I'm new in machine learning and I am following tensorflow's tutorial to create some simple Neural Networks which learn the MNIST data.
i wanna run a code that do the recognition hand writing digits using the MNIST data but i don't know how to run it ... should i dowload the data on my machine and extracted and put it on a file and then set the path on the code or did tensorflow contain the data ...but when i do import input_data i get
No module named 'input_data' also when i do
from tensorflow.examples.tutorials.mnist import input_data ==> No module named 'tensorflow.examples'
ps:when i do import tensorflow as tf i get no erreur so it's fine with tensorflow i think
could u help me plz for example i wanna run the code below what should i do
https://github.com/hwalsuklee/tensorflow-mnist-cnn
If you cannot import tensorflow.examples I'm guessing something went wrong with the installation. Try reinstalling tensorflow with the latest version.
You don't need to download the data on your own, tensorflow will put it in the path you provide. But first, try these steps:
I'm currently using tf 1.2.0 and I'm not getting that error.
If you want to know which version you have installed:
import tensorflow as tf
print(tf.__version__)
After everything is installed try:
from tensorflow.examples.tutorials.mnist import input_data
input_data.read_data_sets("./data/", one_hot=True)
That should copy the data to a "data" folder inside your working folder (the "data" folder will be created and all the files will be available there).
If the above lines of code run with no errors, you should be able to run the example.
Related
I'm not familiar with using python as a ML tool and wanted to train the MNIST data set. I have downloaded the MNIST library using
pip install python-mnist but do not know what my next step should be. What would an import statement look like? Should I also import TensorFlow and or Keras to train the data?
I know the MNIST dataset is available in TensorFlow and Keras, however, importing via pip a necessary solution for my use case. Both TensorFlow and Keras have tutorials using the MNIST data set, but I was wondering if it is possible to use the data set without using their pre-downloaded library.
The import statement should look like this
from mnist import MNIST
mndata = MNIST('./dir_with_mnist_data_files')
images, labels = mndata.load_training()
then you can work directly with the arrays of raw images and labels.
I was able to run my python program three weeks ago but now every time I try to run it, I get the following error:
AttributeError: module 'tensorflow' has no attribute 'placeholder'
I have tensorflow installed (version '2.0.0-alpha0').
I have read a couple of posts related to this issue. They say I should uninstall TensorFlow and re-install it again. The problem is that I am running this on a cluster computer and I do not have sudo permissions.
Any idea?
In Tensorflow 2.0, there is no placeholder. You need to update your TF1.x code to TF2.0 code and then run it on your cluster. Please take a look at the official doc on converting your TF1.x code to TF2.0.
In TF1.x codes, you build tensorflow graph (static graph) with placeholders, constants, variables. Then, run the code in a session with a tf.session() command. During that session, you provide the values for the placeholder and execute the static graph.
In TF2.0, models run eagerly as you enter commands. This is more pythonic. Check more details about TF 2.0 here. Thanks!
After including the tensorflow compat v1 libraries:
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()`
use the v1 syntax like this:
X = tf.compat.v1.placeholder(dtype="float",shape=[None, n_H0, n_W0, n_C0])
Y = tf.compat.v1.placeholder(dtype="float",shape=[None, n_y])
In addition to the #Vishnuvardhan Janapati's answer, you can update folders ("*TREE") and/or files to version 2 of TensorFlow. The upgrade tool tf_upgrade_v2 is automatically included in TensorFlow 1.13 and later.
tf_upgrade_v2 [-h] [--infile INPUT_FILE] [--outfile OUTPUT_FILE]
[--intree INPUT_TREE] [--outtree OUTPUT_TREE]
[--copyotherfiles COPY_OTHER_FILES] [--inplace]
[--reportfile REPORT_FILENAME] [--mode {DEFAULT,SAFETY}]
[--print_all]
An illustration of how the conversion fixed the "placeholder" error:
Note: this fixes similar complaints module 'tensorflow' has no attribute 'xxxxx' (not just the "placeholder").
Calling disable_v2_behavior() function is not necessary
just,
import tensorflow as tf
tf.compat.v1.placeholder()
Changing the library worked for me
#libraries
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
If this doesn't work maybe you need you install TensorFlow again.
I hope it helps
one can use:
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST/", one_hot=True)
But I don't want to download dataset everytime. What is the best way to download the dataset ONLY in case if it is not in MNIST/?
I tried: if not os.path.isdir("MNIST/"): but this way the mnist is not initialised. I want to use this input_data but only its reading ability without the download.
If you don't want to download data-set everytime, you can initialize the first parameter with absolute path, just like this:
input_data.read_data_sets("/your/absolute/path/", one_hot=True)
The function will not download the file if the file is detected. You can debug the function, and you will know. Also, you could modify the code in your way, using the part which you really need.
For running without downloading MNIST data, do following (it worked for me)
input_data.read_data_set('--absolute-path-of-MNIST-DATASET--')
How can I get reproducible results with keras? I followed these steps but I am still getting different results every time I run the Jupyter notebook. I also tried setting shuffle=False when calling model.fit().
My configuration:
conda 4.3.25
keras 2.0.6 with tensorflow backend
tensorflow-gpu 1.2.1
python 3.5
windows 10
See the answer I posted at another question. The main idea is to: first, disable the GPU. And then, seed the libraries like "numpy, random, etc". To sum up, including the code below at the beginning of your code may help solve your problem.
import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = ""
import numpy as np
import tensorflow as tf
import random as rn
from keras import backend as K
sd = 1
np.random.seed(sd)
rn.seed(sd)
os.environ['PYTHONHASHSEED']=str(sd)
config = tf.ConfigProto(intra_op_parallelism_threads=1,inter_op_parallelism_threads=1)
tf.set_random_seed(sd)
sess = tf.Session(graph=tf.get_default_graph(), config=config)
K.set_session(sess)
So apparently the Yann LeCun's website is down so the following lines for reading mnist with tensorflow don't seem to be working :
FROM tensorflow.examples.tutorials.mnist IMPORT input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot = true)
Any ideas how can i read the mnist without using these above lines?
You can access the website here: https://web.archive.org/web/20160117040036/http://yann.lecun.com/exdb/mnist/ - download the data, and read it in from a local copy...
Edit
Here is a code example for reading a local mnist dataset
You can download the dataset individually and put it in the directory you created.Then the code can run normally.
http://yann.lecun.com/exdb/mnist/
Ok guys i fixed my problem,my mistake was that i was extracting the files(to the \MNIST_data directory) from the .gz files where i should have left them as they were unextracted instead.Thanks for the help.