# 케라스

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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

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

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- ID : Q28470421