重装pip:解决No module named ‘pip._internal‘

本文介绍如何在Windows下遇到pip升级问题时,通过使用`curl`下载并执行get-pip.py来强制重装pip,确保软件更新顺利进行。

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更新pip时,系统先删除旧版本,然后下载新版本。如果中途出错,则新版下载失败。pip软件缺失,无法继续下载,感觉十分无语。

可通过强制重装解决:

cmd界面输入
curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
python get-pip.py --force-reinstall

解释:
windows下curl(client url)向目标网址+文件请求下载文件get-pip.py
-o参数将服务器的回应保存成文件
然后通过python运行get-pip.py文件,完成pip重装

参考:https://blog.youkuaiyun.com/wangweiwells/article/details/88374070

AttributeError: module 'tensorflow._api.v2.compat.v2.__internal__' has no attribute 'register_load_context_function'. Did you mean: 'register_call_context_function'? PS D:\Learn\Keras-GAN-master> ^C PS D:\Learn\Keras-GAN-master> ^C PS D:\Learn\Keras-GAN-master> & D:/Anaconda3/envs/tf1cpu/python.exe d:/Learn/Keras-GAN-master/context_encoder/context_encoder.py 2025-03-19 15:44:14.482172: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. 2025-03-19 15:44:15.713157: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. Traceback (most recent call last): File "d:\Learn\Keras-GAN-master\context_encoder\context_encoder.py", line 7, in <module> from keras.layers.advanced_activations import LeakyReLU ModuleNotFoundError: No module named 'keras.layers.advanced_activations' PS D:\Learn\Keras-GAN-master> ^C 修改代码吧 from keras.datasets import cifar10 from keras.layers import Input, Dense, Reshape, Flatten, Dropout, multiply, GaussianNoise from keras.layers import BatchNormalization, Activation, Embedding, ZeroPadding2D from keras.layers import MaxPooling2D from keras.layers.advanced_activations import LeakyReLU from keras.layers.convolutional import UpSampling2D, Conv2D from keras.models import Sequential, Model from keras.optimizers import Adam from keras import losses from keras.utils import to_categorical import keras.backend as K import matplotlib.pyplot as plt import numpy as np class ContextEncoder(): def __init__(self): self.img_rows = 32 self.img_cols = 32 self.mask_height = 8 self.mask_width = 8
03-20
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