fixed softmax_cross_entropy return NaN value

mindarmour/diff_privacy/evaluation/membership_inference.py

@@ -82,7 +82,12 @@ def _softmax_cross_entropy(logits, labels):
     """
     labels = np.eye(logits.shape[1])[labels].astype(np.int32)
     logits = np.exp(logits) / np.sum(np.exp(logits), axis=1, keepdims=True)
-    return -1*np.sum(labels*np.log(logits), axis=1)
+    loss = -1*np.sum(labels*np.log(logits), axis=1)
+
+    nan_index = np.isnan(loss)
+    if np.any(nan_index):
+        loss[nan_index] = 0
+    return loss
 
 
 class MembershipInference:
@@ -243,6 +248,7 @@ class MembershipInference:
         np.random.shuffle(shuffle_index)
         features = features[shuffle_index]
         labels = labels[shuffle_index]
+
         return features, labels
 
     def _generate(self, dataset_x, label):