问题描述
我有一堆使用Keras编写的代码，这些代码是作为单独的pip安装安装的，并且导入语句的编写方式类似于from keras.models import Sequential等.
I have a bunch of code written using Keras that was installed as a separate pip install and the import statements are written like from keras.models import Sequential, etc..
在新机器上，我安装了Tensorflow，现在在 contrib 目录中包括Keras.为了保持版本的一致性，我认为最好使用 contrib 中的功能，而不是单独安装Keras，但这会导致一些导入问题.
On a new machine, I have Tensorflow installed which now includes Keras inside the contrib directory. In order to keep the versions consistent I thought it would be best to use what's in contrib instead of installing Keras separately, however this causes some import issues.
我可以使用import tensorflow.contrib.keras as keras导入Keras，但是做类似from tensorflow.contrib.keras.models import Sequential的操作会给出 ImportError:没有名为模型的模块，而from keras.models import Sequential提供了类似的 ImportError:没有名为keras的模块.型号.
I can import Keras using import tensorflow.contrib.keras as keras but doing something like from tensorflow.contrib.keras.models import Sequential gives ImportError: No module named models, and from keras.models import Sequential gives a similar ImportError: No module named keras.models.
是否有一种简单的方法来使from x.y import z语句起作用?如果不是这样，则意味着更改所有实例以使用冗长的命名(即m1 = keras.models.Sequential())，这不是我的首选语法，但是是可行的.
Is there a simple method to get the from x.y import z statements to work? If not it means changing all the instances to use the verbose naming (ie.. m1 = keras.models.Sequential()) which isn't my preferred syntax but is do-able.
推荐答案
尝试使用最新版本的tensorflow:
Try this with recent versions of tensorflow:
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import LSTM, TimeDistributed, Dense, ...
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