def classify(self, seqs, batch_size = 64):
if not isinstance(seqs[0], list):
seqs = [seqs, ]
pred_probs = self.classify_batch(seqs)
logger.warning('not examined yet, please check')
return pred_probs[0]
else:
kf = lstmtool.get_minibatches_idx(len(seqs), batch_size)
#preds = []
pred_probs = []
for _, idx in kf:
pds = self.classify_batch([seqs[i] for i in idx])
#preds.extend(ps)
pred_probs.extend(pds)
return pred_probs
def train(self,
dataset,
# model params
ydim,
use_dropout = True,
reload_model = False,
fname_model = FNAME_MODEL,
# training params
validFreq = 1000,
saveFreq = 1000,
patience = 10,
max_epochs = 5000,
decay_c = 0.,
lrate = 0.0001,
batch_size = 16,
valid_batch_size = 64,
optimizer = lstmtool.adadelta,
noise_std = 0.,
# debug params
dispFreq = 10,
):
train, valid, test = dataset
# building model
logger.info('building model...')
model_options = locals().copy()
if isinstance(train[0][0][0], list):
model_options['dim_proj'] = len(train[0][0][0])
else:
'''
as i don't know what's wrong with init_layer when dim_proj = 1
so i map x -> (x, x) to change dim_proj as 2
'''
train, valid, test = LstmClassifier.x_1Dto2D(dataset)
model_options['dim_proj'] = 2
params = self.init_params(model_options)
if reload_model:
if os.path.exists(fname_model):
lstmtool.load_params(fname_model, params)
else:
logger.warning('model %s not found'%(fname_model))
return None
tparams = lstmtool.init_tparams(params)
use_noise, x, mask, y, f_pred_prob, f_pred, cost = self.build_model(tparams, model_options)
cPickle.dump(model_options, open('%s.pkl'%(fname_model), 'wb'), -1) # why -1??
# preparing functions for training
logger.info('preparing functions')
if decay_c > 0.:
decay_c = theano.shared(np_floatX(decay_c), name='decay_c')
weight_decay = 0.
weight_decay += (tparams['U'] ** 2).sum()
weight_decay *= decay_c
cost += weight_decay
f_cost = theano.function([x, mask, y], cost, name = 'f_cost')
grads = T.grad(cost, wrt = tparams.values())
f_grad = theano.function([x, mask, y], grads, name = 'f_grad')
lr = T.scalar(name = 'lr')
f_grad_shared, f_update = optimizer(lr, tparams, grads, x, mask, y, cost)
kf_valid = lstmtool.get_minibatches_idx(len(valid[0]), valid_batch_size)
kf_test = lstmtool.get_minibatches_idx(len(test[0]), valid_batch_size)
if validFreq == None:
validFreq = len(train[0]) / batch_size
if saveFreq == None:
saveFreq = len(train[0]) / batch_size
history_errs = []
best_p = None
bad_count = 0
uidx = 0 # number of update done
estop = False # early stop
# training
logger.info('start training...')
start_time = time.time()
try:
for eidx in xrange(max_epochs):
n_samples = 0
kf = lstmtool.get_minibatches_idx(len(train[0]), batch_size, shuffle = True)
for _, train_index in kf:
uidx += 1
use_noise.set_value(1.)
x = [train[0][t] for t in train_index]
y = [train[1][t] for t in train_index]
x, mask = self.prepare_x(x)
n_samples += x.shape[1]
cost = f_grad_shared(x, mask, y)
f_update(lrate)
if np.isnan(cost) or np.isinf(cost):
'''
NaN of Inf encountered
'''
logger.warning('NaN detected')
return 1., 1., 1.
if np.mod(uidx, dispFreq) == 0:
'''
display progress at $dispFreq
'''
logger.info('Epoch %d Update %d Cost %f'%(eidx, uidx, cost))
if np.mod(uidx, saveFreq) == 0:
'''
save new model to file at $saveFreq
'''
logger.info('Model update')
if best_p is not None:
params = best_p
else:
params = lstmtool.unzip(tparams)
np.savez(fname_model, history_errs = history_errs, **params)
if np.mod(uidx, validFreq) == 0:
'''
check prediction error at %validFreq
'''
use_noise.set_value(0.)
# not necessary
train_err = lstmtool.pred_error(f_pred, self.prepare_data, train, kf)
valid_err = lstmtool.pred_error(f_pred, self.prepare_data, valid, kf_valid)
test_err = lstmtool.pred_error(f_pred, self.prepare_data, test, kf_test)
history_errs.append([valid_err, test_err])
if (uidx == 0 or valid_err <= np.array(history_errs)[:, 0].min()):
best_p = lstmtool.unzip(tparams)
bad_count = 0
logger.info('prediction error: train %f valid %f test %f'%(
train_err, valid_err, test_err)
)
if (len(history_errs) > patience and
valid_err >= np.array(history_errs)[:-patience, 0].min()):
bad_count += 1
if bad_count > patience:
logger.info('Early stop!')
estop = True
break
logger.info('%d samples seen'%(n_samples))
if estop:
break
except KeyboardInterrupt:
print logger.debug('training interrupted by user')
end_time = time.time()
if best_p is not None:
lstmtool.zipp(best_p, tparams)
else:
best_p = lstmtool.unzip(tparams)
use_noise.set_value(0.)
kf_train = lstmtool.get_minibatches_idx(len(train[0]), batch_size)
train_err = lstmtool.pred_error(f_pred, self.prepare_data, train, kf_train)
valid_err = lstmtool.pred_error(f_pred, self.prepare_data, valid, kf_valid)
test_err = lstmtool.pred_error(f_pred, self.prepare_data, test, kf_test)
logger.info('prediction error: train %f valid %f test %f'%(
train_err, valid_err, test_err)
)
np.savez(
fname_model,
train_err = train_err,
valid_err = valid_err,
test_error = test_err,
history_errs = history_errs, **best_p
)
logger.info('totally %d epoches in %.1f sec'%(eidx + 1, end_time - start_time))
self.f_pred_prob = f_pred_prob
self.f_pred = f_pred
return train_err, valid_err, test_err
