def validate(dev, gen_test, beam_size, hypo_len, samples, noise_size, glove, cmodel = None, adverse = False,
diverse = False):
vgen = val_generator(dev, gen_test, beam_size, hypo_len, noise_size)
p = Progbar(samples)
batchez = []
while p.seen_so_far < samples:
batch = next(vgen)
preplexity = np.mean(np.power(2, batch[2]))
loss = np.mean(batch[2])
losses = [('hypo_loss',loss),('perplexity', preplexity)]
if cmodel is not None:
ceval = cmodel.evaluate([batch[0], batch[1]], batch[4], verbose = 0)
losses += [('class_loss', ceval[0]), ('class_acc', ceval[1])]
probs = cmodel.predict([batch[0], batch[1]], verbose = 0)
losses += [('class_entropy', np.mean(-np.sum(probs * np.log(probs), axis=1)))]
p.add(len(batch[0]), losses)
batchez.append(batch)
batchez = merge_result_batches(batchez)
res = {}
if adverse:
val_loss = adverse_validation(dev, batchez, glove)
print 'adverse_loss:', val_loss
res['adverse_loss'] = val_loss
if diverse:
div, _, _, _ = diversity(dev, gen_test, beam_size, hypo_len, noise_size, 64, 32)
res['diversity'] = div
print
for val in p.unique_values:
arr = p.sum_values[val]
res[val] = arr[0] / arr[1]
return res
def new_generate_dataset(dataset, samples, gen_test, beam_size, hypo_len, noise_size, cmodel):
vgen = val_generator(dataset, gen_test, beam_size, hypo_len, noise_size)
p = Progbar(samples)
batchez = []
while p.seen_so_far < samples:
batch = next(vgen)
probs = cmodel.predict([batch[0], batch[1]], verbose = 0)
batch += (probs,)
p.add(len(batch[0]))
batchez.append(batch)
return merge_result_batches(batchez)
def validate(dev,
gen_test,
beam_size,
hypo_len,
samples,
noise_size,
glove,
cmodel=None,
adverse=False,
diverse=False):
vgen = val_generator(dev, gen_test, beam_size, hypo_len, noise_size)
p = Progbar(samples)
batchez = []
while p.seen_so_far < samples:
batch = next(vgen)
preplexity = np.mean(np.power(2, batch[2]))
loss = np.mean(batch[2])
losses = [('hypo_loss', loss), ('perplexity', preplexity)]
if cmodel is not None:
ceval = cmodel.evaluate([batch[0], batch[1]], batch[4], verbose=0)
losses += [('class_loss', ceval[0]), ('class_acc', ceval[1])]
probs = cmodel.predict([batch[0], batch[1]], verbose=0)
losses += [('class_entropy',
np.mean(-np.sum(probs * np.log(probs), axis=1)))]
p.add(len(batch[0]), losses)
batchez.append(batch)
batchez = merge_result_batches(batchez)
res = {}
if adverse:
val_loss = adverse_validation(dev, batchez, glove)
print 'adverse_loss:', val_loss
res['adverse_loss'] = val_loss
if diverse:
div, _, _, _ = diversity(dev, gen_test, beam_size, hypo_len,
noise_size, 64, 32)
res['diversity'] = div
print
for val in p.unique_values:
arr = p.sum_values[val]
res[val] = arr[0] / arr[1]
return res