遇到ModuleNotFoundError: No module named 'dataset.mnist' 错误时,通常意味着 Python 无法在你的环境中找到名为 dataset.mnist 的模块。这个问题可能由多种原因引起,以下是一些可能的解决方案: 确认模块来源: 首先,你需要确认 dataset.mnist 模块是第三方库的一部分,还是项目内部自定义的模块。 如果是第三方库,...
ModuleNotFoundError Traceback (most recent call last) in () ---> 1 from mnist import MNIST 2 data = MNIST(data_dir="data/MNIST/") ModuleNotFoundError: No module named 'mnist' 👍1danvargg reacted with thumbs up emoji 👍 RTFM...
将dataset文件复制到anaconda>Lib>site-packages文件夹里,再将sys.path.append(os.pardir)改为sys.path.append(os.getcwd())
机器学习在用到mnist数据集报错No module named 'tensorflow.examples.tutorials'解决办法 检查一下安装有tensorflow包的目录下的examples这个文件夹。 每个人的文件路径是不同的,我的在...\Python3\Lib\site-packages,该目录下有文件夹tensorflow, tensorflow_core, tensorflow_estimator等文件夹。进入tensorflow文件夹,里...
from future import print_function, division import scipy from keras.datasets import mnist from keras_contrib.layers.normalization import InstanceNormalization from keras.layers import Input, Dense, Reshape, Flatten, Dropout, Concatenate ...
from tensorflow.examples.tutorials.mnist import input_data 出错,在使用tensorflow实现辨别手写体的过程中遇到了一下错误那么你则可以看下你当前环境下的TensorFlow的example中是否有tutorials文件或是否有example文件夹进入后如果发现,没有则去此链接中进行下载,提取
Python 使用中出现错误:ImportError: No module named _sqlite3 2019-12-19 18:32 −解决办法: 1、先安装sqlite3 从sqlite官网:https://www.sqlite.org/download.html 上下载linux环境下的安装包:sqlite-autoconf-3190300.tar.gz ... 51运维com
python3 mnist_main.py \ --model_dir=$MODEL_DIR \ --data_dir=$DATA_DIR \ --train_epochs=10 \ --distribution_strategy=one_device \ --num_gpus=$NUM_GPUS \ --download but gets the same error message ModuleNotFoundError: No module named 'official' Here is the full error message ...
ImportError: No module named examples.tutorials.mnist 安装的tensorflow升级版本后就OK了, 0.8.0 -> 0.9.0 sudo pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.9.0rc0-cp27-none-linux_x86_64.whl sudo pip install --upgrade https://storage.googleapis.com/tenso...
No module named 'compat' Describe the expected behavior Contributing - Do you want to contribute a PR? (yes/no): - Briefly describe your candidate solution (if contributing): Standalone code to reproduce the issue Provide a reproducible test case that is the bare minimum necessary to generate...