케라스

수학노트
Pythagoras0 (토론 | 기여)님의 2020년 12월 16일 (수) 01:57 판 (→‎노트: 새 문단)
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노트

  • An AutoML system based on Keras.[1]
  • A great introduction to using Keras for deep learning.[2]
  • Keras in Motion teaches you to build neural-network models for real-world data problems using Python and Keras.[2]
  • Keras, on the other end, is a high-level API that is built on top of TensorFlow.[3]
  • Keras is a high-level library that’s built on top of Theano or TensorFlow.[3]
  • The key idea behind the development of Keras is to facilitate experimentations by fast prototyping.[3]
  • Keras is a high-level interface and uses Theano or Tensorflow for its backend.[3]
  • To alleviate this problem, we introduce a software library, the Fortran-Keras Bridge (FKB).[4]
  • To this end, we propose the Fortran-Keras Bridge (FKB), a two-way bridge connecting models in Keras with ones available in Fortran.[4]
  • Keras abstracts many of the complicated aspects of TensorFlow while still providing customizability and ease of use.[4]
  • This combination makes Keras the first choice of many for deep learning applications.[4]
  • Keras is a high-level python API which can be used to quickly build and train neural networks using either Tensorflow or Theano as back-end.[5]
  • François Chollet , who works at Google developed Keras as a wrapper on top of Theano for quick prototyping.[5]
  • Keras is being hailed as the future of building neural networks.[5]
  • Emerging possible winner: Keras is an API which runs on top of a back-end.[5]
  • Keras is an API designed for human beings, not machines.[6]
  • Keras has also been adopted by researchers at large scientific organizations, in particular CERN and NASA.[6]
  • Keras also supports arbitrary connectivity schemes (including multi-input and multi-output training) and runs seamlessly on CPU and GPU.[7]
  • The core data structure of Keras is a model, a way to organize layers.[7]
  • To be able to easily create new modules allows for total expressiveness, making Keras suitable for advanced research.[7]
  • Pros : Keras is the only platform that runs on top of most popular backends like TensorFlow, pyTorch and Microsoft Cogntitive Toolkit.[8]
  • Keras is compact, easy to learn, high-level Python library run on top of TensorFlow framework.[9]
  • Here, we've used Keras' Sequential() to instantiate a model.[10]
  • This is the final stage in our journey of building a Keras deep learning model.[10]
  • Keras provides the evaluate() function which we can use with our model to evaluate it.[10]
  • In 2017, Google's TensorFlow team decided to support Keras in TensorFlow's core library.[11]
  • Keras was originally conceived to be an interface rather than a standalone machine-learning framework.[11]
  • Here, we use Keras to define a network that recognizes MNIST handwritten digits.[12]
  • () Once we define the model, we have to compile it so that it can be executed by the Keras backend (either Theano or TensorFlow).[12]
  • Training a model in Keras is very simple.[12]
  • So, congratulations, you have just defined your first neural network in Keras.[12]
  • Keras is a neural network library providing a high-level API in Python and R. Use this tag for questions relating to how to use this API.[13]
  • Keras is a high-level neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.[14]
  • By default, Keras will use TensorFlow as its tensor manipulation library.[14]
  • Keras (κέρας) means horn in Greek.[14]
  • Keras is an open-source library of neural network components written in Python.[15]
  • Keras is capable of running atop TensorFlow, Theano, PlaidML and others.[15]
  • The principal author of Keras is Francois Chollet, a Google engineer who also wrote XCeption, a deep neural network model.[15]
  • While Keras officially launched, it was not integrated into Google's TensorFlow core library until 2017.[15]
  • Keras is a deep learning library designed to enable fast experimentation.[16]
  • Keras 2 was released in 2017 to update the system further.[16]
  • Keras enables a stack of layers and reduces the time you spend building your training data.[16]
  • Developers use Keras to define and train neural network models, but use only a few lines of code.[16]
  • Would you like to take a course on Keras and deep learning in Python?[17]
  • In this lab, you will learn how to build, train and tune your own convolutional neural networks from scratch with Keras and Tensorflow 2.[18]
  • In this code lab, you will see how to use them with Keras and Tensorflow 2.[18]
  • The code for training a model on TPU in Keras: # detect the TPU tpu = tf.distribute.cluster_resolver.[18]
  • In Keras, the batch you specify is the global batch size for the entire TPU.[18]
  • This website provides documentation for the R interface to Keras.[19]
  • Keras is a high-level neural networks API developed with a focus on enabling fast experimentation.[19]
  • This will provide you with default CPU-based installations of Keras and TensorFlow.[19]
  • We can learn the basics of Keras by walking through a simple example: recognizing handwritten digits from the MNIST dataset.[19]
  • Keras version 2.1.3 introduced breaking changes that were adapted in KNIME version 3.6.0.[20]
  • This error may occur when using Keras version 2.1.2 to load a Keras network that was saved using an older Keras version.[20]
  • KERAS is an Open Source Neural Network library written in Python that runs on top of Theano or Tensorflow.[21]
  • Keras doesn't handle low-level computation.[21]
  • In this level, Keras also compiles our model with loss and optimizer functions, training process with fit function.[21]
  • If you want to make a simple network model with a few lines, Keras can help you with that.[21]
  • Keras is the most used deep learning framework among top-5 winning teams on Kaggle.[22]
  • Because Keras makes it easier to run new experiments, it empowers you to try more ideas than your competition, faster.[22]
  • Keras is an open-source software library that provides a Python interface for artificial neural networks.[23]
  • Generally, all layers in Keras need to know the shape of their inputs in order to be able to create their weights.[24]
  • In the near future, this repository will be used once again for developing the Keras codebase.[25]
  • For the time being, the Keras codebase is being developed at tensorflow/tensorflow, and any PR or issue should be directed there.[25]
  • Keras is one of the leading high-level neural networks APIs.[26]
  • Keras was created to be user friendly, modular, easy to extend, and to work with Python.[26]
  • The biggest reasons to use Keras stem from its guiding principles, primarily the one about being user friendly.[26]
  • Keras proper does not do its own low-level operations, such as tensor products and convolutions; it relies on a back-end engine for that.[26]
  • : Updated example for the latest versions of Keras and TensorFlow.[27]
  • Updated for Keras v2.2.5 API.[27]
  • Updated for Keras v2.3.0 API and TensorFlow v2.0.0.[27]
  • In this Keras tutorial, we are going to use the Pima Indians onset of diabetes dataset.[27]
  • # Instantiate an end-to-end model predicting both priority and department model = keras.[28]
  • : return tf.matmul(inputs, self.w) + self.b inputs = keras.[28]
  • In Keras With high-level neural network libraries like Keras, we will not need to implement this formula.[29]
  • In Keras, a dense layer would be written as: tf.keras.layers.[29]
  • Configuring the model is done in Keras using the model.compile function.[29]
  • A classification model requires a cross-entropy loss function, called 'categorical_crossentropy' in Keras.[29]
  • Input (( 764 ,), name = 'inputs' ) logits = keras .[30]

소스

  1. ↑ AutoKeras
  2. ↑ 2.0 2.1 Keras in Motion
  3. ↑ 3.0 3.1 3.2 3.3 The What’s What of Keras and TensorFlow
  4. ↑ 4.0 4.1 4.2 4.3 A Fortran-Keras Deep Learning Bridge for Scientific Computing
  5. ↑ 5.0 5.1 5.2 5.3 Keras tutorial: Practical guide from getting started to developing complex deep neural network – CV-Tricks.com
  6. ↑ 6.0 6.1 Why Use Keras?
  7. ↑ 7.0 7.1 7.2 PAT RESEARCH: B2B Reviews, Buying Guides & Best Practices
  8. ↑ Keras Reviews and Pricing
  9. ↑ Tutorialspoint
  10. ↑ 10.0 10.1 10.2 Building a Deep Learning Model
  11. ↑ 11.0 11.1 Deep Learning Software
  12. ↑ 12.0 12.1 12.2 12.3 Deep Learning with Keras
  13. ↑ Newest 'keras' Questions
  14. ↑ 14.0 14.1 14.2 Keras 1.2.2 Documentation
  15. ↑ 15.0 15.1 15.2 15.3 Keras
  16. ↑ 16.0 16.1 16.2 16.3 Learn Keras with Online Courses and Classes
  17. ↑ (Tutorial) KERAS Tutorial: DEEP LEARNING in PYTHON
  18. ↑ 18.0 18.1 18.2 18.3 Keras and modern convnets, on TPUs
  19. ↑ 19.0 19.1 19.2 19.3 Getting Started with Keras
  20. ↑ 20.0 20.1 KNIME Deep Learning - Keras Integration
  21. ↑ 21.0 21.1 21.2 21.3 Keras Tutorial for Beginners with Python: Deep Learning EXAMPLE
  22. ↑ 22.0 22.1 Keras: the Python deep learning API
  23. ↑ Wikipedia
  24. ↑ The Sequential model
  25. ↑ 25.0 25.1 keras-team/keras: Deep Learning for humans
  26. ↑ 26.0 26.1 26.2 26.3 What is Keras? The deep neural network API explained
  27. ↑ 27.0 27.1 27.2 27.3 Your First Deep Learning Project in Python with Keras Step-By-Step
  28. ↑ 28.0 28.1 Loner의 학습노트 :: 텐서플로 2.0 keras 개인정리 (모델 작성 심화)
  29. ↑ 29.0 29.1 29.2 29.3 TensorFlow, Keras and deep learning, without a PhD
  30. ↑ tensorflow 2.0 케라스 정리
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