idx"])
nsentences = len(train_lex)
# instanciate the model
numpy.random.seed(s["seed"])
random.seed(s["seed"])
rnn = model(nh=s["nhidden"], nc=nclasses, ne=vocsize, de=s["emb_dimension"], cs=s["win"])
# train with early stopping on validation set
best_f1 = -numpy.inf
s["clr"] = s["lr"]
for e in xrange(s["nepochs"]):
# shuffle
shuffle([train_lex, train_ne, train_y], s["seed"])
s["ce"] = e
tic = time.time()
for i in xrange(nsentences):
cwords = contextwin(train_lex[i], s["win"])
words = map(lambda x: numpy.asarray(x).astype("int32"), minibatch(cwords, s["bs"]))
labels = train_y[i]
for word_batch, label_last_word in zip(words, labels):
rnn.train(word_batch, label_last_word, s["clr"])
