I am using different version – following Install on Windows with Docker here – and had similar problem.

An easy workaround I’ve found was:

1.Into the Linux command line, figure out where is the input_data.py on my Docker image (in your case you mentionned that you had to download it manually. In my case, it was already here). I used the follwing linux command:

$ sudo find . -print | grep -i '.*[.]py'

I’ve got the files & path

./tensorflow/g3doc/tutorials/mnist/mnist.py
./tensorflow/g3doc/tutorials/mnist/input_data.py

2.launch Python and type the following command using SYS:

>> import sys
>> print(sys.path)

you will get the existing paths.

['', '/usr/lib/python2.7', '/usr/lib/python2.7/plat-x86_64-linux-gnu', '/usr/lib/python2.7/lib-tk', '/usr/lib/python2.7/lib-old', '/usr/lib/python2.7/lib-dynload', '/usr/local/lib/python2.7/dist-packages', '/usr/lib/python2.7/dist-packages', '/usr/lib/python2.7/dist-packages/PILcompat']

4.add the path of inputa_data.py:

>> sys.path.insert(1,'/tensorflow/tensorflow/g3doc/tutorials/mnist')

Hope that it can help. If you found better option, let me know. 🙂

How can I start the tutorial

I didn’t download the folder you did but I installed tensorflow by pip and then I had similar problem.

My workaround was to replace

import tensorflow.examples.tutorials.mnist.input_data

with

import tensorflow.examples.tutorials.mnist.input_data as input_data

The old tutorial said, to import the MNIST data, use:

import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

This will cause the error.
The new tutorial uses the following code to do so:

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data", one_hot=True)

And this works well.

I might be kinda late, but for tensorflow version 0.12.1, you might wanna use input_data.read_data_sets instead.

Basically using this function to load the data from your local drive that you had downloaded from http://yann.lecun.com/exdb/mnist/.

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('data_set/')

cd your_mnist_dir &&
wget https://github.com/HIPS/hypergrad/raw/master/data/mnist/mnist_data.pkl &&
wget https://github.com/HIPS/hypergrad/raw/master/data/mnist/t10k-images-idx3-ubyte.gz &&
wget https://github.com/HIPS/hypergrad/raw/master/data/mnist/t10k-labels-idx1-ubyte.gz &&
wget https://github.com/HIPS/hypergrad/raw/master/data/mnist/train-images-idx3-ubyte.gz &&
wget https://github.com/HIPS/hypergrad/raw/master/data/mnist/train-labels-idx1-ubyte.gz

MNIST input_data was built-in, it’s just not a individual module, it’s inside Tensorflow module, try

from tensorflow.examples.tutorials.mnist import input_data

MNIST data set included as a part of tensorflow examples tutorial, If we want to use this :

Import MNIST data to identify handwritten digites

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST data", one_hot=True)

As TensorFlow official website shown, All MNIST data is hosted on http://yann.lecun.com/exdb/mnist/

For TensorFlow API 2.0 the mnist data changed place to: tf.keras.datasets.mnist.load_data

There’s now a much easier way to load MNIST data into tensorflow without having to download the data by using Tensorflow 2 and Tensorflow Datasets

To get started, make sure you import Tensorflow and specify the 2nd version:

%tensorflow_version 2.x
import tensorflow as tf

Then load the data into a dictionary using the following code:

MNIST_data = tfds.load(name = "mnist")

and Then split the data into train and test:

train, test = MNIST_data['train'] , MNIST_data['test']

Now you can use these data generators however you like.

For Tensorflow API above 2.0, to use MNIST dataset following command can be used,

import tensorflow_datasets as tfds
data = tfds.load(name = "mnist")

The following steps work perfectly in my Notebook:

step 1 : get Python files from github :
!git clone https://github.com/tensorflow/tensorflow.git

step 2 : append these files in my Python path :

import sys

sys.path.append('/content/tensorflow/tensorflow/examples/tutorials/mnist')

step 3 : load the MNIST data with ‘input_data’ fonction

import input_data

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

That’s all !

If you’re using Tensorflow 2.0 or higher, you need to install tensorflow_datasets first:

pip install tensorflow_datasets

or if you’re using an Anaconda distribution:

conda install tensorflow_datasets

from the command line.

If you’re using a Jupyter Notebook you will need to install and enable ipywidgets. According to the docs (https://ipywidgets.readthedocs.io/en/stable/user_install.html) using pip:

pip install ipywidgets
jupyter nbextension enable --py widgetsnbextension

If you’re using an Anaconda distribution, install ipywidgets from the command line like such:

conda install -c conda-forge ipywidgets

With the Anaconda distribution there is no need to enable the extension, conda handles this for you.

Then import into your code:

import tensorflow_datasets as tfds
mnist = tfds.load(name='mnist')

You should be able to use it without error if you follow these instructions.

Remove the lines:

from tensorflow.examples.tutorials.mnist import input_data
fashion_mnist = input_data.read_data_sets('input/data',one_hot=True)

and the line below will suffice:

fashion_mnist = keras.datasets.fashion_mnist

Note that if the dataset is not available in the examples built-in to the keras, this will download the dataset and solve the problem. 🙂