def adverse_validation(dev, batchez, glove):
samples = len(batchez[1])
discriminator = am.discriminator(glove, 50)
ad_model = am.adverse_model(discriminator)
res = ad_model.fit([dev[1][:samples], batchez[1]], np.zeros(samples), validation_split=0.1,
verbose = 0, nb_epoch = 20, callbacks = [EarlyStopping(patience=2)])
return np.min(res.history['val_loss'])
def adverse_model_validate(model_dir, dev, aug_dev, glove, dim):
discriminator = am.discriminator(glove, dim)
discriminator.load_weights(model_dir + '/adverse.weights')
dev_len = len(aug_dev[1])
preds_orig = discriminator.predict(dev[1][:dev_len])
preds_aug = discriminator.predict(aug_dev[1])
return np.mean(preds_orig > preds_aug)
def adverse_model_train(model_dir, train, aug_train, dev, aug_dev, dim, glove):
discriminator = am.discriminator(glove, dim)
ad_model = am.adverse_model(discriminator)
dev_len = len(aug_dev[1])
res = ad_model.fit([train[1], aug_train[1]], np.zeros(len(train[1])),
validation_data=([dev[1][:dev_len], aug_dev[1]], np.zeros(dev_len)),
verbose = 1, nb_epoch = 5)
discriminator.save_weights(model_dir + '/adverse.weights')
def adverse_validation(dev, batchez, glove):
samples = len(batchez[1])
discriminator = am.discriminator(glove, 50)
ad_model = am.adverse_model(discriminator)
res = ad_model.fit([dev[1][:samples], batchez[1]],
np.zeros(samples),
validation_split=0.1,
verbose=0,
nb_epoch=20,
callbacks=[EarlyStopping(patience=2)])
return np.min(res.history['val_loss'])
def init_adverserial_threshold_function(target_dir, ad_threshold, glove, dim, wi, hypo_len):
### too slow to use
discriminator = am.discriminator(glove, dim)
discriminator.load_weights(target_dir + '/adverse.weights')
padding_example = (['a'] * 25, ['a'] * 15, 'entailment')
def t_func(example, loss, cpred, ctrue):
if not ctrue:
return False
vec_ex = load_data.prepare_split_vec_dataset([example, padding_example], wi.index, True)
pred = discriminator.predict(vec_ex[1][:1])[0][0]
return pred > ad_threshold
return t_func
def filter_adverserial(dataset, threshold, target_size, target_dir, glove, dim):
arg = threshold[1] == 'b'
num = float(threshold[2:])
label_size = target_size / 3
discriminator = am.discriminator(glove, dim)
discriminator.load_weights(target_dir + '/adverse.weights')
ad_preds = discriminator.predict(dataset[1], verbose = 1)[:,0]
indices = (ad_preds < num) if arg else (ad_preds > num)
final_indices = []
for l in range(3):
label_args = dataset[2][:, l] == 1
final_indices += list(np.where(indices * label_args)[0][:label_size])
ind = np.sort(final_indices)
return (dataset[0][ind], dataset[1][ind], dataset[2][ind])