fix cross_entropy

paddlepalm/task_paradigm/cls.py

@@ -75,8 +75,8 @@ class TaskParadigm(task_paradigm):
                 name=scope_name+"cls_out_b", initializer=fluid.initializer.Constant(0.)))
 
         if self._is_training:
-            loss = fluid.layers.softmax_with_cross_entropy(
-                logits=logits, label=label_ids)
+            loss = fluid.layers.cross_entropy(
+                input=logits, label=label_ids)
             loss = layers.mean(loss)
             return {"loss": loss}
         else:

paddlepalm/task_paradigm/match.py

@@ -79,8 +79,8 @@ class TaskParadigm(task_paradigm):
                 initializer=fluid.initializer.Constant(0.)))
 
         if self._is_training:
-            ce_loss, probs = fluid.layers.softmax_with_cross_entropy(
-                logits=logits, label=labels, return_softmax=True)
+            ce_loss, probs = fluid.layers.cross_entropy(
+                input=logits, label=labels, return_softmax=True)
             loss = fluid.layers.mean(x=ce_loss)
             return {'loss': loss}
         else:

paddlepalm/task_paradigm/mlm.py

@@ -100,8 +100,8 @@ class TaskParadigm(task_paradigm):
             is_bias=True)
 
         if self._is_training:
-            mask_lm_loss = fluid.layers.softmax_with_cross_entropy(
-                logits=fc_out, label=mask_label)
+            mask_lm_loss = fluid.layers.cross_entropy(
+                input=fc_out, label=mask_label)
             loss = fluid.layers.mean(mask_lm_loss)
             return {'loss': loss}
         else:

paddlepalm/task_paradigm/mrc.py

@@ -100,9 +100,10 @@ class TaskParadigm(task_paradigm):
         start_logits, end_logits = fluid.layers.unstack(x=logits, axis=0)
 
         def _compute_single_loss(logits, positions):
-            """Compute start/end loss for mrc model"""
-            loss = fluid.layers.softmax_with_cross_entropy(
-                logits=logits, label=positions)
+            """Compute start/en
+            d loss for mrc model"""
+            loss = fluid.layers.cross_entropy(
+                input=logits, label=positions)
             loss = fluid.layers.mean(x=loss)
             return loss
 

paddlepalm/utils/reader_helper.py

@@ -22,8 +22,6 @@ from paddle import fluid
 
 
 def _check_and_adapt_shape_dtype(rt_val, attr, message=""):
-    print(rt_val)
-    print(attr)
     if not isinstance(rt_val, np.ndarray):
         rt_val = np.array(rt_val)
         assert rt_val.dtype != np.dtype('O'), "yielded data is not a valid tensor(number of elements on some dimension may differ)."