!464 update hyperlink of resnet50 in benchmark

Merge pull request !464 from guoqi/master

docs/source_en/benchmark.md

@@ -16,7 +16,7 @@ For details about the MindSpore pre-trained model, see [Model Zoo](https://gitee
 |  |  |  |  | Ascend: 16 * Ascend 910 </br> CPU：384 Cores | Mixed | 256 | 32768 images/sec | 0.96 |
 
 1. The preceding performance is obtained based on ModelArts, the HUAWEI CLOUD AI development platform. It is the average performance obtained by the Ascend 910 AI processor during the overall training process. 
-2. For details about other open source frameworks, see [ResNet-50 v1.5 for TensorFlow](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/Classification/RN50v1.5#nvidia-dgx-2-16x-v100-32g).
+2. For details about other open source frameworks, see [ResNet-50 v1.5 for TensorFlow](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/Classification/ConvNets/resnet50v1.5).
 
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docs/source_zh_cn/benchmark.md

@@ -15,7 +15,7 @@
 |  |  |  |  | Ascend: 16 * Ascend 910 </br> CPU：384 Cores | Mixed | 256 | 32768 images/sec | 0.96 |
 
 1. 以上数据基于华为云AI开发平台ModelArts测试获得，是训练过程整体下沉至Ascend 910 AI处理器执行所得的平均性能。
-2. 业界其他开源框架数据可参考：[ResNet-50 v1.5 for TensorFlow](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/Classification/RN50v1.5#nvidia-dgx-2-16x-v100-32g)。
+2. 业界其他开源框架数据可参考：[ResNet-50 v1.5 for TensorFlow](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/Classification/ConvNets/resnet50v1.5)。
 
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