def generate(self, init_states):
init_states = [dy.nobackprop(s) for s in init_states]
states = self.lstm.initial_state(init_states)
result = [None]
while result[-1] != vocabulary.STOP_I:
unormalized_distribution = W * state.output() + b
vec = unormalized_distribution.value()
prediction = np.argmax(dis_vec)
result.append(prediction)
states.add_input(self.lu[prediction])
return "".join(result)
def get_w_repr(self, word, train=False, update=True):
"""
Get representation of word (word embedding)
"""
if train:
if self.w_dropout_rate > 0.0:
w_id = self.w2i[UNK] if drop(word, self.wcount, self.w_dropout_rate) else self.w2i.get(word, self.w2i[UNK])
else:
if self.mimickx_model_path: # if given use MIMICKX
if word not in self.w2i: #
#print("predict with MIMICKX for: ", word)
return dynet.inputVector(self.mimickx_model.predict(word).npvalue())
w_id = self.w2i.get(word, self.w2i[UNK])
if not update:
return dynet.nobackprop(self.wembeds[w_id])
else:
return self.wembeds[w_id]
def get_w_repr(self, word, train=False, update=True):
"""
Get representation of word (word embedding)
"""
if train:
if self.w_dropout_rate > 0.0:
w_id = self.w2i[UNK] if drop(word, self.wcount, self.w_dropout_rate) else self.w2i.get(word, self.w2i[UNK])
else:
if self.mimickx_model_path: # if given use MIMICKX
if word not in self.w2i: #
#print("predict with MIMICKX for: ", word)
return dynet.inputVector(self.mimickx_model.predict(word).npvalue())
w_id = self.w2i.get(word, self.w2i[UNK])
if not update:
return dynet.nobackprop(self.wembeds[w_id])
else:
return self.wembeds[w_id]
def generator_train(self, example):
text = example.sentence
coded_text = self.vocabulary.code_chars(text)
input = self.get_input(example, training=True, do_not_renew=False, backprop=True)
input_noback = dy.nobackprop(input)
real_loss = self.generator.train_real(input_noback, coded_text)
fake_loss = - self.generator.train_fake(input, coded_text)
fake_loss.backward()
self.generator.zero_gradient()
self.trainer.update()
return real_loss.value()
def discriminator_train(self, example):
real_labels = example.get_aux_labels()
n_labels = self.adversary_classifier.output_size()
fake_labels = set([i for i in range(n_labels) if i not in real_labels])
input = self.get_input(example, training=True, do_not_renew=False, backprop=True)
input_noback = dy.nobackprop(input)
real_loss = self.discriminator.train_real(input_noback, real_labels)
fake_loss = self.discriminator.train_fake(input, fake_labels)
fake_loss.backward()
self.discriminator.zero_gradient()
self.trainer.update()
return real_loss.value()
def get_input(self, example, training, do_not_renew=False, backprop=True):
encoding = self._get_input(example, training, do_not_renew, backprop)
if backprop:
return encoding
else:
return dy.nobackprop(encoding)