vocsize = len(set(reduce(\
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['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_x+test_x)))
print "vocsize=",vocsize
nclasses = len(set(reduce(\
lambda x, y: list(x)+list(y),\
train_y)))
nsentences = len(train_x)
# instanciate the model
print "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'] )
fileTime = time.strftime("%Y-%m-%d",time.localtime(time.time()))
folder = "..\\paramInfor\\rawMyRnn\\" + fileTime + os.path.basename(__file__).split('.')[0]
if not os.path.exists(folder): os.mkdir(folder)
# train with early stopping on validation set
print "train with early stopping nepochs ..."
s['clr'] = s['lr']
for e in xrange(s['nepochs']):
# shuffle
shuffle([train_x, train_y], s['seed'])
s['ce'] = e
tic = time.time()
for i in xrange(nsentences):
