diff --git a/mindarmour/diff_privacy/evaluation/membership_inference.py b/mindarmour/diff_privacy/evaluation/membership_inference.py
index 9378fa54dd36ed0a68fa150486aaea5b32d26b0a..5e7f152b848961f1c093afaad6449ec99d909836 100755
--- a/mindarmour/diff_privacy/evaluation/membership_inference.py
+++ b/mindarmour/diff_privacy/evaluation/membership_inference.py
@@ -20,8 +20,6 @@ import numpy as np
 import mindspore as ms
 from mindspore.train import Model
 from mindspore.dataset.engine import Dataset
-import mindspore.nn as nn
-import mindspore.context as context
 from mindspore import Tensor
 from mindarmour.diff_privacy.evaluation.attacker import get_attack_model
 from mindarmour.utils.logger import LogUtil
@@ -71,6 +69,22 @@ def _eval_info(pred, truth, option):
     raise ValueError(msg)
 
 
+def _softmax_cross_entropy(logits, labels):
+    """
+    Calculate the SoftmaxCrossEntropy result between logits and labels.
+
+    Args:
+        logits (numpy.ndarray): Numpy array of shape(N, C).
+        labels (numpy.ndarray): Numpy array of shape(N, )
+
+    Returns:
+        numpy.ndarray: Numpy array of shape(N, ), containing loss value for each vector in logits.
+    """
+    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)
+
+
 class MembershipInference:
     """
     Evaluation proposed by Shokri, Stronati, Song and Shmatikov is a grey-box attack.
@@ -192,8 +206,8 @@ class MembershipInference:
             raise TypeError(msg)
 
         metrics = set(metrics)
-        metrics_list = {"precision", "accruacy", "recall"}
-        if metrics > metrics_list:
+        metrics_list = {"precision", "accuracy", "recall"}
+        if not metrics <= metrics_list:
             msg = "Element in 'metrics' must be in {}, but got {}.".format(metrics_list, metrics)
             LOGGER.error(TAG, msg)
             raise ValueError(msg)
@@ -244,19 +258,12 @@ class MembershipInference:
                 N is the number of sample. C = 1 + dim(logits).
             - numpy.ndarray, Labels for each sample, Shape is (N,).
         """
-        if context.get_context("device_target") != "Ascend":
-            msg = "The target device must be Ascend, " \
-                  "but current is {}.".format(context.get_context("device_target"))
-            LOGGER.error(TAG, msg)
-            raise RuntimeError(msg)
         loss_logits = np.array([])
         for batch in dataset_x.create_dict_iterator():
             batch_data = Tensor(batch['image'], ms.float32)
-            batch_labels = Tensor(batch['label'], ms.int32)
-            batch_logits = self.model.predict(batch_data)
-            loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, is_grad=False, reduction=None)
-            batch_loss = loss(batch_logits, batch_labels).asnumpy()
-            batch_logits = batch_logits.asnumpy()
+            batch_labels = batch['label'].astype(np.int32)
+            batch_logits = self.model.predict(batch_data).asnumpy()
+            batch_loss = _softmax_cross_entropy(batch_logits, batch_labels)
 
             batch_feature = np.hstack((batch_loss.reshape(-1, 1), batch_logits))
             if loss_logits.size == 0: