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  • hello world it's Siraj in this video we're going to compare the most popular deep

  • learning frameworks out there right now to see what works best

  • the deep-learning space is exploding with frameworks right now ,it's like every

  • single week some major tech company decides to open source their own deep

  • learning library and that's not including the dozens of deep learning

  • frameworks being released every single week on github by cowboy developers how

  • *how many layers you have?*

  • let's start off with scikit-learn scikit it was made to provide an easy-to-use

  • interface for developers to use off-the-shelf general-purpose machine

  • learning algorithms for both supervised and unsupervised learning , scikit provides

  • functions that like you apply classic machine learning algorithms like support

  • vector machines logistic regressions and k nearest neighbor very easily but the

  • one type of machine learning algorithm he doesn't let you implement is a neural

  • network it doesn't provide GPU support either which is what helps neural

  • networks scale , since like two months ago

  • pretty much every single general-purpose algorithm that psyche learned

  • implemented has since been implemented in tensorflow sidekick you just got

  • LEARNED , there's also caffe which was basically the first mainstream

  • production grade deep learning library started in 2014, the cafe isn't

  • very flexible think of a neural network as a computational graph in cafe each

  • note is considered a layer so if you want new layer types you define the full

  • forward backward and gradient updates these layers are building blocks that

  • are unnecessarily big there's an endless list of them that you can pick from

  • intensive flow each note is considered a tensor operation like matrix add,

  • matrix multiply or convolution and a layer can be defined as a composition of

  • those operations so tender flows building blocks are smaller which allows

  • for more modularity ,cafe also requires a lot of unnecessary verbosity if you want

  • to support both the CPU and the GPU you need to implement extra functions for

  • each and you have to define your model using a plain text editor that is just

  • ghetto model should be defined programmatically because it's better for

  • modularity between different components , also caffe main architect now works on

  • the tensorflow team we're all out of caffe

  • speaking of modularity let's talk about keras : Keras has been the go-to

  • source to get started with deep learning for a while because it provides a very

  • high level API to build deep learning models Kera sits on top of the other

  • deep learning libraries like Theano and tensorflow it uses an

  • object-oriented design so everything is considered an object be that layers

  • models optimizers and all the parameters of the model can be access object

  • properties like model.layers[3].output will give you the output tensor

  • for the third layer in the model and model.layers[3].weights is a list

  • of symbolic weight tensors this is a cleaner interface as opposed to the

  • functional approach of making layers function that create weights when being

  • called great documentation it's all gucci yes i'm bringing that back but

  • because it's so general-purpose it lacks on the side of performance Keras has been

  • known to have performance issues when used with a tensorflow backend since

  • it's not really optimized for it but it does work pretty well with the Theano

  • backend , the two frameworks that are neck-and-neck right now in the race to

  • be the best library for both research and Industry are tensorflow and Theano

  • Theano currently outperforms tensorflow on a single GPU potential flow

  • outperforms piano for parallel execution across multiple gpus , Theano has got more

  • documentation because it's been around for a while and it's got native windows

  • support which tensorflow doesn't yet dammit windows in terms of syntax let's

  • just take a look at some code to see some differences

  • we're going to compare two scripts in tensorflow and beyond

  • they both do the same thing initializing phony data and then learn the line of

  • best fit for that data is it can predict future data points let's look at the

  • first step in both tensorflow and Theano for generating the data pretty much the

  • same way using numpy arrays so there's not really a difference there

  • let's look at the model initialization parts ,this is the basic "y=mx+b"

  • formula in tensorflow it doesn't require any special treatment of the x

  • and y variables

  • they're just they're natively but Theano we have to specifically say that

  • the variables are symbolic inputs to the function the tensorflow syntax for

  • defining the B&W variables is cleaner than we implement our gradient descent

  • function which is what helps us learn or trying to minimize the mean squared

  • error over time which is what makes our model more accurate as we train the

  • syntax for defining what we're minimizing is pretty similar then we

  • look at the actual optimizer which helps us do that will notice a difference in

  • syntax again

  • the flow just gives you access to a bunch of optimizers right out-of-the-box

  • things like gradient descent or Adam Theano makes you do this from scratch

  • then we have our training function which is again more verbose see the trend here

  • theano so far is making us implement more code than tensorflow so it seems to

  • give us more fine-grained control but at the cost of readability finally we'll

  • get to the actual training part itself they look pretty identical but

  • tensorflow methodology of encapsulating the computational graph feels conceptually

  • cleaner than pianos tensorflow is just growing so fast that it seems inevitable

  • that whatever feature it lacks right now because of how new it is it will gain

  • very rapidly

  • I mean just look at the amount of activity happening in the tension flow

  • repo versus the Theano repo on get up right now and while keras serves as an

  • easy use wrapper around different libraries it's not optimized for

  • tensorflow a better alternative you want to learn and get started

  • easily with deep learning is TF learn which is basically keras but optimized

  • for tensorflow so to sum things up the best library to use for research is

  • tensorflow the world-class researchers at both open AI and deep mine are now

  • using it for production best library to use is still tensorflow is it scaled

  • better across multiple GPUs and its closest competitor Theano .Lastly for

  • learning the best library to use is TF learn which is a high-level wrapper

  • around tensorflow that lets you get started really easily also shout out to

  • rahul do for being able to generate an upbeat midnight file badass of the

  • week please subscribe for more programming videos for now I've got to

  • go worship tensorflow some more so thanks for watching

hello world it's Siraj in this video we're going to compare the most popular deep

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B1 中級

ディープラーニングのフレームワークの比較 (Deep Learning Frameworks Compared)

  • 83 18
    alex に公開 2021 年 01 月 14 日
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