Tensorflow 2.16.0+在PyCharm中找不到keras的报错解决

在PyCharm(2024.2版本)中,直接使用from tensorflow import keras会提示"Cannot find reference 'keras' in 'init .py' ",找不到keras,如下图所示。

查阅相关资料,可以发现在tf2.16之后,默认的keras后端升级为了3.0,且直接引用即可。

参考链接:PyCharm cannot parse any content under tensorflow.keras 2.16.2 #73110

以及:Introducing Keras 3.0

TF 2.15: Keras2.0

python 复制代码
import tensorflow as tf
from tensorflow import keras

TF 2.16: Keras 3.0

python 复制代码
import tensorflow as tf
import keras

reddit上也有人讨论:

Because in the old days Keras was entirely separate to Tensorflow. It had a backend for Theano and maybe some other stuff. Then Francois and the TF team at Google decided to bring Keras into TF as their main high level API, trying to address the fragmentation that had been created in TF land. Since TF usage is dwindling in research, and possibly showing signs of similar in industry, Keras is now multi-backend again, supporting TensorFlow, PyTorch, and JAX.

  1. Very old code will import keras directly, and be referring to Keras 1. This code will usually use > Theano or TensorFlow 1.x as well.
  2. More recent code will import tensorflow.keras and be referring to Keras 2. This is the majority of the code you'll find.
  3. Very recent code (i.e. this year I think) will import keras directly again, or maybe keras_core, and be referring to Keras 3, the latest version.

也就是说,在最新版(2.16.0+)的Tensorflow中,只需要直接引用import keras即可正常使用keras3.0。经测试,使用keras.modelskeras.layers均可正常使用,也会有代码补全。

相关推荐
shughui1 天前
Miniconda下载、安装、关联配置 PyCharm(2026最新图文教程)
ide·python·pycharm·miniconda
FriendshipT1 天前
算法部署知识点:TensorRT、Tensorflow、Flask、Docker、TFLite
算法·docker·flask·tensorflow
chushiyunen1 天前
pycharm创建桌面时间控件小程序
ide·小程序·pycharm
郑同学zxc1 天前
机器学习17-tensorflow2 线性代数
线性代数·机器学习·tensorflow
在屏幕前出油2 天前
02. FastAPI——路由
服务器·前端·后端·python·pycharm·fastapi
墨染天姬2 天前
【AI】TensorFlow 框架
人工智能·python·tensorflow
AI+程序员在路上2 天前
在pyCharm 中命令打包生成exe文件方法
ide·python·pycharm
爱打代码的小林2 天前
识别盒装图标项目的一些功能函数
python·pycharm
在屏幕前出油2 天前
00. FastAPI——了解FastAPI
后端·python·pycharm·fastapi
技术小黑2 天前
TensorFlow学习系列07 | 实现咖啡豆识别
人工智能·学习·tensorflow