Keras and TensorFlow are the state of the art in deep learning tools and with the keras package you can now access both with a fluent R interface. After installing this configuration on different machines (both OSX and Ubuntu Linux) I will use this answer to at least document it for myself. The simplest model in Keras is the sequential, which is built by stacking layers sequentially. R lstm tutorial. I execute the following code in Python import numpy as np from keras. Keras and TensorFlow can be configured to run on either CPUs or GPUs. Lasagne is aimed at people who are already using Theano in their research and want an easier way to build neural nets. Just to narrow it down as much as possible, these were my steps: installation of R 3. It was developed with a focus on enabling fast experimentation. Keras has grown in popularity and supported on a wide set of platforms including Tensorflow, CNTK, Apple's CoreML, and Theano. R in terms of time required to train a convolutional neural network based model for image recognition. You have just found Keras. The main competitor to Keras at this point in time is PyTorch, developed by Facebook. Adadelta(learning_rate=1. The skip-gram model is a flavor of word2vec, a class of computationally-efficient predictive models for learning word embeddings from raw text. Albany students: Accelebrate offers a wide array of customized, instructor led, hands-on Java training and Python training at your site or online. Keras and TensorFlow can be configured to run on either CPUs or GPUs. This post will show how to write custom loss functions in R when using Keras, and show how using different approaches can be beneficial for different types of data sets. Keras is a high-level neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. The package provides an R interface to Keras, a high-level neural networks API developed with a focus on enabling fast experimentation. It was developed with a focus on enabling fast experimentation. Keras is a library of tensorflow, and they are both developed under python. I´m developing a regression Model in Keras for R. evaluate() computes the loss based on the input you pass it, along with any other metrics that you requested in th. R software works on both Windows and Mac-OS. Last released: 2019/10/12 BERT implemented in Keras. Adadelta is a more robust extension of Adagrad that adapts learning rates based on a moving window of gradient updates, instead of accumulating all past gradients. Implementation of the networks in Keras. 2 installation of RStudio 1. In this tutorial, we will present a few simple yet effective methods that you can use to build a powerful image classifier, using only very few training examples --just a few hundred or thousand pictures from each class you want to be able to recognize. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both 'CPU' and 'GPU' devices. A/B Testing Admins Automation Barug Big Data Bigkrls Bigquery Blastula Package Book Review Capm Chapman University Checkpoint Classification Models Cleveland Clinic Climate Change Cloud Cloudml Cntk Co2 Emissions Complex Systems Containers Control Systems Convex Optimization Cran Cran Task Views Cvxr Package Data Data Cleaning Data Flow. Documentation for the TensorFlow for R interface. In this post you will discover the dropout regularization technique and how to apply it to your models in Python with Keras. Activations that are more complex than a simple TensorFlow/Theano/CNTK function (eg. Keras models are made by connecting configurable building blocks together, with few restrictions. Loading The Data. So I recently got a new system and I need to go through all the hoops to get GPU support to work for Keras in R. Keras for R. To dive more in-depth into the differences between the Functional API and Model subclassing, you can read What are Symbolic and Imperative APIs in TensorFlow 2. I am able to compile. This is Part 2 of a MNIST digit classification notebook. R in terms of time required to train a convolutional neural network based model for image recognition. Keras Mask R-CNN. 04, 64 bit) I opened an R session and ran. Keras is a high-level neural networks API developed with a focus on enabling fast experimentation. Following is my code: import numpy as np import pandas. Regression Example with Keras in R Regression data can be easily fitted with a Keras Deep Learning API. Keras is the official high-level API of TensorFlow tensorflow. At the same time, R has a rich history as an open-source statistical computing project and is a mainstay of data science. Following is my code: import numpy as np import pandas. To run execute simple_request. [1] [2] Designed to enable fast experimentation with deep neural networks , it focuses on being user-friendly, modular, and extensible. Hot Network Questions Typesetting smilies in the input How to select pairs in a list of pairs where the second pair element is within. Regression with keras neural networks model in R. Keras models are made by connecting configurable building blocks together, with few restrictions. The CPU version is much easier to install and configure so is the best starting place especially when you are first learning how to use Keras. In this tutorial, we will present a few simple yet effective methods that you can use to build a powerful image classifier, using only very few training examples --just a few hundred or thousand pictures from each class you want to be able to recognize. The RNN model processes sequential data. Layer to implement our own model. in a 6-class problem, the third label corresponds to [0 0 1 0 0 0]) suited for classification. Keras on tensorflow in R & Python 1. Creating a sequential model in Keras. 5 using TensorFlow, Keras, and MXNet Deep learning is a powerful subset of machine learning that is very successful in domains such as computer vision and natural language processing (NLP). Keras is a high level library, used specially for building neural network models. One of the key points in Deep Learning is to understand Creating a sequential model in Keras. Continuous Wavelet Transform Python. Installing The keras Package. Activations that are more complex than a simple TensorFlow/Theano/CNTK function (eg. Getting Started Installation. In this tutorial, I will show how to use R with Keras with a tensorflow-gpu backend. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. It was developed with a focus on enabling fast experimentation. Continuing the series of articles on neural network libraries, I have decided to throw light on Keras - supposedly the best deep learning library so far. Keras code is portable, meaning that you can implement a neural network in Keras. Keras is a high-level neural networks API, written in Python and capable of running on top of either TensorFlow or Theano. Visualize The Model Training History. Dogs classifier (with a pretty small training set) based on Keras’ built-in ‘ResNet50’ model. R Interface to the Keras Deep Learning Library. Layer to implement our own model. Just tried to install keras but got a problem. The sequential API allows you to create models layer-by-layer for most problems. Adadelta is a more robust extension of Adagrad that adapts learning rates based on a moving window of gradient updates, instead of accumulating all past gradients. It seems that Keras with TensorFlow backend is the best choice for this question. Built-in support for. Nesterov accelerated gradient (NAG) Intuition how it works to accelerate gradient descent. If you need to implement some classical model, there is no difference. Enable CNTK as Keras back end. Keras bietet eine einheitliche Schnittstelle für verschiedene Backends, darunter TensorFlow, Microsoft Cognitive Toolkit (vormals CNTK) und Theano. 'Keras' provides specifications for describing dense neural networks, convolution neural networks (CNN) and recurrent neural networks (RNN) running on top of either 'TensorFlow' or 'Theano'. Keras and TensorFlow can be configured to run on either CPUs or GPUs. To install TensorFlow and Keras from R use install_keras() function. Stay tuned for Part 3 of this series which will be published next week. I execute the following code in Python import numpy as np from keras. Regression with keras neural networks model in R. Input shape. 463 installation of Python 3. 4 Full Keras API. Shirin Elsinghorst on how easy it is to build a CNN model in R using Keras. Mix-and-matching different API styles. This post demonstrates how easy it is to apply batch normalization to an existing Keras model and showed some training results comparing two models with and without batch normalization. Keras Tutorial: The Ultimate Beginner's Guide to Deep Learning in Python Share Google Linkedin Tweet In this step-by-step Keras tutorial, you'll learn how to build a convolutional neural network in Python!. I am trying to install Keras for R from the. It allows a small gradient when the unit is not active: f(x) = alpha * x for x < 0, f(x) = x for x >= 0. Being able to go from idea to result with the least possible delay is key to doing good research. A one-hot encoding is a representation of categorical variables (e. For those seeking an introduction to Keras in R, please check out Customer Analytics: Using Deep Learning With Keras To Predict Customer Churn. Logistic Regression (LR) is a simple yet quite effective method for carrying out binary classification tasks. Provides a consistent interface to the 'Keras' Deep Learning Library directly from within R. keras VGG-16 CNN and LSTM for Video Classification Example For this example, let's assume that the inputs have a dimensionality of (frames, channels, rows, columns) , and the outputs have a dimensionality of (classes). Documentation for the TensorFlow for R interface. 'Keras' provides specifications for describing dense neural networks, convolution neural networks (CNN) and recurrent neural networks (RNN) running on top of either 'TensorFlow' or 'Theano'. I would like to pass a vector that is outside of the training data, but the same length as the training data, to a custom loss function. 5 (61 ratings) Course Ratings are calculated from individual students' ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately. Thanks to Keras, performing deep learning on a very large number of Reddit submissions is actually pretty easy. You can learn more about R Keras from its official site. The easiest way to set up keras in an R session is to use: keras::install_keras() This will install keras, tensorflow and all other python dependencies you need, in a virtualenv where the R keras package knows where to find it. Keras is a high-level neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. This sequential layer framework allows the developer to easily bolt together layers, with the tensor outputs from each layer flowing easily and implicitly into the next layer. I execute the following code in Python import numpy as np from keras. The R interface to TensorFlow lets you work productively using the high-level Keras and Estimator APIs, and when you need more control provides full access to the core TensorFlow API:. First, to create an “environment” specifically for use with tensorflow and keras in R called “tf-keras” with a 64-bit version of Python 3. Allaireによる共著。. Keras has grown in popularity and supported on a wide set of platforms including Tensorflow, CNTK, Apple's CoreML, and Theano. TensorFlow™ is an open-source software library for Machine Intelligence. The use of R interfaces for TensorFlow and Keras with backends for choice (i. 0 release will be the last major release of multi-backend Keras. LSTM example in R Keras LSTM regression in R. For more detail, read about the integration with R. Fine-tune the pre-trained models. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs. We'd like to have a smarter ball, a ball that has a notion of where it is going so that it knows to slow down before the hill slopes up again. in a 6-class problem, the third label corresponds to [0 0 1 0 0 0]) suited for classification. 4%, I will try to reach at least 99% accuracy using Artificial Neural Networks in this notebook. Keras的核心数据结构是model,一种组织网络层的方式，最简单的数据模型是Sequential模型，它是由多个网络层线性堆叠的栈，对于更复杂的结构，你应该使用Keras函数式，它允许构建任意的神经. I am using package Keras in R to do a neural network. 6th October 2018 5th October 2018 Muhammad Rizwan VGG16, VGG16 - Implementation Using Keras, VGG16 Implementation 1- Introduction: Karen Simonyan and Andrew Zisserman investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. A model is a directed acyclic graph of layers. Regression Example with Keras in R Regression data can be easily fitted with a Keras Deep Learning API. 1 (259 ratings) Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately. Chollet (one of the Keras creators) Deep Learning with R by F. On the other hand, Keras is a high level API built on TensorFlow (and can be used on top of Theano too). From there, we'll review our directory structure for this project and then install Keras + Mask R-CNN on our system. keras-rl implements some state-of-the art deep reinforcement learning algorithms in Python and seamlessly integrates with the deep learning library Keras. Sequence guarantees the ordering and guarantees the single use of every input per epoch when using use_multiprocessing=True. We have keras_save and keras_load to save and load the entire object, keras_save_weights and keras_load_weights to store only the weights, and keras_model_to_json and keras_model_from_json to store only the model architecture. Keras and TensorFlow can be configured to run on either CPUs or GPUs. This talk introduces the new Keras interface for R. R lstm tutorial. The kerasformula package offers a high-level interface for the R interface to Keras. In Keras, the syntax is tf. Auxiliary Classifier Generative Adversarial Network, trained on MNIST. The functional API in Keras. Being able to go from idea to result with the least possible delay is key to doing good research. The code below creates an empty neural net model. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. What I did not show in that post was how to use the model for making predictions. Deep Learning with R [Francois Chollet, J. The easiest way to set up keras in an R session is to use:. The keras R package wraps the Keras Python Library that was expressly built for developing Deep Learning Models. powered by slackinslackin. Summary Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. learnable activations, which maintain a state) are available as Advanced Activation layers, and can be found in the module keras. Being able to go from idea to result with the least possible delay is key to doing good research. Keras models are made by connecting configurable building blocks together, with few restrictions. Keras Cheat Sheet: Neural Networks in Python Make your own neural networks with this Keras cheat sheet to deep learning in Python for beginners, with code samples. Sun 05 June 2016 By Francois Chollet. It supports convolutional networks (for computer vision), recurrent networks (for sequence processing), and any combination of both, as well as arbitrary network architectures: multi-input or multi-output models, layer sharing, model. Demonstrates how to build a variational autoencoder with Keras using deconvolution layers. A popular Python machine learning API. R interface to Keras. Last released: 2019/10/12 BERT implemented in Keras. Keras bietet eine einheitliche Schnittstelle für verschiedene Backends, darunter TensorFlow, Microsoft Cognitive Toolkit (vormals CNTK) und Theano. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both 'CPU' and 'GPU' devices. (2012) ) to find out the regions of interests and passes them to a ConvNet. neural-image-assessment - Implementation of NIMA: Neural Image Assessment in Keras #opensource. kerasR: R Interface to the Keras Deep Learning Library. Keras is now the recommended high level API and this post will focus on subclassing keras. Keras is an API used for running high-level neural networks. R Interface to the Keras Deep Learning Library. The guide Keras: A Quick Overview will help you get started. 6th October 2018 5th October 2018 Muhammad Rizwan VGG16, VGG16 - Implementation Using Keras, VGG16 Implementation 1- Introduction: Karen Simonyan and Andrew Zisserman investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. It is written in Python and is compatible with both Python – 2. In that article, we saw how we can perform sentiment analysis of user reviews regarding different movies on IMDB. Map categorical values to integer values. We will also see how data augmentation helps in improving the performance of the network. In this tutorial we will implement the skip-gram model created by Mikolov et al in R using the keras package. While the code in Keras gives me always an. Keras is a high-level neural networks API, written in Python and capable of running on top of either TensorFlow or Theano. For Windows: Choose a wheel URL from here [For Linux]: Install the prerequisites and then choose a wheel URL from here. Get unlimited access to the best stories on Medium — and support writers while you're at it. Keras has a useful utility titled "callbacks" which can be utilised to track all sorts of variables during training. One of the key points in Deep Learning is to understand Creating a sequential model in Keras. Convoluted Neural Networks. Inception v3, trained on ImageNet. LSTM example in R Keras LSTM regression in R. For R users, there hasn't been a production grade solution for deep learning (sorry MXNET). The kerasformula package offers a high-level interface for the R interface to Keras. - the cutting edge is always unknown. 采用 Conv2DTranspose 重建图像. He is driven by delivering great. The LSTM (Long Short-Term Memory) network is a type of Recurrent Neural networks (RNN). There are many models such as AlexNet, VGGNet, Inception, ResNet, Xception and many more which we can choose from, for our own task. Use Keras if you need a deep learning library that: Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility). Thanks to Keras, performing deep learning on a very large number of Reddit submissions is actually pretty easy. Following is my code: import numpy as np import pandas. For example: cat is mapped to 1, dog is mapped to 2, and. I am using package Keras in R to do a neural network. One simple trick to train Keras model faster with Batch Normalization | DLology. I am trying to use conv1D layer from Keras for predicting Species in iris dataset (which has 4 numeric features and one categorical target). I think Keras and Lasagne cater to different audiences. You can read about one participant's experience in my last workshop: Big Data - a buzz word you can find everywhere these days, from nerdy blogs to scientific research papers and even in the news. is_keras_available() Check if Keras is Available. If you wish to learn more about Keras and deep learning you can find my articles on that here and here. class: center, middle, inverse, title-slide # Keras: Deep Learning com R ## rday-keras. We need to represent a given sentence in a form that an algorithm can understand. Constructing the Model. Fine-tune the pre-trained models. Perangkat keras (hardware) komputer adalah alat-alat dari komputer yang dapat dilihat, disentuh atau berwujud/berbentuk untuk mendukung kinerja dari computer itu sendiri. Flexible Data Ingestion. Activations that are more complex than a simple TensorFlow/Theano/CNTK function (eg. Keras has the following key features:. Download the file for your platform. User-friendly API which makes it easy to quickly prototype deep learning models. If you need to implement some classical model, there is no difference. In this tutorial, we will present a few simple yet effective methods that you can use to build a powerful image classifier, using only very few training examples --just a few hundred or thousand pictures from each class you want to be able to recognize. You can now book me and my 1-day workshop on deep learning with Keras and TensorFlow using R. Pre-trained models and datasets built by Google and the community. Showing 1-20 of 1021 topics. Fine-tune the pre-trained models. The keras R package wraps the Keras Python Library that was expressly built for developing Deep Learning Models. Activations that are more complex than a simple TensorFlow/Theano/CNTK function (eg. One benefit of normalize is that is allows for normalizing arrays along arbitrary dimensions, a useful feature in convolutional and recurrent neural networks. It uses search selective ( J. We need to represent a given sentence in a form that an algorithm can understand. In the next example, we are stacking three dense layers, and keras builds an implicit input layer with your data, using the input_shape parameter. This makes it very easy for someone who has used Keras in any language to transition smoothly between other languages. Thanks to the use of Auto-Keras, R programmers with few lines of code will be able to train several deep learning models for their data and get the one that outperforms the others. The winners of ILSVRC have been very generous in releasing their models to the open-source community. The simplest model in Keras is the. A one-hot encoding is a representation of categorical variables (e. Input shape. 95) Adadelta optimizer. The main focus of Keras library is to aid fast prototyping and experimentation. Nesterov accelerated gradient (NAG) Intuition how it works to accelerate gradient descent. To run execute simple_request. First, to create an “environment” specifically for use with tensorflow and keras in R called “tf-keras” with a 64-bit version of Python 3. From a data science perspective, R has numerous packages helping implement deep learning models similar to the other machine learning models. Text classification isn't too different in terms of using the Keras principles to train a sequential or function model. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. R Keras allows us to build deep learning models just like we would using Keras in Python. In my workshop, you will learn the basics of deep learning what cross-entropy and loss is about activation functions how to optimize weights and biases with backpropagation and gradient descent how to build (deep) neural networks with Keras and TensorFlow how to save and load models and model weights. From there, we'll review our directory structure for this project and then install Keras + Mask R-CNN on our system. Predict Labels of. I'm looking for an equivalent function in R that works with the k. These functions provide methods for loading and saving a keras model. Creating a sequential model in Keras. R software works on both Windows and Mac-OS. July 10, 2016 200 lines of python code to demonstrate DQN with Keras. validation_split tells Keras what percentage of our training data to reserve for validation. Keras is a high-level neural networks API developed with a focus on enabling fast experimentation. From there, we'll review our directory structure for this project and then install Keras + Mask R-CNN on our system. About Keras in R. R software works on both Windows and Mac-OS. conda install linux-64 v2. Layer to implement our own model. Agenda • Introduction to neural networks &Deep learning • Keras some examples • Train from scratch • Use pretrained models • Fine tune. Keras is a higher-level framework wrapping commonly used deep learning layers and operations into neat, lego-sized building blocks, abstracting the deep learning complexities away from the precious eyes of a data scientist. The Keras Python library makes creating deep learning models fast and easy. TensorFlow or Keras? Which one should I learn? Aakash Nain. Specify "default" to install the latest release. Just to narrow it down as much as possible, these were my steps: installation of R 3. From a data science perspective, R has numerous packages helping implement deep learning models similar to the other machine learning models. get_file() Downloads a file from a URL if it not already in the cache. I decided to test performance of Python vs. So in total we’ll have an input layer and the output layer. RStudio is a user friendly environment for R. Text classification isn't too different in terms of using the Keras principles to train a sequential or function model. 5 I typed: conda create -n tf-keras python=3. How to develop a CNN using keras package in R? I am trying to develop a three class CNN classification model with dipeptide as input (2000 sequences, 400 features, 3 classes). Note that "virtualenv" is not available on Windows (as this isn't supported by TensorFlow). While we could use the R function scale, another option is the keras-specific function normalize, which we use here. One of its biggest advantages is its "user friendliness". You can also use it to create checkpoints which saves the model at different stages in training to help you avoid work loss in case your poor overworked computer decides to crash. A dict mapping input names to the corresponding array/tensors, if the model has named inputs. The use of keras. Sunspots are dark spots on the sun, associated with lower temperature. Regression data can be easily fitted with a Keras Deep Learning API. 2 installation of RStudio 1. packages("keras") The Keras R interface uses the TensorFlow backend engine by default. The R interface to TensorFlow lets you work productively using the high-level Keras and Estimator APIs, and when you need more control provides full access to the core TensorFlow API:. Auto-Keras is an open source software library for automated machine learning (AutoML). com R Interface to 'Keras' Interface to 'Keras' , a high-level neural networks 'API'. I used the same architecture for both Keras and Tensorflow. It allows a small gradient when the unit is not active: f(x) = alpha * x for x < 0, f(x) = x for x >= 0. You can now book me and my 1-day workshop on deep learning with Keras and TensorFlow using R. advanced_activations. The model is trained for 500 epochs, recording training and validation accuracy in a keras_training_history object. The model runs on top of TensorFlow, and was developed by Google. r, update gate, z, and the proposal state. keras-package R interface to Keras Description Keras is a high-level neural networks API, developed with a focus on enabling fast experimentation. The skip-gram model is a flavor of word2vec, a class of computationally-efficient predictive models for learning word embeddings from raw text. Keras is a high-level neural networks API, developed with a focus on enabling fast experimentation. Predict Labels of. Need to understand the working of 'Embedding' layer in Keras library. After reading this post you will know: How the dropout regularization. Otherwise specify an alternate version. Training a CNN Keras model in Python may be up to 15% faster compared to R. Showing 1-20 of 1021 topics. Keras is a popular programming framework for deep learning that simplifies the process of building deep learning applications. Provides a consistent interface to the 'Keras' Deep Learning Library directly from within R. In my previous article, I explained how to create a deep learning-based movie sentiment analysis model using Python’s Keras library. For those seeking an introduction to Keras in R, please check out Customer Analytics: Using Deep Learning With Keras To Predict Customer Churn. First, install the required Python packages:. The winners of ILSVRC have been very generous in releasing their models to the open-source community. Being able to go from idea to result with the least possible delay is key to doing good research. Keras is the official high-level API of TensorFlow tensorflow. Just tried to install keras but got a problem. Keras is a Deep Learning package built on the top of Theano, that focuses on enabling fast experimentation. Keras is a popular programming framework for deep learning that simplifies the process of building deep learning applications. Chollet and J. The keras R package wraps the Keras Python Library that was expressly built for developing Deep Learning Models. I am using package Keras in R to do a neural network. It tries to find out the areas that might be an object by combining similar pixels and textures into several rectangular boxes. txt) or view presentation slides online. Keras models are made by connecting configurable building blocks together, with few restrictions. R深度学习——Keras入门在人工智能时代到来之际，最成熟的AI框架莫过于Tensorflow，但其构建是基于Python的，但我们作为熟练R语言的数据科学家，也想使用Tensorflow，这一梦想R. 6m) pro-rata to its shareholders]. This means that evaluating and playing around with different algorithms is easy. Saving & Loading Keras Models Jovian Lin Given that deep learning models can take hours, days, or weeks to train, it is paramount to know how to save and load them from disk. This is the value that we want the Keras model to learn to predict for state s and action a i. I used the same architecture for both Keras and Tensorflow. This function adds an independent layer for each time step in the recurrent model. This sequential layer framework allows the developer to easily bolt together layers, with the tensor outputs from each layer flowing easily and implicitly into the next layer. Transformer implemented in Keras. activation , loss and optimizer are the parameters that define the characteristics of the neural network, but we are not going to discuss it here. Adadelta is a more robust extension of Adagrad that adapts learning rates based on a moving window of gradient updates, instead of accumulating all past gradients. This post introduces. In that article, we saw how we can perform sentiment analysis of user reviews regarding different movies on IMDB. The comparison for using the keras model across the 2 languages will be addressing the classic image classification problem of cats vs dogs. Flexible Data Ingestion. Keras: One-hot Encode/Decode Sequence Data. Summary Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. If you want to use the GPU version you have to install some prerequisites first. io on Slack. I´m developing a regression Model in Keras for R. Provides a consistent interface to the 'Keras' Deep Learning Library directly from within R. The post ends by providing some code snippets that show Keras is intuitive and powerful.