
TensorFlow – Consise Examples for Beginners - aymericdamien
https://github.com/aymericdamien/TensorFlow-Examples
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jszymborski
I find it so aggravating that nearly every last ML framework documents their
CNN libraries in terms of canned MNIST datasets imported from the library in a
preprocessed form.

It's always left as a useless exercise for the reader to divine how to
generate such a dataset from his/her own data.

Examples should be way more general. The starting point shouldn't be:

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

, it should start with "here is a directory of images and their classes" and
end with a CNN model.

EDIT: Should anyone have any insight as to where I might get such a tutorial
(or have the desire to write one), I know a herd of ML pre-initiates that
would be grateful.

~~~
mmanfrin
A few of the lessons on Udacity's Dada Science course cover finding/sifting
through datasets and formatting them in a way to work with:

[https://classroom.udacity.com/courses/ud359](https://classroom.udacity.com/courses/ud359)

~~~
zellyn
"Dada Science"… fantastic!

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dmix
I'm totally new to TensorFlow and ML in general, but I've been curious about
how this could fit into a system.

Say you need a CNN text classifier algorithm to categorize simple single page
documents. So you set one up via TensorFlow, train it with a big dataset, and
get it outputting categories with decent accuracy. Could you then use some
type of API and query it from a (low traffic) production web app?

Or is it more for the research phase rather than real-time interaction?

~~~
adyus
Take a look at
[https://tensorflow.github.io/serving/serving_basic](https://tensorflow.github.io/serving/serving_basic)
and
[https://tensorflow.github.io/serving/serving_advanced](https://tensorflow.github.io/serving/serving_advanced)

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Omnipresent
I am looking for a TensorFlow resource that shows how to classify images of a
certain kind. For example: given 100 images, find the ones that might contain
a soccer ball. I haven't come across such a learning resource with TensorFlow.
Has anyone else?

~~~
bdupharm
Why don't you just start with the CIFAR-10 example and go from there?
[https://www.tensorflow.org/versions/r0.8/tutorials/deep_cnn/...](https://www.tensorflow.org/versions/r0.8/tutorials/deep_cnn/index.html)

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jdoliner
Can we get a mod fix on the title?

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rrggrr
This is not for beginners. Without simple examples and example datasets the
concepts remain laregely inaccessible to a real beginner.

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aanchan
Irrelevant comment: Title 'consise' -> 'concise'

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struct
Thanks for this! I recently ported something over from Theano to TensorFlow (I
wrote it up at [1]) and I have to admit I generally enjoyed the experience a
great deal, even if the single-GPU performance wasn't good enough to make me
switch. The TFLearn library (especially [2]) looks very compelling for
prototyping however, some I'm very excited to see how the project develops.

[1] [https://medium.com/@sentimentron/faceoff-theano-vs-
tensorflo...](https://medium.com/@sentimentron/faceoff-theano-vs-
tensorflow-e25648c31800#.tqq5nyjks)

[2]
[https://github.com/tflearn/tflearn/blob/master/examples/nlp/...](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm.py)

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guan
This is missing a FizzBuzz example.

~~~
visarga
That's an advanced use case.

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boundlessdreamz
Does someone point me in the right direction for using a neural network for
prediction (the input will be time series data). All the beginner's tutorials
I have seen deal with classification and not prediction.

~~~
moultano
LSTM

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boundlessdreamz
Thank you!

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Omnipresent
curated list of dedicated resources: [https://github.com/jtoy/awesome-
tensorflow](https://github.com/jtoy/awesome-tensorflow)

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d136o
That looks helpful. In case you want something shorter to look through here's
a very concise set of notes I took for myself when I first played with TF:

[https://gist.github.com/d136o/4c68d010ecb0abfc7c52](https://gist.github.com/d136o/4c68d010ecb0abfc7c52)

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bdi73849
Great examples! I find it more accessible for me than the official tutorial.

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arcanus
Anyone know of any benchmarks on tensorflow that provide a baseline estimate
of the expected performance for some off-the-shelf hardware(s)?

~~~
krasin
These are the benchmarks supported by Soumith Chintala (Facebook AI Research):

[https://github.com/soumith/convnet-
benchmarks](https://github.com/soumith/convnet-benchmarks)

They place TensorFlow performance on par with Torch (within 10%).

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vonnik
Typo: it should be concise with two c's and one s.

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jackson_1
In the most compact way possible:

TensorFlow is like Numpy, only it is capable of working symbolically and can
work more easily with your GPU in a highly parallel manner. For those two
reasons, it is vastly superior to numpy for tasks like deep learning / machine
learning.

Theano is another symbolic numerical library, coming before TensorFlow, though
TensorFlow has seemingly gained more popularity and people have more general
faith in it.

