def test_genmodel(gen_model, train, dev, word_index, classify_model = None, glove_alter = None, batch_size = 64, ci = False):
gens_count = 6
dev_counts = 6
gens = []
for i in range(gens_count):
creatives = np.random.random_integers(0, len(train), batch_size)
preds = generation_predict_embed(gen_model, word_index.index, dev[:batch_size], creatives, class_indices = [i % 3] * batch_size)
gens.append(get_classes(preds))
for j in range(dev_counts):
print " ".join(dev[j][0])
print " ".join(dev[j][1])
print "Generations: "
for i in range(gens_count):
gen_lex = gens[i][j]
gen_str = word_index.print_seq(gen_lex)
if ci:
gen_str = load_data.LABEL_LIST[i%3][0] + ' ' + gen_str
if classify_model != None and glove_alter != None:
probs = misc.predict_example(" ".join(dev[j][0]), gen_str, classify_model, glove_alter)
print probs[0][0][i%3], gen_str
else:
print gen_str
print
def test_model2(model, dev, glove):
from misc import predict_example
tp = 0
for ex in dev:
probs = predict_example(" ".join(ex[0]), " ".join(ex[1]), model, glove)
label = load_data.LABEL_LIST[np.argmax(probs)]
if label == ex[2]:
tp +=1
return tp / float(len(dev))
def tune_model(observed_example, train_example, model, glove):
class_arg = load_data.LABEL_LIST.index(observed_example[2])
prem = " ".join(observed_example[0])
hypo = " ".join(observed_example[1])
print prem, hypo, observed_example[2], class_arg
for i in range(30):
probs = misc.predict_example(prem, hypo, model, glove)[0]
print i, probs
if probs.argmax() == class_arg:
break
models.update_model_once(model, glove, [train_example])
