opt rec_att_head
@@ -64,8 +64,10 @@ class AttentionHead(nn.Layer):
(outputs, hidden), alpha = self.attention_cell(hidden, inputs,
char_onehots)
probs_step = self.generator(outputs)
- probs = paddle.unsqueeze(
- probs_step, axis=1) if probs is None else paddle.concat(
+ if probs is None:
+ probs = paddle.unsqueeze(probs_step, axis=1)
+ else:
+ probs = paddle.concat(
[probs, paddle.unsqueeze(
probs_step, axis=1)], axis=1)
next_input = probs_step.argmax(axis=1)
@@ -152,8 +154,10 @@ class AttentionLSTM(nn.Layer):
char_onehots)
probs_step = self.generator(hidden[0])
hidden = (hidden[1][0], hidden[1][1])
- probs = paddle.unsqueeze(
- probs_step, axis=1) if probs is None else paddle.concat(
+ if probs is None:
+ probs = paddle.unsqueeze(probs_step, axis=1)
+ else:
+ probs = paddle.concat(
[probs, paddle.unsqueeze(
probs_step, axis=1)], axis=1)
