def choice_label_by_max(self, logits):
logits_reshaped = np.reshape(logits, newshape=[2, int(logits.shape[-1] / 2)])
for i in range(int(logits.shape[-1] / 2)):
logits_reshaped[:, i] = softmax(logits_reshaped[:, i])
logits_diff = logits_reshaped[0, :] - logits_reshaped[1, :]
label = np.argmax(logits_diff)
return [label]
def _make_single_loss_weight(self, loss_weight_list):
loss_weight = []
sum_weight = sum(loss_weight_list)
for i in range(len(loss_weight_list)):
loss_weight.append(
math.log(sum_weight / max(loss_weight_list[i], 1)))
loss_weight = softmax(loss_weight)
return tf.constant(loss_weight, dtype=tf.float32)
def _make_double_loss_weight(self, loss_weight_list):
loss_weight = []
sum_weight = sum(loss_weight_list)
loss_weight_list_ = [sum_weight - lw for lw in loss_weight_list]
for i in range(len(loss_weight_list)):
tmp_loss_weight = softmax([math.log(sum_weight / max(loss_weight_list[i], 1)),
math.log(sum_weight / max(loss_weight_list_[i], 1))])
loss_weight.append(tmp_loss_weight.tolist())
return tf.constant(loss_weight, dtype=tf.float32)
def _random_pick(self, word_prob):
words, probs = zip(*word_prob)
probs = softmax(np.array(probs)).tolist()
x = uniform(0, 1)
cum_prob = 0.
result = None
for item, prob in zip(words, probs):
cum_prob += prob
if x < cum_prob:
result = item
break
return result
def predict(self, x: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
if len(x) == 0:
return np.asarray([], dtype=np.int32), np.asarray([], dtype=np.float32)
outputs = self.sess.run(self._output, feed_dict={self._inputs: x})
outputs_cls = np.argmax(outputs, axis=1)
return outputs_cls, softmax(outputs)
