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The code is tested using Tensorflow r1. The test cases can be found here and the results can be found here. NOTE: If you use any of the models, please do not forget to give proper credit to those providing the training dataset as well. The code is heavily inspired by the OpenFace implementation. This training set consists of total of images over 10 identities after face detection. Some performance improvement has been seen if the dataset has been filtered before training.
Some more information about how this was done will come later. One problem with the above approach seems to be that the Dlib face detector misses some of the hard examples partial occlusion, silhouettes, etc. This makes the training set too "easy" which causes the model to perform worse on other benchmarks. To solve this, other face landmark detectors has been tested.
One face landmark detector that has proven to work very well in this setting is the Multi-task CNN. Currently, the best results are achieved by training the model using softmax loss. A couple of pretrained models are provided. They are trained using softmax loss with the Inception-Resnet-v1 model.Jeremy Howard: Deep Learning Frameworks - TensorFlow, PyTorch, cronopoison.pw - AI Podcast Clips
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Comparison of deep-learning software
Is TensorFlow or Keras better? Should I invest my time studying TensorFlow? Or Keras? The above are all examples of questions I hear echoed throughout my inbox, social media, and even in-person conversations with deep learning researchers, practitioners, and engineers. As of midKeras was actually fully adopted and integrated into TensorFlow. You can insert TensorFlow code directly into your Keras model or training pipeline!
Stop worrying and just get started. My suggestion would be to use Keras to start and then drop down into TensorFlow for any specific functionality you may need. Including Keras inside tf. Does this mean that you have to use tf. Is the standard Keras package now obsolete? No, of course not. Keras as a library will still operate independently and separately from TensorFlow so there is a possibility that the two will diverge in the future; however, given that Google officially supports both Keras and TensorFlow, that divergence seems extremely unlikely.
There is no more Keras vs. TensorFlow argument — you get to have both and you get the best of both worlds. The CIFAR dataset itself consists of 10 separate classes with 50, training images and 10, testing images.
Open up the minivggnetkeras. Dropout is also applied to reduce overfitting. For a brief review of the layer types and terminology, be sure to check out my previous Keras tutorial where they are explained.
Our FC and Softmax classifier are appended onto the network. Finally, we assemble and export our plot. And as our output plot demonstrates in Figure 6there is no overfitting occurring.
To get started, open up the minivggnettf. In this file, notice that the imports are replaced by a single line Line 2. The tf.
These lines are highlighted in yellow. For more information, please refer to the Shang et al. Instead, focus on how we were able to swap in a TensorFlow activation function in-place of a standard Keras activation function inside of a Keras model! Ultimately, we found that trying to decide between Keras and TensorFlow is starting to become more and more irrelevant.
Keras vs. TensorFlow – Which one is better and which one should I learn?
The Keras library has been integrated directly into TensorFlow via the tf. Essentially, you can code your model and training procedures using the easy to use Keras API and then custom implementations into the model or training process using pure TensorFlow!As per the same report AI can create substantial value across industries. To unlock the potential value of AI, companies must choose the right deep learning framework.
In this tutorial, you will learn about the different libraries available to carry out deep learning tasks. Some libraries have been around for years while new library like TensorFlow has come to light in recent years. All of them are open source and popular in the data scientist community. We will also compare popular ML as a service providers Torch Torch is an old open source machine learning library.
TensorFlow vs Theano vs Torch vs Keras: Deep Learning Libraries
It is first released was 15 years ago. It is primary programming languages is LUA, but has an implementation in C. Torch supports a vast library for machine learning algorithms, including deep learning. It supports CUDA implementation for parallel computation. Torch is used by most of the leading labs such as Facebook, Google, Twitter, Nvidia, and so on.
Torch has a library in Python names Pytorch. The library contains analytical tools such as Bayesian analysis, hidden Markov chain, clustering. Keras Keras is a Python framework for deep learning.
It is a convenient library to construct any deep learning algorithm. Besides, the coding environment is pure and allows for training state-of-the-art algorithm for computer vision, text recognition among other.
Theano has been developed to train deep neural network algorithms. According to Microsoft, the library is among the fastest on the market. Microsoft toolkit is an open-source library, although Microsoft is using it extensively for its product like Skype, Cortana, Bing, and Xbox.
MXNet MXnet is a recent deep learning library.Machine learning is one of the hottest topics in modern software development. Still, it is a fairly young technology, which is undergoing rapid change and development. We wrote this article to aid you in selecting the right flavor of ML for your projectshowcasing the top frameworks along with their upsides and shortcomings. Scikit-learn is a Python library used for machine learning. A major benefit of this library is the BSD license it's distributed under.
This license allows you to decide whether to upstream your changes without any restriction on commercial use. On the other hand, scikit-learn is not the best choice for deep learning.
Its main advantages include its cost free for commercial use under the BSD licenceefficient use of RAM, and incredible speed. These, however, come with a tradeoff — the tech can be hard to get used to for beginners.
Although its website might look uninviting, dlib offers an impressive range of features, including exhaustive documentation, support for machine learningnumerical, and graphical model inference algorithms, as well as a GUI API, and a number of other perks and utilities. One popular use of dlib is face recognition including face landmark detection. Since the framework has been in developmentit can be used for many other things, although doing so might require significant time investment in order to become familiar with its many features.
A topic model is a statistical model used to discover abstract "topics" in documents. While the concept was initially developed for text mining, topic models have been used to detect structures in other types of data, such as genetic information.
The main advantages of Gensim, as mentioned by its creators, are its clarity, efficiency, and scalability. As for its disadvantages, Gensim is not a general-purpose framework, so using it for things like image recognition is out of the question. MLlib is, as implied by its fairly straightforward name, a machine learning library, maintained as a part of Apache Spark.
It is intended as a tool for big data processing; one could also call it an open-source cluster computing platform.
As for its advantages and disadvantagesthe former include extremely fast processing, dynamic nature, reusability and fault tolerance. However, weaknesses such as memory expensiveness, high latency, the necessity of manual optimization, and lack of file management may degrade your experience with MLlib.
It will serve you best in such applications as fraud detection, electronic trading data, and log processing in live streams website logs. This means that it can be used to do things like recommending items to users based on their past purchases or ratings or matching users with people sharing similar tastes.
Its advantages are, as the creators say, great documentation, full control over your experiments, and easy implementation. Pandas Python Data Analysis Library is not precisely a machine learning library, but t it's widely used in machine learning community. It boasts many benefits, most of which are related to the fact that in pandas, your data has labels. The labels and data come in an R-style data frame, which makes it easier to get into for developers familiar with that language.
Its main and best use case is data wrangling sometimes referred to as data mungingthat is, processing and transforming raw data from one format into another for analytical purposes.
TensorFlow is, according to its inviting tagline, an open-source machine learning framework for everyone. The tool was initially developed for internal use at Google, but was released publicly under the Apache 2.
The framework is extremely powerful, which means two things: you can do a lot of things with it, but the other thing is that using it for simple things may be a huge overkill.
One thing TensorFlow is great at is deep learning. One major example is RankBrainan advanced keyword processing tool used by Google in its search engine. PyTorcha Python-centric deep learning framework, is all about flexible experimentation and customization, as well as strong GPU acceleration.
Some example applications include word-level language modeling, time sequence prediction, or training imagenet classifiers. A major advantage of this framework is its support for dynamic computation graphs DCGswhich is a boon for uses such as linguistic analysis. As for its downsides, PyTorch is still fairly immature, so it may be more difficult to find information about it and to recruit experienced devs. Its self-touted advantages are user-friendliness, modularity, easy extensibility, and the ability to work with the well-loved programming language that is Python.The deep learning landscape is constantly changing.
Theano was the first widely adopted deep learning framework, created and maintained by MILA — headed by Yoshua Bengio, one of the pioneers of deep learning. However, things have changed. In September of this year MILA announced that there will be no further development work on Theano in after releasing the latest version. In the past years different open source Python deep learning frameworks were introduced, often developed or backed by one of the big tech companies, and some got a lot of traction.
Some expected that with the introduction of TensorFlow Google would dominate the market for years. However, it looks like other frameworks did manage to attract a growing —and passionate — user base as well. Most mention worthy might be the introduction and growth of PyTorch.
PyTorch was introduced by Facebook, amongst others, in January Next to the GPU acceleration and the efficient usages of memory, the main driver behind the popularity of PyTorch is the use of dynamic computational graphs. These dynamic computational graphs were already being used by other, lesser known, deep learning frameworks like Chainer. Especially, in cases where the input can vary, for example with unstructured data like text, this is extremely useful and efficient.
Microsoft developed an internal deep learning framework called CNTK and officially launched the 2. InFacebook also launched Caffe2.
The original Caffe framework, developed by the Berkeley Vision and Learning Center, was and still is extremely popular for its community, its applications in computer vision, and its Model Zoo — a selection of pre-trained models.
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Where MXNet stands-out is its scalability and performance stay tuned for Part II — where we will compare the most popular frameworks on speed amongst other metrics.
These are just small selection of a wide range of frameworks. Other frameworks worth mentioning are H Next to all these frameworks, we also have interfaces that are wrapped around one or multiple frameworks. The most well know and widely used interface for deep learning is without a doubt Keras. This means that Keras will be included in the next TensorFlow release. This seems ideal for quick prototyping and Keras is also a popular tool in Kaggle competitions.
So at one side we currently have the high-level Keras API, that let you easily build simple and advanced deep learning models, and the low-level TensorFlow framework that gives you more flexibility in building models. Both are backed by Google. Gluon is a direct competitor for Keras and although AWS claims that they strongly support all deep learning frameworks they, of course, bet on Gluon for the democratization of AI.
Surprisingly enough, the biggest competitor of TensorFlow today seems to be PyTorch. After the growing interest in PyTorch from the community — for example, in the latest competitions at Kaggle users often choose to use PyTorch as part of their solutions and it has been used in the latest research papers as well —TensorFlow introduced in October Eager Execution.
With this launch, Google hopes to win back the users that fell in love with PyTorch and its dynamic graph. For the developers of the popular deep learning course fast. In September, fast. Jeremy Howard, founding researcher at fast. Only time will tell. With all these deep learning frameworks around it can be challenging for new comers to choose a framework.
This allows users to more easily move models between different frameworks.The following table compares notable software frameworkslibraries and computer programs for deep learning. From Wikipedia, the free encyclopedia. Redirected from Comparison of deep learning software. Some libraries may use other libraries internally under different licenses. July 19, — via GitHub. November 20, September 11, May 24, June 1, March 21, Retrieved 13 November September 3, Retrieved November 19, Microsoft Corporation.
Yes . No . No .
Skymind engineering team; Deeplearning4j community; originally Adam Gibson. No . Yes  . Computational Graph. Yes . Yes . Boost Software License.