Keras Backend Print Tensor

models import Sequential from keras import layers from keras. 5; osx-64 v2. Aliases: Module tf. Instead of providing all the functionality itself, it uses either TensorFlow or Theano behind the scenes and adds a standard, simplified programming interface on top. This is also the last major release of multi-backend Keras. models import Sequential. Note that print_tensor returns a new tensor identical to x which should be used in the following code. Alternately, how do I rewrite this function in Keras? I shouldn't ever need to use the Theano backend, so it isn't necessary for me to rewrite my function in Keras. 이 포스트는 케라스 창시자에게 배우는 딥러닝 (Machine Learning with Python)의 내용 중 3. Please ask usage questions on stackoverflow, slack, or the google group. This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e. 我们也可以通过定义环境变量KERAS_BACKEND来覆盖上面配置文件中定义的后端:. * Dense (fully connected) layer with input of 20 dimension vectors, which means you have 20 columns in your data. 1; win-64 v2. The network. Keras 模型对象. 将backend字段的值改写为你需要使用的后端:theano或tensorflow,即可完成后端的切换. Pre-trained models and datasets built by Google and the community. Keras runs on a single node using the GPUs. 以下のコードを実行すると AttributeError: module 'tensorflow. Develop Your First Neural Network in Python With this step by step Keras Tutorial!. Yesterday, the Keras team announced the release of Keras 2. import numpy as np import keras. For a single GPU, the difference is about 15%. From the official TensorFlow model optimization documentation. Keras is an open-source neural-network library written in Python. print_tensor( x, message= '') Note that print_tensor returns a new tensor identical to x which should be used in the following code. batch_dot(x, y, axes=None) Batchwise dot product. one_hot), but this has a few caveats - the biggest one being that the input to K. cast(x, dtype) Casts a tensor to a different dtype and returns it. utils import multi_gpu_model from keras. A "Keras tensor" is a tensor that was returned by a Keras layer, (Layer class) or by Input. Note that print_tensor returns a new tensor identical to x which should be used in the following code. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research. 継承元: Dense 、 Layer tensorflow/python/keras/_impl/keras/layers/core. 如果你的模型包含这样的层,你需要指定你希望模型工作在什么模式下,通过Keras的backend你可以了解当前的工作模式: from keras import backend as K print K. lancaster import LancasterStemmer stemmer = LancasterStemmer() # things we need for Tensorflow import numpy as np from keras. layers, no matter how I defined the input of the model. A simple example showing how to explain an MNIST CNN trained using Keras with DeepExplainer. Keras and Theano have been installed on the Power-8 cluster (Panther) and set up to use the K80 GPUs there. TensorFlow or Keras? Which one should I learn? Queues are a powerful mechanism for computing tensors asynchronously in a graph. A few months ago I demonstrated how to install the Keras deep learning library with a Theano backend. Let's see how. a Inception V1). sequence_categorical_column_with_identity tf. layers import Dense, Activation from tensorflow. , **, /, //, % for Theano. Find this and other hardware projects on Hackster. I've shuffled the training set, divided it. It does not handle itself low-level operations such as tensor products, convolutions and so on. Keras is compact, easy to learn, high-level Python library run on top of TensorFlow framework. It has become time for Keras to take advantage of these advances. Note that this behavior is specific to Keras dot. print_tensor, but the output was truncated to 3 values from the tensor. Deep Learning for humans. The latter just implement a Long Short Term Memory (LSTM) model (an instance of a Recurrent Neural Network which avoids the vanishing gradient problem). k_print_tensor: Prints 'message' and the tensor value when evaluated. Instead, it relies on a specialized, well-optimized tensor manipulation library to do so, serving as the "backend engine" of Keras. In keras: R Interface to 'Keras' Description Usage Arguments Value Keras Backend. This is a useful tool when trying to understand what is going on inside the layers of a neural network. No idea what the problem is. Contribute to explore this is a valid definition of an additional vector to learn how to. Installing Keras, Theano and TensorFlow with GPU on Windows 8. It helps researchers to bring their ideas to life in least possible time. I proceeded to dig deeper: tf. sequence_input_layer tf. 이 포스트는 케라스 창시자에게 배우는 딥러닝 (Machine Learning with Python)의 내용 중 3. Given an input tensor, returns a new tensor with the same values as the input tensor with shape shape. So, the "backend engine" will perform the computation and development of the models. CHRYSLER OEM Front Seat-Cushion Cover-Top Back Left 1JL661J8AA,EBC Brakes S13KF1432 S13 Kits Yellowstuff and RK Rotors,NAJS3T 3 Ton Aluminum Ratcheting Jack Stands. There is nothing which is currently present in deep learning equivalent to PyTorch in this regard. Keras Tutorial, Keras Deep Learning, Keras Example, Keras Python, keras gpu, keras tensorflow, keras deep learning tutorial, Keras Neural network tutorial, Keras shared vision model, Keras sequential model, Keras Python tutorial. Keras 模型对象. They are extracted from open source Python projects. Interestingly, Keras has a modular design, and you can also use Theano or CNTK as backend engines. Returns: A variable instance (with Keras metadata included). sequence_categorical_column_with_hash_bucket tf. TensorFlow is an open-source software library. io ) § High-level API § Focus on user experience § “Deep learning accessible to everyone” § History § Announced at Feb. batch_dot keras. A tutorial about setting up Jetson TX2 with TensorFlow, OpenCV, and Keras for deep learning projects. Creating a Deep Learning iOS App with Keras and Tensorflow Take the Food Classifier that we trained last time around and export and prepare it to be used in an iPhone app for real-time classification. When using TensorFlow as backend, Keras CNNs require a 4D array (which we'll also refer to as a 4D tensor) as input, with shape. Keras is a high-level neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. Lancaster stemming library is used to collapse distinct word forms: import nltk from nltk. Author: Yuwei Hu. run commands and tensorflow sessions, I was sort of confused. Deep Learning for humans. Installing Keras, Theano and TensorFlow with GPU on Windows 8. Install Keras with GPU TensorFlow as backend on Ubuntu 16. backend APIs. where nb_samples corresponds to the total number of images (or samples), and rows, columns, and channels correspond to the number of rows, columns, and channels for each image, respectively. Keras is a Python library that provides, in a simple way, the creation of a wide range of Deep Learning models using as backend other libraries such as TensorFlow, Theano or CNTK. The Keras code calls into the TensorFlow library, which does all the work. Contribute to keras-team/keras development by creating an account on GitHub. , **, /, //, % for Theano. function函数tf. Instead, it relies on a specialized, well-optimized tensor manipulation library to do so, serving as the "backend engine" of Keras. function creates theano/tensorflow tensor functions which is later used to get the output from the symbolic graph given the input. learning_phase() 向feed_dict中传递1(训练模式)或0(测试模式)即可指定当前工作模式:. For an introductory discussion of Graphical Processing Units (GPU) and their use for intensive parallel computation purposes, see GPGPU. Note that print_tensor returns a new tensor identical to x which should be used in the following code. 7GB + 1GB of swap. Note that print_tensor returns a new tensor identical to x which should be used in the following code. from keras. 2014] on the "Frey faces" dataset, using the keras deep-learning Python library. Develop Your First Neural Network in Python With this step by step Keras Tutorial!. input_tensor: optional Keras tensor (i. 4 (with 60% validation accuracy). axis: Position where to add a new axis. Returns: A variable instance (with Keras metadata included). 2 module or as a backend to the keras/1. one_hot must be an integer tensor, but by default Keras passes around float tensors. tensor as T import keras import numpy as np import matplotlib. I proceeded to dig deeper: tf. from __future__ import absolute_import, division, print_function import tensorflow as tf tf. batch_dot(x, y, axes=None) Batchwise dot product. Print() and. TensorFlow argument and how it's the wrong question to be asking. cast_to_floatx (): Cast a Numpy array to the default Keras float type. sequence_categorical_column_with_vocabulary_list tf. The keras package contains the following man pages: activation_relu application_densenet application_inception_resnet_v2 application_inception_v3 application_mobilenet application_mobilenet_v2 application_nasnet application_resnet50 application_vgg application_xception backend bidirectional callback_csv_logger callback_early_stopping callback_lambda callback_learning_rate_scheduler callback. print_tensor, but the output was truncated to 3 values from the tensor. returns a tensor of size. Working with Keras is easy as working with Lego blocks. Otherwise the print operation is not taken into account during evaluation. I have written a rather complex loss function for a Keras model and it keeps returning nan while training. models import Sequential, Model Using TensorFlow backend. exp exp( x, name=None ) Defined in tensorflow/python/ops/gen_math_ops. tensor: A "tensor" is like a matrix but with an arbitrary number of dimensions. ResNet-50 training throughput (images per second) comparing Keras using the MXNet backend (green bars) to a native MXNet implementation (blue bars). dtype: Tensor type. Customizing keras is define and that calculates the. OK, I Understand. You can vote up the examples you like or vote down the ones you don't like. Keras with Theano Backend. import numpy as np import keras. When it comes to Keras, it’s not working independently. print_tensor( x, message= '') Note that print_tensor returns a new tensor identical to x which should be used in the following code. applications. A tutorial about setting up Jetson TX2 with TensorFlow, OpenCV, and Keras for deep learning projects. Run your Keras models in C++ Tensorflow So you've built an awesome machine learning model in Keras and now you want to run it natively thru Tensorflow. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, Theano, or PlaidML. Django整合Keras报错:ValueError: Tensor Tensor("Placeholder:0", shape=(3, 3, 1, 32), dtype=float32) is not an element of this graph. The following are code examples for showing how to use keras. 케라스 소개 케라스는 거의 모든 종류의 딥러닝 모델을 간편하게 만들고 훈련시킬 수 있는 파이썬을 위한 딥러닝 프레임워크입니다. We have detected your current browser version is not the latest one. They are extracted from open source Python projects. 今回はTensorFlow + Kerasで機械学習するための環境構築からサンプルコードの実行までを行いました。 Kerasはシンプルに実装できそうでいい感じですね。 色々試してみたいと思います!. print_tensor(x, message="x is: ") Arguments. eval in your loss function because the tensors are not initialized. Keras后端 什么是"后端" Keras是一个模型级的库,提供了快速构建深度学习网络的模块。Keras并不处理如张量乘法、卷积等底层操作。. In addition, the Keras model can inference at 60 FPS on Colab's Tesla K80 GPU, which is twice as fast as Jetson Nano, but that is a data center card. It is a reproduction of. rnn_TensorFlow官方文档_w3cschool 下载APP 随时随地学编程. TensorFlow, CNTK, Theano, etc. I’m Francois. Package 'keras' October 8, 2019 Type Package Title R Interface to 'Keras' Version 2. The keras package contains the following man pages: activation_relu application_densenet application_inception_resnet_v2 application_inception_v3 application_mobilenet application_mobilenet_v2 application_nasnet application_resnet50 application_vgg application_xception backend bidirectional callback_csv_logger callback_early_stopping callback_lambda callback_learning_rate_scheduler callback. But recently I started to migrate to a pure Tensorflow approach, and I'm not getting good results, what is strange, since I'm using the TF backend in Keras, so I was expecting similar results. ResNet-50 training throughput (images per second) comparing Keras using the MXNet backend (green bars) to a native MXNet implementation (blue bars). axis: Position where to add a new axis. Keras Tutorial, Keras Deep Learning, Keras Example, Keras Python, keras gpu, keras tensorflow, keras deep learning tutorial, Keras Neural network tutorial, Keras shared vision model, Keras sequential model, Keras Python tutorial. TensorFlow program that uses tensorflow. This is a useful tool when trying to understand what is going on inside the layers of a neural network. To understand the concept of a backend, consider building a website from scratch. Description. Let's try to re-implement the Logistic Regression Model using the keras. To use Keras sequential and functional model styles. Keras Backend. The default proposed solution is to use a Lambda layer as follows: Lambda(K. print_tensor(x, message='') 在评估时打印 message 和张量的值。 请注意, print_tensor 返回一个与 x 相同的新张量,应该在后面的代码中使用它。. In this notebook we will be using the Keras backend module, which provides an abstraction over both Theano and Tensorflow. ResNet-50 training throughput (images per second) comparing Keras using the MXNet backend (green bars) to a native MXNet implementation (blue bars). I had a hard time understanding what Keras tensors really were. one_hot), but this has a few caveats - the biggest one being that the input to K. Print() and. The folder structure of image recognition code implementation is as shown below − The dataset. When it comes to Keras, it's not working independently. io/) is a high-level neural networks API, written in Python and capable of running on top of either TensorFlow or Theano. 7GB + 1GB of swap. You can vote up the examples you like or vote down the ones you don't like. I made a simple piece of code to show what I mean. A 2-dimensions tensor is a matrix. FRANCOIS CHOLLET: Hello, everyone. They are used in a lot of more advanced use of Keras but I couldn't find a simple explanation of what they mean inside Keras. Java调用Keras、Tensorflow模型 2018-04-03; 3,265; 实现python离线训练模型,Java在线预测部署。 目前深度学习主流使用python训练自己的模型,有非常多的框架提供了能快速搭建神经网络的功能,其中Keras提供了high-level的语法,底层可以使用tensorflow或者theano。. layers and the keras. _add_inbound_node(). sequence_categorical_column_with_identity tf. It works as an upper layer for prevailing deep learning frameworks; namely with TensorFlow, Theano & CNTK (MXNet backend for Keras is on the way). 케라스 소개 케라스는 거의 모든 종류의 딥러닝 모델을 간편하게 만들고 훈련시킬 수 있는 파이썬을 위한 딥러닝 프레임워크입니다. Asserts and boolean checks BayesFlow Entropy BayesFlow Monte Carlo BayesFlow Stochastic Graph BayesFlow Stochastic Tensors BayesFlow Variational Inference Building Graphs Constants, Sequences, and Random Values Control Flow Copying Graph Elements CRF Data IO FFmpeg Framework Graph Editor Higher Order Functions Histograms Images Inputs and. Purchase Order Number SELECT PORDNMBR [Order ID], * FROM PM10000 WITH(nolock) WHERE DEX_ROW_TS > '2019-05-01';. Keras is a model-level library, providing high-level building blocks for developing deep-learning models. To use Keras and Tensor Processing Units (TPUs) to build your custom models faster. This is a summary of the official Keras Documentation. Find this and other hardware projects on Hackster. import numpy as np import pandas as pd import theano import theano. Python keras. print_tensor( x, message= '') Note that print_tensor returns a new tensor identical to x which should be used in the following code. It does not handle itself low-level operations such as tensor products, convolutions and so on. Print() and. abs(): Element-wise absolute value. Dynamically switch Keras backend in Jupyter notebooks Christos - Iraklis Tsatsoulis January 10, 2017 Keras 5 Comments Recently, I was looking for a way to dynamically switch Keras backend between Theano and TensorFlow while working with Jupyter notebooks; I thought that there must be a way to work with multiple Keras configuration files , but. Package 'keras' October 8, 2019 Type Package Title R Interface to 'Keras' Version 2. input_tensor: optional Keras tensor (i. It is backward-compatible with TensorFlow 1. At most one component of shape can be -1. They are used in a lot of more advanced use of Keras but I couldn't find a simple explanation of what they mean inside Keras. Keras is a model-level library, providing high-level building blocks for developing deep learning models. A fast-paced introduction to Deep Learning that starts with a simple yet complete neural network (no frameworks), followed by an overview of activation functions, cost functions, backpropagation, and then a quick dive into CNNs. This is a useful tool when trying to understand what is going on inside the layers of a neural network. Casts a tensor to a different dtype and returns it. Session() `print. Working with Keras is easy as working with Lego blocks. Django整合Keras报错:ValueError: Tensor Tensor("Placeholder:0", shape=(3, 3, 1, 32), dtype=float32) is not an element of this graph. value: Numpy array, initial value of the tensor. Keras is a high-level neural networks API, developed with a focus on enabling fast experimentation and not for final products. mean时,默认axis=None,会在整个batch级别做平均 完整的测试代码,可以尝试变更Lamda层的注释体会默认batch级别平均的坑爹之处. The latter just implement a Long Short Term Memory (LSTM) model (an instance of a Recurrent Neural Network which avoids the vanishing gradient problem). A blog about software products and computer programming. weights: one of `None` (random initialization) or "imagenet" (pre-training on ImageNet). Note that print_tensor returns a new tensor identical to x which should be used in the following code. we can use the operations supported by Keras backend such as dot, transpose, max, pow, sign, etc as well as those are not specified in the backend documents but actually supported by Theano and TensorFlow - e. feature_column. 継承元: Dense 、 Layer tensorflow/python/keras/_impl/keras/layers/core. Aliases: Module tf. Note that this behavior is specific to Keras dot. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as. backend; Functions. rnn_TensorFlow官方文档_w3cschool 下载APP 随时随地学编程. 0, which is the first release of multi-backend Keras with TensorFlow 2. - We update the _keras_shape of every input tensor with its new shape (obtained via self. from __future__ import print_function import keras from keras. layers import Input, Dense >>> np_var = numpy. 2014] on the "Frey faces" dataset, using the keras deep-learning Python library. py定義されています。. Keras Tutorial, Keras Deep Learning, Keras Example, Keras Python, keras gpu, keras tensorflow, keras deep learning tutorial, Keras Neural network tutorial, Keras shared vision model, Keras sequential model, Keras Python tutorial. One of Theano's design goals is to specify computations at an abstract level, so that the internal function compiler has a lot of flexibility about how to carry out those computations. Keras Backend. shape(x) to get the shape of a tensor or use model. [2] 다음 단계에서는 Loss Function, Optimizer, Accuracy Metrics를 정의하고 학습시킨다. It does not handle itself low-level operations such as tensor products, convolutions and so on. I have written a rather complex loss function for a Keras model and it keeps returning nan while training. To understand the concept of a backend, consider building a website from scratch. 2018 Leaf Legends Of Wrestling #4 Bret Hart & Kevin Nash Dual Autograph Card,1969 Green Bay Packers vs Atlanta Falcons Football Program T*,VIPER 7756V 2-WAY LCD REMOTE CONTROL FOR 3606V 3706V 4606V 4706V 5606V 5706V. The aim of this tutorial is to show the use of TensorFlow with KERAS for classification and prediction in Time Series Analysis. They are extracted from open source Python projects. from keras. 4 § Characteristics § "Simplified workflow for TensorFlow users. It depends on your input layer to use. Instead of providing all the functionality itself, it uses either TensorFlow or Theano behind the scenes and adds a standard, simplified programming interface on top. 04 LTS with CUDA 8 and a NVIDIA TITAN X (Pascal) GPU, but it should work for Ubuntu Desktop 16. learning_phase() 向feed_dict中传递1(训练模式)或0(测试模式)即可指定当前工作模式:. layers and the keras. pyplot as plt from sklearn. cast(x, dtype) Casts a tensor to a different dtype and returns it. The good news about Keras and TensorFlow is that you don't need to choose between them! The default backend for Keras is TensorFlow and Keras can be integrated seamlessly with TensorFlow workflows. a Inception V1). feature_column. A backend is a computational engine — it builds the network graph/topology, runs the optimizers, and performs the actual number crunching. Keras is a popular programming framework for deep learning that simplifies the process of building deep learning applications. import tensorflow as tf from keras import backend as K x = K. Put another way, you write Keras code using Python. We have detected your current browser version is not the latest one. The keras/1. It has become time for Keras to take advantage of these advances. I proceeded to dig deeper: tf. To use the tf. Convolutional variational autoencoder with PyMC3 and Keras¶ In this document, I will show how autoencoding variational Bayes (AEVB) works in PyMC3's automatic differentiation variational inference (ADVI). all(): Bitwise reduction (logical AND). Package 'keras' October 8, 2019 Type Package Title R Interface to 'Keras' Version 2. function creates theano/tensorflow tensor functions which is later used to get the output from the symbolic graph given the input. The network. HOW I GOT KERAS + TENSORFLOW WORKING ON MY MAC OS 10. Keras ( https://keras. 0's beginner tutorial. Install Keras with GPU TensorFlow as backend on Ubuntu 16. models import Sequential from keras import layers from keras. There is nothing which is currently present in deep learning equivalent to PyTorch in this regard. Running on Power-8 Panther or Paragon. Both are very powerful libraries, but both can be difficult to use directly for creating deep learning models. If you have already built a model, you can use the model. placeholder(shape=(2, 4, 5)) >>> input_ph. backend 模块, get_session() 实例源码. Description. Given an input tensor, returns a new tensor with the same values as the input tensor with shape shape. In particular, a shape of [-1] flattens into 1-D. Using the GPU¶. TensorFlow, CNTK, Theano, etc. GoogLeNet in Keras. Package 'keras' October 8, 2019 Type Package Title R Interface to 'Keras' Version 2. io ) § High-level API § Focus on user experience § “Deep learning accessible to everyone” § History § Announced at Feb. It was developed and maintained by François Chollet , an engineer from Google, and his code has been released under the permissive license of MIT. @Falkenjack Use keras. 2017 § Bundled as an contribution package from TF 1. shape) output: (1,2,16) However, in Keras version 2. It does not handle itself low-level operations such as tensor products, convolutions and so on. 我们也可以通过定义环境变量KERAS_BACKEND来覆盖上面配置文件中定义的后端:. rnn_TensorFlow官方文档_w3cschool 下载APP 随时随地学编程. When it comes to Keras, it’s not working independently. 7GB + 1GB of swap. It is made with focus of understanding deep learning techniques, such as creating layers for neural networks maintaining the concepts of shapes and mathematical details. A simple example showing how to explain an MNIST CNN trained using Keras with DeepExplainer. You are going to learn step by step how to freeze and convert your trained Keras model into a single TensorFlow pb file. Aliases: Module tf. The network. io ) § High-level API § Focus on user experience § "Deep learning accessible to everyone" § History § Announced at Feb. Let's try to re-implement the Logistic Regression Model using the keras. x: A tensor or variable. Pre-trained models and datasets built by Google and the community. But I'll use whatever works. TensorFlow, CNTK, Theano, etc. Front Page DeepExplainer MNIST Example¶. 4, these two functions produce identical results. squeeze keras. Print , which, according to the documentation has a summarize parameter one cannot set through keras. In this tutorial we will be using python3. is_keras_tensor (k_var) # A variable indirectly created outside of keras is not a Keras tensor. And I work on the Keras team. Keras Tutorial, Keras Deep Learning, Keras Example, Keras Python, keras gpu, keras tensorflow, keras deep learning tutorial, Keras Neural network tutorial, Keras shared vision model, Keras sequential model, Keras Python tutorial. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, Theano, or PlaidML. shape) output: (1,2,16) However, in Keras version 2. You shouldn't need to do anything like that using the backend, as Keras will take strings as arguments, or you can use a regular print function. OK, I Understand. backend 模块, get_session() 实例源码. It does not handle itself low-level operations such as tensor products, convolutions and so on. Both are very powerful libraries, but both can be difficult to use directly for creating deep learning models. 1; win-32 v2. In fact, it's even possible to: 1) define a Keras model with the Theano backend 2) switch to the TensorFlow backend, 3) re-apply. image import ImageDataGenerator from keras. The network. Keras is a popular programming framework for deep learning that simplifies the process of building deep learning applications. Prints message and the tensor value when evaluated. 1 and 10 in less than 4 hours Introduction If you want to install the main deep learning libraries in 4 hours or less and start training your own models you have come to the right place. 4 § Characteristics § “Simplified workflow for TensorFlow users, more powerful features to Keras users” § Most Keras code can be used on TensorFlow (with keras. This article is an introductory tutorial to deploy keras models with Relay. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research. However, Keras is used most often with TensorFlow. Nov 18, and develop state-of-the-art models. layers and the keras. In this relatively short post, I'm going to show you how to deal with metrics and summaries in TensorFlow 2. Therefore, I need to print the intermediate tensors while training. Keras Tutorial: Keras is a powerful easy-to-use Python library for developing and evaluating deep learning models. Keras is compact, easy to learn, high-level Python library run on top of TensorFlow framework. Front Page DeepExplainer MNIST Example¶. There are certain special formatting options in Keras, for example, saving model weights during training, inserting the current epoch and validation loss into the name to help give them meaning. It works as an upper layer for prevailing deep learning frameworks; namely with TensorFlow, Theano & CNTK (MXNet backend for Keras is on the way). With powerful numerical platforms Tensorflow and Theano, Deep Learning has been predominantly a Python. It depends on your input layer to use. Keras is a Python library that provides, in a simple way, the creation of a wide range of Deep Learning models using as backend other libraries such as TensorFlow, Theano or CNTK.