Remove loss_fn name

Author: Laicheng0830

setup.py

@@ -20,7 +20,7 @@
 
 MAJOR = 0
 MINOR = 5
-PATCH = 2
+PATCH = 3
 PRE_RELEASE = ''
 # Use the following formatting: (major, minor, patch, prerelease)
 VERSION = (MAJOR, MINOR, PATCH, PRE_RELEASE)

tensorlayerx/model/core.py

@@ -307,7 +307,7 @@ def tf_train(
                     val_loss, val_acc, n_iter = 0, 0, 0
                     for X_batch, y_batch in test_dataset:
                         _logits = network(X_batch)  # is_train=False, disable dropout
-                        val_loss += loss_fn(_logits, y_batch, name='eval_loss')
+                        val_loss += loss_fn(_logits, y_batch)
                         if metrics:
                             metrics.update(_logits, y_batch)
                             val_acc += metrics.result()
@@ -360,7 +360,7 @@ def ms_train(
                     val_loss, val_acc, n_iter = 0, 0, 0
                     for X_batch, y_batch in test_dataset:
                         _logits = network(X_batch)
-                        val_loss += loss_fn(_logits, y_batch, name='eval_loss')
+                        val_loss += loss_fn(_logits, y_batch)
                         if metrics:
                             metrics.update(_logits, y_batch)
                             val_acc += metrics.result()
@@ -414,7 +414,7 @@ def pd_train(
                     val_loss, val_acc, n_iter = 0, 0, 0
                     for X_batch, y_batch in test_dataset:
                         _logits = network(X_batch)  # is_train=False, disable dropout
-                        val_loss += loss_fn(_logits, y_batch, name='eval_loss')
+                        val_loss += loss_fn(_logits, y_batch)
                         if metrics:
                             metrics.update(_logits, y_batch)
                             val_acc += metrics.result()
@@ -468,7 +468,7 @@ def th_train(
                     val_loss, val_acc, n_iter = 0, 0, 0
                     for X_batch, y_batch in test_dataset:
                         _logits = network(X_batch)  # is_train=False, disable dropout
-                        val_loss += loss_fn(_logits, y_batch, name='eval_loss')
+                        val_loss += loss_fn(_logits, y_batch)
                         if metrics:
                             metrics.update(_logits, y_batch)
                             val_acc += metrics.result()

tensorlayerx/package_info.py

@@ -4,7 +4,7 @@
 
 MAJOR = 0
 MINOR = 5
-PATCH = 2
+PATCH = 3
 PRE_RELEASE = ''
 # Use the following formatting: (major, minor, patch, prerelease)
 VERSION = (MAJOR, MINOR, PATCH, PRE_RELEASE)