Doc(Transfer learning and fine-tuning) is quite different from real executive result.
@lida2003,
Could you please confirm whether the difference in Accuracy & Loss has happened in GPU/CPU with both tensorflow v2.15, v2.16? Also I will also try to debug more on this issue and provide the resolution. Thank you! > @lida2003,
> Could you please confirm whether the difference in Accuracy & Loss has happened in GPU/CPU with both tensorflow v2.15, v2.16? 


- NVIDIA 2.15.0+nv24.03 GPU version faild 100%
- Colab is consistent with tensorflow document (But I don't know the version)

PS: Jetson Orin Nano 8GB, CPU&GPU shared memory



> v2.16

Well, on the very begining, I have installed (pip binary installation) 2.16 CPU version on Jetson Orin Nano. Runing [Keras-Fine-Tuning-Pre-Trained-Models](https://github.com/spmallick/learnopencv/tree/master/Keras-Fine-Tuning-Pre-Trained-Models) without any resource warning. It might be the way CPU using swap area.

The result is also different from the document, See link below:

- [Multiple executive warnings after switching tensorflow from 2.16.1 CPU to v60dp tensorflow==2.15.0+nv24.03 GPU version](https://forums.developer.nvidia.com/t/multiple-executive-warnings-after-switching-tensorflow-from-2-16-1-cpu-to-v60dp-tensorflow-2-15-0-nv24-03-gpu-version/291208/4)

When I switched to NVIDIA 2.15.0+nv24.03 GPU version: [Tensorflow v2.16.1 GPU version local build on Jetson Orin Nano failed](https://forums.developer.nvidia.com/t/tensorflow-v2-16-1-gpu-version-local-build-on-jetson-orin-nano-failed/291195/9)

- [The resource issue, we have confirmed with NVIDIA.](https://forums.developer.nvidia.com/t/multiple-executive-warnings-after-switching-tensorflow-from-2-16-1-cpu-to-v60dp-tensorflow-2-15-0-nv24-03-gpu-version/291208)

> Also I will also try to debug more on this issue and provide the resolution. Thank you!

There are also a copule of other things might be a clue for you. Here is a link on NVIDIA forum: 
- [Tensorflow v2.16.1 GPU version local build on Jetson Orin Nano failed](https://forums.developer.nvidia.com/t/tensorflow-v2-16-1-gpu-version-local-build-on-jetson-orin-nano-failed/291195/7)
- [Multiple executive warnings after switching tensorflow from 2.16.1 CPU to v60dp tensorflow==2.15.0+nv24.03 GPU version](https://forums.developer.nvidia.com/t/multiple-executive-warnings-after-switching-tensorflow-from-2-16-1-cpu-to-v60dp-tensorflow-2-15-0-nv24-03-gpu-version/291208/4)

Please take a look at those warnings and memory issue. I think we need a sanity check before software is packed for release (put on repo).


EDIT: Keep sync with NVIDIA feedback.
- [NVIDIA claimed their binary compiled following tensorflow guide.](https://forums.developer.nvidia.com/t/how-to-compile-tensorflow-for-jetson-orin-nano/291321/7)
- [Those warnings seems OK with NVIDIA 2.15 binary version.](https://forums.developer.nvidia.com/t/inconsistency-of-nvidia-2-15-0-nv24-03-v-s-colab-v-s-tensorflow-documentation/291613/4)
- [Inconsistency of NVIDIA 2.15.0+nv24.03 v.s. Colab v.s. Tensorflow Documentation](https://forums.developer.nvidia.com/t/inconsistency-of-nvidia-2-15-0-nv24-03-v-s-colab-v-s-tensorflow-documentation/291613)






 @lida2003,
As you mentioned, I tried to execute the transfer learning code on the colab with the Tensorflow v2.16, v2.15 and observed that the loss and Accuracy is the same as the document. I suspect there might be an issue with the GPU. I will re-check and deep dive on the same. 
![image](https://github.com/tensorflow/tensorflow/assets/81610181/e1c45818-1619-4ddf-9d52-f4639bdac90d)


Thank you!
### Issue type

Support

### Have you reproduced the bug with TensorFlow Nightly?

No

### Source

binary

### TensorFlow version

2.15.0+nv24.03 GPU version, check this link: https://forums.developer.nvidia.com/t/multiple-executive-warnings-after-switching-tensorflow-from-2-16-1-cpu-to-v60dp-tensorflow-2-15-0-nv24-03-gpu-version/291208

### Custom code

No

### OS platform and distribution

Jetson Orin Nano ubuntu 22.04 Jammy

### Mobile device

_No response_

### Python version

3.10.12

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

CUDA12.2.140/cuDNN8.9.4.25

### GPU model and memory

sm90 8GB

### Current behavior?

The executive result (trend of curve and abosolute value) is quite different from document.

- My result: [transfer_learning.zip](https://github.com/tensorflow/tensorflow/files/15162333/transfer_learning.zip)

![图片](https://github.com/tensorflow/tensorflow/assets/1296171/708f72e2-30f4-4033-9e2b-d064886db4ff)

- Document (https://github.com/tensorflow/docs/blob/master/site/en/tutorials/images/transfer_learning.ipynb) says:

![图片](https://github.com/tensorflow/tensorflow/assets/1296171/99489450-da99-4529-a599-516682bba858)




![图片](https://github.com/tensorflow/tensorflow/assets/1296171/7834ab25-25ae-4443-8775-869905cf7d01)


### Standalone code to reproduce the issue

```shell
100% reproducable
```


### Relevant log output

Also tried Colab, which is consistent with documentation:

![图片](https://github.com/tensorflow/tensorflow/assets/1296171/5b474c91-805c-4d00-8158-294981a8fc0f)
