def train_and_eval(runid, module_prep_model, c, glove, vocab, gr, s0, grt, s0t, do_eval=True):
print('Model')
model = build_model(glove, vocab, module_prep_model, c)
print('Training')
if c.get('balance_class', False):
one_ratio = np.sum(gr['score'] == 1) / len(gr['score'])
class_weight = {'score': {0: one_ratio, 1: 0.5}}
else:
class_weight = {}
# XXX: samples_per_epoch is in brmson/keras fork, TODO fit_generator()?
model.fit(gr, validation_data=grt,
callbacks=[HypEvCB(s0t, grt),
ModelCheckpoint('hyp-weights-'+runid+'-bestval.h5', save_best_only=True, monitor='acc', mode='max'),
EarlyStopping(monitor='acc', mode='max', patience=4)],
class_weight=class_weight,
batch_size=c['batch_size'], nb_epoch=c['nb_epoch'])
model.save_weights('hyp-weights-'+runid+'-final.h5', overwrite=True)
if c['ptscorer'] is None:
model.save_weights('hyp-weights-'+runid+'-bestval.h5', overwrite=True)
model.load_weights('hyp-weights-'+runid+'-bestval.h5')
if do_eval:
print('Predict&Eval (best epoch)')
prediction = model.predict(gr)['score'][:,0]
prediction_t = model.predict(grt)['score'][:,0]
ev.eval_hypev(prediction, s0, gr['score'], 'Train')
ev.eval_hypev(prediction_t, s0t, grt['score'], 'Val')
dump_questions(s0, s1, gr['score'], prediction, 'Train')
dump_questions(s0t, s1t, grt['score'], prediction_t, 'Val')
return model
def train_and_eval(runid,
module_prep_model,
c,
glove,
vocab,
gr,
s0,
grt,
s0t,
do_eval=True):
print('Model')
model = build_model(glove, vocab, module_prep_model, c)
print('Training')
if c.get('balance_class', False):
one_ratio = np.sum(gr['score'] == 1) / len(gr['score'])
class_weight = {'score': {0: one_ratio, 1: 0.5}}
else:
class_weight = {}
# XXX: samples_per_epoch is in brmson/keras fork, TODO fit_generator()?
model.fit(gr,
validation_data=grt,
callbacks=[
HypEvCB(s0t, grt),
ModelCheckpoint('hyp-weights-' + runid + '-bestval.h5',
save_best_only=True,
monitor='acc',
mode='max'),
EarlyStopping(monitor='acc', mode='max', patience=4)
],
class_weight=class_weight,
batch_size=c['batch_size'],
nb_epoch=c['nb_epoch'])
model.save_weights('hyp-weights-' + runid + '-final.h5', overwrite=True)
if c['ptscorer'] is None:
model.save_weights('hyp-weights-' + runid + '-bestval.h5',
overwrite=True)
model.load_weights('hyp-weights-' + runid + '-bestval.h5')
if do_eval:
print('Predict&Eval (best epoch)')
prediction = model.predict(gr)['score'][:, 0]
prediction_t = model.predict(grt)['score'][:, 0]
ev.eval_hypev(prediction, s0, gr['score'], 'Train')
ev.eval_hypev(prediction_t, s0t, grt['score'], 'Val')
dump_questions(s0, s1, gr['score'], prediction, 'Train')
dump_questions(s0t, s1t, grt['score'], prediction_t, 'Val')
return model
def eval(self, model):
res = []
for gr, fname in [(self.gr, self.trainf), (self.grv, self.valf), (self.grt, self.testf)]:
if gr is None:
res.append(None)
continue
ypred = model.predict(gr)['score'][:,0]
res.append(ev.eval_hypev(ypred, gr['score'], fname))
return tuple(res)
def eval(self, model):
res = []
for gr, fname in [(self.gr, self.trainf), (self.grv, self.valf), (self.grt, self.testf)]:
if gr is None:
res.append(None)
continue
ypred = self.predict(model, gr)
res.append(ev.eval_hypev(gr.get('qids', None), ypred, gr['score'], fname))
return tuple(res)
def eval(self, model):
res = []
for gr, fname in [(self.gr, self.trainf), (self.grv, self.valf),
(self.grt, self.testf)]:
if gr is None:
res.append(None)
continue
ypred = model.predict(gr)['score'][:, 0]
res.append(ev.eval_hypev(ypred, gr['score'], fname))
return tuple(res)
def eval(self, model):
res = []
for gr, fname in [(self.gr, self.trainf), (self.grv, self.valf),
(self.grt, self.testf)]:
if gr is None:
res.append(None)
continue
ypred = self.predict(model, gr)
res.append(
ev.eval_hypev(gr.get('qids', None), ypred, gr['score'], fname))
return tuple(res)
