update example cross entropy API usage (#572)

Author: LiuChiachi

examples/text_generation/couplet/model.py

@@ -23,8 +23,8 @@ def __init__(self):
         super(CrossEntropyCriterion, self).__init__()
 
     def forward(self, predict, label, trg_mask):
-        cost = F.softmax_with_cross_entropy(
-            logits=predict, label=label, soft_label=False)
+        cost = F.cross_entropy(
+            input=predict, label=label, reduction='none', soft_label=False)
         cost = paddle.squeeze(cost, axis=[2])
         masked_cost = cost * trg_mask
         batch_mean_cost = paddle.mean(masked_cost, axis=[0])

examples/text_generation/couplet/train.py

@@ -14,10 +14,7 @@
 
 from args import parse_args
 
-import numpy as np
 import paddle
-import paddle.nn as nn
-import paddle.nn.functional as F
 from paddlenlp.metrics import Perplexity
 
 from data import create_train_loader

examples/text_generation/vae-seq2seq/model.py

@@ -40,8 +40,8 @@ def forward(self, kl_loss, dec_output, trg_mask, label):
         self.update_kl_weight()
         self.kl_loss = kl_loss
 
-        rec_loss = F.softmax_with_cross_entropy(
-            logits=dec_output, label=label, soft_label=False)
+        rec_loss = F.cross_entropy(
+            input=dec_output, label=label, reduction='none', soft_label=False)
 
         rec_loss = paddle.squeeze(rec_loss, axis=[2])
         rec_loss = rec_loss * trg_mask
@@ -117,7 +117,9 @@ def __init__(self, ppl, nll, log_freq=200, verbose=2):
 
     def on_train_begin(self, logs=None):
         super(TrainCallback, self).on_train_begin(logs)
-        self.train_metrics = ["loss", "ppl", "nll", "kl weight", "kl loss", "rec loss"]
+        self.train_metrics = [
+            "loss", "ppl", "nll", "kl weight", "kl loss", "rec loss"
+        ]
 
     def on_epoch_begin(self, epoch=None, logs=None):
         super(TrainCallback, self).on_epoch_begin(epoch, logs)