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Tensorflow v1.2 released

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Re: Tensorflow v1.2 released

#31

Post by cshenton »

RockyMcNuts wrote: post #28
claudiug wrote: post #12

Can someone explain why should I pick this over scikit? I don't have any ML exp. I found ML quite magically :/ and totally difficult to start if you don't have a phd in mathematics

TensorFlow is low-level. scikit or sklearn is high level off-the-shelf ML, apply this algorithm to this dataset with these parameters. TFlearn is a high-level off-the-shelf library built on TensorFlow, giving you some of the benefits e.g. GPU. It's hard to get state of the art results using off-the-shelf algorithms, unless your problem is very vanilla you typically need to get under the hood and do custom hyperparame…

Didn't tflearn become tensorflow.contrib.learn? So there's technically a high level API in the TF standard library. Though Keras would probably be a better choice if OP wanted a high level API for deep learning.

Re: Tensorflow v1.2 released

#32

Post by netheril96 »

claudiug wrote: post #12

Can someone explain why should I pick this over scikit? I don't have any ML exp. I found ML quite magically :/ and totally difficult to start if you don't have a phd in mathematics

ML/DL requires only sophomore math. Calculus, linear algebra and probability covers the majority of what you need.

Re: Tensorflow v1.2 released

#33

Post by tomjakubowski »

swang wrote: post #26
matt4077 wrote: post #19

Earlier quoted context omitted.

Officially, there shouldn't be very many people for whom it's relevant. The last Macs with Nvidia GPUs were sold around 2011 if I remember correctly. Unofficially, there may be some people using Hackitoshs with rather beefy GPUs for machine learning. There's a lot you can do easily on a $500 GPU that should take too long on CPU. And I prefer the shorter write/run/debug loop of working locally. It's the same niche oth…

My late 2013 MBP has a Nvidia 750M

Doing a little binary search on Everymac, it seems that the last new MBP model with Nvidia graphics was mid-2014. Both offerings with discrete graphics from mid-2015 had AMD cards. I'm also fairly sure you could still buy those mid-2014 MBPs well into 2015; I distinctly remember seeing both AMD and Nvidia MBPs available in the online store around that time.

http://www.everymac.com/systems/apple/macbook_pro/specs/macb...

Re: Tensorflow v1.2 released

#34

Post by aseipp »

cshenton wrote: post #31
RockyMcNuts wrote: post #28

Earlier quoted context omitted.

TensorFlow is low-level. scikit or sklearn is high level off-the-shelf ML, apply this algorithm to this dataset with these parameters. TFlearn is a high-level off-the-shelf library built on TensorFlow, giving you some of the benefits e.g. GPU. It's hard to get state of the art results using off-the-shelf algorithms, unless your problem is very vanilla you typically need to get under the hood and do custom hyperparame…

Didn't tflearn become tensorflow.contrib.learn? So there's technically a high level API in the TF standard library. Though Keras would probably be a better choice if OP wanted a high level API for deep learning.

A version of Keras is included in Tensorflow since 1.1 as well, under `tensorflow.contrib.keras.python.keras`. (This has been particularly useful for me as a Windows user, since my GTX 1080 is stuck in a Windows machine, and it's a little more painful to deal with upstream Keras dependencies on Windows, but TF alone works great)

Re: Tensorflow v1.2 released

#35

Post by mwfunk »

dbecker wrote: post #29
Houshalter wrote: post #24

Earlier quoted context omitted.

Macs don't have Nvidia GPUs and tensorflow is only supported on Nvidia.

I have an MBP and an iMac, both of which came with nvidia GPU's. The MBP is older, but I doubt either of these are atypical machines out there today.

They probably are atypical systems for people using TensorFlow professionally though.

Re: Tensorflow v1.2 released

#37

Post by skynode »

matt4077 wrote: post #21
claudiug wrote: post #12

Can someone explain why should I pick this over scikit? I don't have any ML exp. I found ML quite magically :/ and totally difficult to start if you don't have a phd in mathematics

Scikit doesn't support GPUs, which makes it infeasible to run the sort of deep learning stuff that's currently making waves. The competitors to tensorflow are torch, caffe, and maybe Microsoft's CN(something, but not "Y")K. To get started, keras is an excellent library that's build on top of tensorflow and has recently become an official part of it.

Microsoft CNTK. Originally Computational Network Toolkit. Abbreviation remains but now Microsoft Cognitive Toolkit.

Re: Tensorflow v1.2 released

#38

Post by microtonal »

Houshalter wrote: post #24
jswny wrote: post #8

Earlier quoted context omitted.

Is there any explanation for why they decided to do this? I would imagine they just don't have the means to test on Mac anymore but I'd like to know why for sure.

Macs don't have Nvidia GPUs and tensorflow is only supported on Nvidia.

I am not sure what the current progress on OpenCL support is, but Tensorflow's configure script definitely asks whether you want to install Tensorflow with OpenCL support.

Re: Tensorflow v1.2 released

#40

Post by baristaGeek »

claudiug wrote: post #12

Can someone explain why should I pick this over scikit? I don't have any ML exp. I found ML quite magically :/ and totally difficult to start if you don't have a phd in mathematics

Tensorflow implements the (really abstract, at least for me) concept of a computational graph. Think of it as nodes being values and edges operations.

Among other things, what this allows you to do is partition your computational graph into different subgraphs and run each subgraph on parallel.

Sklearn doesn't allow you to run things on parallel; however, I do agree that TF doesn't have a favorable learning curve, so you might want to start with SKlearn (or TFLearn) to get to know the basics of ML first.

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