def generative_predict(test_model, word_index, batch, embed_indices, class_indices, batch_size = 64, prem_len = 22,
hypo_len = 12):
prem, _, _ = load_data.prepare_split_vec_dataset(batch, word_index)
padded_p = load_data.pad_sequences(prem, maxlen=prem_len, dim = -1)
core_model, premise_func, noise_func = test_model
premise = premise_func(padded_p)
noise = noise_func(embed_indices, load_data.convert_to_one_hot(class_indices, 3))
core_model.reset_states()
core_model.nodes['attention'].set_state(noise)
word_input = np.zeros((batch_size, 1))
result = []
for i in range(hypo_len):
data = {'premise' :premise,
'creative': noise,
'hypo_input': word_input,
'train_input': np.zeros((batch_size,1))}
preds = core_model.predict_on_batch(data)['output']
result.append(preds)
word_input = np.argmax(preds, axis=2)
result = np.transpose(np.array(result)[:,:,-1,:], (1,0,2))
return result
def single_generate(premise, label, gen_test, beam_size, hypo_len, noise_size, noise_input = None):
batch_size = gen_test[0].input_layers[0].input_shape[0]
per_batch = batch_size / beam_size
premises = [premise] * per_batch
if noise_input is None:
noise_input = np.random.normal(scale=0.11, size=(per_batch, 1, noise_size))
class_indices = np.ones(per_batch) * label
class_indices = load_data.convert_to_one_hot(class_indices, 3)
words, loss = generative_predict_beam(gen_test, premises, noise_input,
class_indices, True, hypo_len)
return words
def generation_predict_embed(model, word_index, batch, embed_indices, batch_size = 64, hs = True, class_indices = None):
prem, hypo, y = load_data.prepare_split_vec_dataset(batch, word_index)
X_p = load_data.pad_sequences(prem, maxlen=PREM_LEN, dim = -1)
data = {'premise_input': X_p, 'embed_input': embed_indices[:,None]}
if class_indices is not None:
C = load_data.convert_to_one_hot(class_indices, 3)
data['class_input'] = C
if hs:
data['train_input'] = np.zeros((batch_size, HYPO_LEN))
model_pred = model.predict_on_batch(data)
return model_pred['output']
def print_hypos(premise, label, gen_test, beam_size, hypo_len, noise_size, wi):
words = single_generate(premise, label, gen_test, beam_size, hypo_len, noise_size)
batch_size = gen_test[0].input_layers[0].input_shape[0]
per_batch = batch_size / beam_size
premises = [premise] * per_batch
noise_input = np.random.normal(scale=0.11, size=(per_batch, 1, noise_size))
class_indices = np.ones(per_batch) * label
class_indices = load_data.convert_to_one_hot(class_indices, 3)
words, loss = generative_predict_beam(gen_test, premises, noise_input,
class_indices, True, hypo_len)
print 'Premise:', wi.print_seq(premise)
print 'Label:', load_data.LABEL_LIST[label]
print
print 'Hypotheses:'
for h in words:
print wi.print_seq(h)
def print_hypos(premise, label, gen_test, beam_size, hypo_len, noise_size, wi):
words = single_generate(premise, label, gen_test, beam_size, hypo_len, noise_size)
batch_size = gen_test[0].input_layers[0].input_shape[0]
per_batch = batch_size / beam_size
premises = [premise] * per_batch
noise_input = np.random.normal(scale=0.11, size=(per_batch, 1, noise_size))
class_indices = np.ones(per_batch) * label
class_indices = load_data.convert_to_one_hot(class_indices, 3)
words, loss = generative_predict_beam(gen_test, premises, noise_input,
class_indices, True, hypo_len)
print 'Premise:', wi.print_seq(premise)
print 'Label:', load_data.LABEL_LIST[label]
print
print 'Hypotheses:'
for h in words:
print wi.print_seq(h)
def single_generate(premise,
label,
gen_test,
beam_size,
hypo_len,
noise_size,
noise_input=None):
batch_size = gen_test[0].input_layers[0].input_shape[0]
per_batch = batch_size / beam_size
premises = [premise] * per_batch
if noise_input is None:
noise_input = np.random.normal(scale=0.11,
size=(per_batch, 1, noise_size))
class_indices = np.ones(per_batch) * label
class_indices = load_data.convert_to_one_hot(class_indices, 3)
words, loss = generative_predict_beam(gen_test, premises, noise_input,
class_indices, True, hypo_len)
return words
def get_explanation(premise, label, gen_test, beam_size, hypo_len, noise_size, wi):
words = single_generate(premise, label, gen_test, beam_size, hypo_len, noise_size)
batch_size = gen_test[0].input_layers[0].input_shape[0]
per_batch = batch_size / beam_size
premises = [premise] * per_batch
noise_input = np.random.normal(scale=0.11, size=(per_batch, 1, noise_size))
class_indices = np.ones(per_batch) * label
class_indices = load_data.convert_to_one_hot(class_indices, 3)
words, loss = generative_predict_beam(gen_test, premises, noise_input,
class_indices, True, hypo_len)
# sentence = wi.print_seq(premise)
# print 'Label:', load_data.LABEL_LIST[label]
# print
explanation = ''
for h in words:
explanation = wi.print_seq(h)
return explanation.encode('utf-8')
