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  • 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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