More fix (#68)

* More fix

* Add decorator to pow

keras/backend/mxnet_backend.py

@@ -12,6 +12,7 @@
 _MODEL = None
 _REENTRY = False
 
+placeholder_name_dict = dict()
 set_image_dim_ordering('th')
 
 
@@ -419,6 +420,7 @@ def __gt__(self, other):
                     lhs=self.symbol,
                     rhs=other.symbol))
 
+    @keras_symbol_child
     def __pow__(self, power, modulo=None):
         return KerasSymbol(self.symbol.__pow__(power), neighbor=[self])
 
@@ -526,6 +528,13 @@ def placeholder(shape=None, ndim=None, dtype=None, sparse=False, name=None):
     dtype = np.dtype(dtype)
     if name is None:
         name = _autogen_name('placeholder')
+    elif name in placeholder_name_dict:
+        placeholder_name_dict[name] += 1
+        name = name + '_' + str(placeholder_name_dict[name] - 1)
+        placeholder_name_dict[name] = 1 if name not in placeholder_name_dict \
+                                        else placeholder_name_dict[name] + 1
+    else:
+        placeholder_name_dict[name] = 1
     if not shape:
         if ndim:
             shape = tuple([0 for _ in range(ndim)])
@@ -2516,10 +2525,14 @@ def sparse_categorical_crossentropy(output, target, from_logits=False):
     """Categorical crossentropy between an output tensor
     and a target tensor, where the target is an integer tensor.
     """
-    output = mx.sym.softmax_cross_entropy(output.symbol, target.symbol)
+    assert not from_logits
+    target = KerasSymbol(mx.sym.one_hot(flatten(target).symbol, output.shape[1]))
+    axis = ndim(output) - 1
+    output = output.symbol
+    output = mx.sym.clip(output, a_min=_EPSILON, a_max=1. - _EPSILON)
+    output = - mx.sym.sum(target.symbol * mx.sym.log(output), axis=axis)
     return KerasSymbol(output)
 
-
 @keras_symbol_child
 def binary_crossentropy(output, target, from_logits=False):
     """Binary crossentropy between an output tensor and a target tensor.

keras/engine/training.py

@@ -1848,7 +1848,7 @@ def str2context(s):
             self._test_sym = test_keras_symbol.symbol
             self._ntest = len(self.metrics_tensors) + 1
 
-            pred_keras_symbol = K.group(self.outputs + state_updates)
+            pred_keras_symbol = K.group(self.outputs + [symbol for symbol in state_updates if symbol not in self.outputs])
             bind_values.update(K.dfs_get_bind_values(pred_keras_symbol))
             self._pred_sym = pred_keras_symbol.symbol
             self._npred = len(self.outputs)