
Instead of trying to figure out the perfect combination of neural network layers to recognize flowers, we will first use a technique called transfer learning to adapt a powerful pretrained model to our dataset. I am using Keras to train a deep neural network. We will go through this example because it won't consume your GPU, and your cloud budget to. What I did not show in that post was how to use the model for making predictions. This results in the training nodes being assigned a weight of 1 during training, and the nodes outside the training set being assigned a weight of 0. Introduction to Keras. 0 is the first release of multibackend Keras that supports TensorFlow 2. Recently, I came across this blogpost on using Keras to extract learned features from models and use those to cluster images. Now, to balance this how should I assign class weights?. Few lines of keras code will achieve so much more than native Tensorflow code. Keras provides a language for building neural networks as connections between general purpose layers. Develop Your First Neural Network in Python With this step by step Keras Tutorial!. balanced_batch_generator¶ imblearn. Keras is a bit unusual because it's a highlevel wrapper over TensorFlow. From Keras docs: class_weight : Optional dictionary mapping class indices (integers) to a weight (float) value, used for weighting the loss function (during training only). Today we'll train an image classifier to tell us whether an image contains a dog or a cat, using TensorFlow's eager API. Image Recognition (Classification). models import Sequential from keras. I created it by converting the GoogLeNet model from Caffe. A course on Coursera, by Andrew NG. Here is a very simple example for Keras with data embedded and with visualization of dataset, trained result, and errors. Put another way, you write Keras code using Python. For more details on how to add different layers refer to keras website. 0 入门教程持续更新： Doit：最全Tensorflow 2. LambdaCallback(). Ideally we can find weights for Keras directly but often this is not the case. The following figure shows 225 sample images from the dataset. classes: ndarray. It was developed with a focus on enabling fast experimentation. An image classification system built with transfer learning The basic technique to get transfer learning working is to get a pretrained model (with the weights loaded) and remove final fullyconnected layers from that model. You can store the whole model (model definition, weights and training configuration) as HDF5 file, just the model configuration (as JSON or YAML file) or just the weights (as HDF5 file). With the stateful model. layers import Embedding, LSTM, Dropout, Dense from keras. py has an example of sample_weights are being applied. But in cases such as a graph recurrent. 評価を下げる理由を選択してください. Instead, we write a mime model: We take the same weights, but packed as a stateless model. Good software design or coding should require little explanations beyond simple comments. Example of Deep Learning With R and Keras Recreate the solution that one dev created for the Carvana Image Masking Challenge, which involved using AI and image recognition to separate photographs. It's good to do the following before initializing Keras to limit Keras backend TensorFlow to use first GPU. If you want to give each sample a custom weight for consideration then using sample_weight is considerable. Keras comes bundled with many models. Let's grab the Dogs vs Cats dataset from Microsoft. Keras is a simpletouse but powerful deep learning library for Python. This code sample creates a 2D convolutional layer in Keras. If the machine on which you train on has a GPU on 0, make sure to use 0 instead of 1. In models relying on sparse features, L 1 regularization helps drive the weights of irrelevant or barely relevant features to exactly 0, which removes those features from the model. Defining it as none initializes weights randomly. WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. Keras documentation describes 'stateful' as "Boolean (default False). When you want to do some tasks every time a training/epoch/batch, that's when you need to define your own callback. 0 Using Theano backend. Neural network are very sensitive to nonnormalized data. Overview Finetuning is one of the important methods to make bigscale model with a small amount of data. models import Sequential layer = LSTM(500) # 500 is hidden size. This article is intended to target newcomers who are interested in Reinforcement Learning. Luckily the scipy. Additional information How many layers, how big ? Selecting layer sizes is more of an art than a science. Use our tool to log hyperparameters and output metrics from your runs, then visualize and compare results and quickly share findings with your colleagues. Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Here I will be using Keras[1] to build a Convolutional Neural network for classifying hand written digits. sample_weights is defined on a persample basis and is independent from the class. save_weights(…). [Update: The post was written for Keras 1. 2014] on the "Frey faces" dataset, using the keras deeplearning Python library. crossentropy for keras (2. There are many examples for Keras but without data manipulation and visualization. But this method seems outdated for the latest version of Keras. Understanding XOR with Keras and TensorFlow. It is important to have a rather small batch size and to scale the count data. The wrapped models aim to expose the same fit methods as the original Keras models in order to make their use as simple as possible. [Update: The post was written for Keras 1. Is there a way in Keras to apply different weights to a cost function in different examples? feature into keras itself? Since we already have sample weighting in. We do this in two stages: In the first run, with the embedding layer weights frozen, we allow the rest of the network to learn. Obtain weights from LSTM¶ Philippe Rémy commented how to obtain weights for forgate gatesm input gates, cell states and output gates. save_weights method. fit() and keras. Keras is a neural network API that is written in Python. Instead, we write a mime model: We take the same weights, but packed as a stateless model. This blog is my first ever step towards applying deep learning techniques to Image data. text import Tokenizer from keras. name of a loss function. predict needs a complete batch, which is not convenient here. , we will get our hands dirty with deep learning by solving a real world problem. The following is a list of deviations or additions: class_weights, sample_weights are not supported; fit_generator accepts a batch_size argument; fit_generator is not supported by all ImportanceTraining classes. Szegedy, Christian, et al. Obtain weights from LSTM¶ Philippe Rémy commented how to obtain weights for forgate gatesm input gates, cell states and output gates. You can find the clear documentation of the Keras which is also simple. Add a couple lines of code to your training script and we'll keep track of your hyperparameters, system metrics, and outputs so you can compare experiments, see live graphs of training, and easily share your findings with colleagues. BalancedBatchGenerator (X, y, sample_weight=None, sampler=None, batch_size=32, keep_sparse=False, random_state=None) [source] ¶ Create balanced batches when training a keras model. That includes cifar10 and cifar100 small color images, IMDB movie reviews, Reuters newswire topics. save_weights method. In this post, we'll build a simple Convolutional Neural Network (CNN) and train it to solve a real problem with Keras. Artificial neural networks have been applied successfully to compute POS tagging with great performance. In this post, we'll learn how to fit and predict regression data with a keras LSTM model in R. 0 API on March 14, 2017. An indepth introduction to using Keras for language modeling; word embedding, recurrent and convolutional neural networks, attentional RNNs, and similarity metrics for vector embeddings. Today we'll train an image classifier to tell us whether an image contains a dog or a cat, using TensorFlow's eager API. " Feb 11, 2018. In this tutorial, we will walk you through the process of solving a text classification problem using pretrained word embeddings and a convolutional neural network. Index values in weights not found in sampled object will be ignored and index values in sampled object not in weights will be assigned weights of zero. Throughout this book, the code examples use Keras (https://keras. *FREE* shipping on qualifying offers. Keras was specifically developed for fast execution of ideas. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. Coding Inception Module using Keras. If the model has multiple outputs, you can use a different sample_weight_mode on each output by passing a dictionary or a list of modes. How can I put sample_weight to train the net Learn more about deep learning Deep Learning Toolbox For example, keras has the functionality to specify the. Philippe's blog states, "If the model is stateless, the cell states are reset at each sequence. the version displayed in the diagram from the AlexNet paper; @article{ding2014theano, title={Theanobased LargeScale Visual Recognition with Multiple GPUs}, author={Ding, Weiguang and Wang, Ruoyan and Mao, Fei and Taylor, Graham}, journal={arXiv preprint arXiv:1412. applications import VGG16 vgg_conv = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3)) In the above code, we load the VGG Model along with the ImageNet weights similar to our previous tutorial. vgg16 import VGG16 model = VGG16(include_top= True, weights= 'imagenet', input_tensor= None, input_shape= None) VGG16クラスは4つの引数を取る。. Prediction with stateful model through Keras function model. keras implementation. See the examples in the Keras docs. If ‘balanced’, class weights will be given by n_samples / (n_classes * np. An image classification system built with transfer learning The basic technique to get transfer learning working is to get a pretrained model (with the weights loaded) and remove final fullyconnected layers from that model. Make sure you have already installed keras beforehand. These weights can be used to make predictions as is, or used as the basis for ongoing training. This means that if you want a weight decay with coefficient alpha for all the weights in your network, you need to add an instance of regularizers. sample_weights is defined on a persample basis and is independent from the class. Index values in weights not found in sampled object will be ignored and index values in sampled object not in weights will be assigned weights of zero. Weights & Biases helps you keep track of your machine learning experiments. Assume that you used softmax log loss and your output is [math]x\in R^d[/math]: [math]p(x_i)=e^{x_{i,j}}/\sum_{1 \le k \le d}e^{x_{i,k}}[/math] with [math]j[/math] being the dimension of the supposed correct class. For load_model_weights(), if by_name is FALSE (default) weights are loaded based on the network's topology, meaning the architecture should be the same as when the weights were saved. Keras provides a language for building neural networks as connections between general purpose layers. The problem lies with keras multiinput functional API. crossentropy for keras (2. W= Number of weights in the. I have four unbalanced classes with onehot encoded target labels. If you want a more detailed comparison between those two consider checking this answer I posted on a related question. freeze_weights(); unfreeze_weights() Freeze and unfreeze weights PREDICT predict() Generate predictions from a Keras model predict_proba() and predict_classes() Generates probability or class probability predictions for the input samples predict_on_batch() Returns predictions for a single batch of samples. You can vote up the examples you like or vote down the ones you don't like. sample_weight: sample weights, as a Numpy array. So, I will make CNN model and by CAM, check if it really works. Creating a sequential model in Keras. Both these functions can do the same task but when to use which function is the main question. Sample weights are not yet implemented in flow_from_dataframe. name of a loss function. I am using Keras to train a deep neural network. After training the model, we can extract individual layers. ckpt extension (saving in HDF5 with a. We do this in two stages: In the first run, with the embedding layer weights frozen, we allow the rest of the network to learn. In today's blog post we are going to learn how to utilize: Multiple loss functions; Multiple outputs …using the Keras deep learning library. After much research and guidance of working professionals, Keras was found to be an easy to learn and interpret for beginners in Deep Learning. First steps with Keras 2: A tutorial with Examples 1. If the model has multiple outputs, you can use a different sample_weight_mode on each output by passing a dictionary or a list of modes. One Shot Learning and Siamese Networks in Keras There is also a L2 weight decay term in the loss to encourage the network to learn smaller/less noisy weights and. Default 'None' results in equal probability weighting. Luckily the scipy. The sampler defines the sampling strategy used. Table of Contents. This is all that we need to do to differentiate between training and test data. Here is a Keras model of GoogLeNet (a. Use Keras Pretrained Models With Tensorflow. Classification of handwritten digits was the first big problem where deep learning outshone all the other known methods and this. I will assume. models import Sequential from keras. Overview Finetuning is one of the important methods to make bigscale model with a small amount of data. You can find the clear documentation of the Keras which is also simple. Variational autoencoder for "Frey faces" using keras Oct 22, 2016 In this post, I'll demo variational autoencoders [Kingma et al. Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Deep Language Modeling for Question Answering using Keras April 27, 2016. Keras and PyTorch deal with logloss in a different way. vgg16 import VGG16 model = VGG16(include_top= True, weights= 'imagenet', input_tensor= None, input_shape= None) VGG16クラスは4つの引数を取る。. 0 入门教程持续更新： Doit：最全Tensorflow 2. I created it by converting the GoogLeNet model from Caffe. For this reason, the documentation states that (inputs, targets, sample_weights) should be the same length. In this post, we'll build a simple Convolutional Neural Network (CNN) and train it to solve a real problem with Keras. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved. Today we'll train an image classifier to tell us whether an image contains a dog or a cat, using TensorFlow's eager API. Content Intro Neural Networks Keras Examples Keras concepts Resources 2 3. However, I could not locate a clear documentation on how this weighting works in practice. Devil is in the details so: tqdm it is just a library that implements a progress bar that will inform about the progression of the training during one epoch. save_weights method. Keras provides a language for building neural networks as connections between general purpose layers. This is the second part of AlexNet building. net = importKerasNetwork(modelfile,Name,Value) imports a pretrained TensorFlowKeras network and its weights with additional options specified by one or more namevalue pair arguments. We will use the Speech Commands dataset which consists of 65,000 onesecond audio files of people saying 30 different words. serving or just tf) apply optimizations (freezing, quantitization etc) Theoretically you could even train as Keras Model, convert to tf. The wrapped models aim to expose the same fit methods as the original Keras models in order to make their use as simple as possible. *FREE* shipping on qualifying offers. Keras Tutorial: The Ultimate Beginner's Guide to Deep Learning in Python Share Google Linkedin Tweet In this stepbystep Keras tutorial, you'll learn how to build a convolutional neural network in Python!. From Keras docs: class_weight : Optional dictionary mapping class indices (integers) to a weight (float) value, used for weighting the loss function (during training only). In this vignette we illustrate the basic usage of the R interface to Keras. Keras provides a basic save format using the HDF5 standard. Keras only asks that you provide the dimensions of the input tensor(s), and it figure out the rest of the tensor dimensions automatically. They are extracted from open source Python projects. Say I have two classes with sample size $1000$ (for class $0$) and $10000$ (for class $1$). sample_weight_mode. A clarification: do you want to debug a keras model (then you don’t need reticulate at all), or do you want to debug the keras framework?In the second case, since keras is a Python Open Source project, it’s much better if you learn Python and you make PRs on the GitHub repository, so that all keras users can benefit from your debugging. If not given, all classes are supposed to have weight one. The latter just implement a Long Short Term Memory (LSTM) model (an instance of a Recurrent Neural Network which avoids the vanishing gradient problem). Keras in a highlevel API that is used to make deep learning networks easier with the help of backend engine. 0, called "Deep Learning in Python". The fact that autoencoders are dataspecific makes them generally impractical for realworld data compression problems: you can only use them on data that is similar to what they were trained on, and making them more general thus requires lots of training data. The aim of this tutorial is to show the use of TensorFlow with KERAS for classification and prediction in Time Series Analysis. How can I put sample_weight to train the net Learn more about deep learning Deep Learning Toolbox For example, keras has the functionality to specify the. Even with small changes in the weights the result is still 0. pb model (expecting 3x as many as available) , We have a sample to illustrate plugin API with UFF model. You can vote up the examples you like or vote down the ones you don't like. class_weight: dict, 'balanced' or None. sample_weight_mode. We named the first layer "embedding". It's good to do the following before initializing Keras to limit Keras backend TensorFlow to use first GPU. The weights are large files and thus they are not bundled with Keras. Create a keras Sequence which is given to fit_generator. crossentropy for keras (2. layers import Dense, Activation model Sequential([ Dense (32, input dim=784) , Activation(' re I u'), Dense (ID ,. 1BestCsharp blog 7,766,141 views. We’ll attempt to learn how to apply five deep learning models to the challenging and wellstudied UCF101 dataset. Features of Keras?? User Friendly: Keras helps in reducing cognitive load. when you wrap the DataGenerator. You can create a Sequential model by passing a list of layer instances to the constructor: from keras. Features of Keras?? User Friendly: Keras helps in reducing cognitive load. 最全Tensorflow 2. Ideally we can find weights for Keras directly but often this is not the case. For this comprehensive guide, we shall be using VGG network but the techniques learned here can be used…. Keras is a high level library, used specially for building neural network models. As mentioned in the introduction to this tutorial, there is a difference between multilabel and multioutput prediction. GoogLeNet paper: Going deeper with convolutions. Estimator and use tf to export to inference graph. get_weights()  returns the layer weights as a list of Numpy arrays. I created it by converting the GoogLeNet model from Caffe. You can play with different models and check for the best model to fit in. How are sample weights handled in Keras? Showing 13 of 3 messages. Image Recognition (Classification). Devil is in the details so: tqdm it is just a library that implements a progress bar that will inform about the progression of the training during one epoch. In particular, the adjustment of sample weights to compensate for noncoverage and nonresponse is described. Hence, the loss becomes a weighted average, where the weight of each sample is specified by class_weight and its corresponding class. Restore Backbone Network (Keras applications) Keras pakage a number of deep leanring models alongside pretrained weights into an applications module. keras implementation. LSTM networks are a way of solving this problem. Keras provides a language for building neural networks as connections between general purpose layers. Lightweight and quick: Keras is designed to remove boilerplate code. py has an example of sample_weights are being applied. The following figure shows 225 sample images from the dataset. sample_weight_mode: If you need to do timestepwise sample weighting (2D weights), set this to "temporal". In Keras, you create 2D convolutional layers using the keras. An indepth introduction to using Keras for language modeling; word embedding, recurrent and convolutional neural networks, attentional RNNs, and similarity metrics for vector embeddings. You can play with different models and check for the best model to fit in. keras 빨리 훑어보기(intro) 1. utils to convert the target variable into multiple columns with values 0 or 1 depending on the value. If ‘balanced’, class weights will be given by n_samples / (n_classes * np. tflite file using python API; How to set class weight for imbalance dataset in Keras? How to get the output of Intermediate Layers in Keras? Passing Data Between Two Screen in Flutter. ” Feb 11, 2018. Name Description; addition_rnn: Implementation of sequence to sequence learning for performing addition of two numbers (as strings). L 2 regularization. I am using Keras to train a deep neural network. When training deep learning models, the checkpoint is the weights of the model. This means the weights of those earlier layers won't be changed significantly and therefore the network won't learn longterm dependencies. So, I'm setting the weights as (1/frequency of label) for each label. Ideally we can find weights for Keras directly but often this is not the case. Lightweight and quick: Keras is designed to remove boilerplate code. We will train the architecture on the popular CIFAR10 dataset which consists of 32x32 images belonging to 10 different classes. core import Dense, Dropout, Activation from keras. Using Keras and Deep QNetwork to Play FlappyBird. babi_memnn: Trains a memory network on the bAbI dataset for reading comprehension. sequence import pad_sequences from keras. Example of Deep Learning With R and Keras Recreate the solution that one dev created for the Carvana Image Masking Challenge, which involved using AI and image recognition to separate photographs. Keras in a highlevel API that is used to make deep learning networks easier with the help of backend engine. Want the code? It’s all available on GitHub: Five Video Classification Methods. However, it must be used with caution. model = InceptionV3(weights='imagenet', include_top=True) We can monitor the preconstructed structure and pretrained weights once model is loaded. In this blog post, I would like to discuss the stateful flag in Keras's recurrent model. There are many examples for Keras but without data manipulation and visualization. From Keras docs: class_weight : Optional dictionary mapping class indices (integers) to a weight (float) value, used for weighting the loss function (during training only). The Keras framework even has them builtin in the keras. sample_weights in Keras (self. You can vote up the examples you like or vote down the ones you don't like. 0 is the first release of multibackend Keras that supports TensorFlow 2. An indepth introduction to using Keras for language modeling; word embedding, recurrent and convolutional neural networks, attentional RNNs, and similarity metrics for vector embeddings. Is there a way in Keras to apply different weights to a cost function in different examples? feature into keras itself? Since we already have sample weighting in. None defaults to samplewise weights (1D). It's good to do the following before initializing Keras to limit Keras backend TensorFlow to use first GPU. (Default value = None) For keras. A while ago, I wrote two blogposts about image classification with Keras and about how to use your own models or pretrained models for predictions and using LIME to explain to predictions. If the model has multiple outputs, you can use a different sample_weight_mode on each output by passing a dictionary or a list of modes. Content Intro Neural Networks Keras Examples Keras concepts Resources 2 3. Also notice that we don't have to declare any weights or bias variables like we do in TensorFlow, Keras sorts that out for us. WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. Say I have two classes with sample size $1000$ (for class $0$) and $10000$ (for class $1$). we sample some experiences from the memory and call I added the saved weights for those who want to skip the. Do you want to build complex deep learning models in Keras? Do you want to use neural networks for classifying images, predicting prices, and classifying samples in several categories? Keras is the most powerful library for building neural networks models in Python. It should be noted that weights should once be saved before being loaded, else errors might occur. I created it by converting the GoogLeNet model from Caffe. This is the reason why you have to specify. l2(alpha) to each layer with weights (typically Conv2D and Dense layers) as you initialize them. In this tutorial we will build a deep learning model to classify words. Instead of trying to figure out the perfect combination of neural network layers to recognize flowers, we will first use a technique called transfer learning to adapt a powerful pretrained model to our dataset. Simple Audio Classification with Keras. A selfcontained introduction to general neural networks is outside the scope of this document; if you are unfamiliar with. Features of Keras?? User Friendly: Keras helps in reducing cognitive load. This article is intended to target newcomers who are interested in Reinforcement Learning. Here is a Keras model of GoogLeNet (a. Keras is a neural network API that is written in Python. If the initial weights map all our sample points to values smaller than 0, the ReLu maps everything to 0. Download Open Datasets on 1000s of Projects + Share Projects on One Platform. If the model has multiple outputs, you can use a different sample_weight_mode on each output by passing a list of modes. This Edureka Keras Tutorial you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. The wrapped models aim to expose the same fit methods as the original Keras models in order to make their use as simple as possible. Emerging possible winner: Keras is an API which runs on top of a backend. How are sample weights handled in Keras? Showing 13 of 3 messages. Customer Analytics: Using Deep Learning With Keras To Predict Customer Churn Written by Matt Dancho on November 28, 2017 Customer churn is a problem that all companies need to monitor, especially those that depend on subscriptionbased revenue streams. In this article, we will look at one of the basic examples from the Keras repository: mnist_mlp. How to save/load model and continue training using the HDF5 file in Keras? How to save and load model weights in Keras? How to convert. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. In this post we will learn a step by step approach to build a neural network using keras library for classification. class_weight: dictionary mapping classes to a weight value, used for scaling the loss function (during training only). Raw sample input data. If you want a more detailed comparison between those two consider checking this answer I posted on a related question. While choosing the framework for one of the projects based on NLP, we were given the option to get hands dirty with TF(Tensor Flow) or Keras. Overview and Prerequisites This example will the Keras R package to build an image classifier in TIBCO® Enterprise Runtime for R (TERR™). keras will be integrated directly into TensorFlow 1. From cognitive load one can understand that Keras makes the things easy and you don't need to worry how the things will work. Since doing the first deep learning with TensorFlow course a little over 2 years ago, much has changed. Things have been changed little, but the the repo is uptodate for Keras 2. callbacks. In this tutorial, you will implement something very simple, but with several learning benefits: you will implement the VGG network with Keras, from scratch, by reading the. In the previous post, titled Extract weights from Keras's LSTM and calcualte hidden and cell states, I discussed LSTM model. Keras Visualization Toolkit. Below is the Python module that initializes the neural network. 我觉得你很困惑 sample_weights 和 class_weights. Keras weighted categorical_crossentropy. I have four unbalanced classes with onehot encoded target labels. Integrating Keras (TensorFlow) YOLOv3 Into Apache NiFi Workflows Integrating live YOLO v3 feeds (TensorFlow) and ingesting their images and metadata. We do this in two stages: In the first run, with the embedding layer weights frozen, we allow the rest of the network to learn. You can create a Sequential model by passing a list of layer instances to the constructor: from keras. sample_weights, as the name suggests, allows further control of the relative weight of samples that belong to the same. preprocessing. You saw how to load the weights into a model. These models have a number of methods and attributes in common:. SimpleRNN(). Package ‘kerasR’ June 1, 2017 Type Package Title R Interface to the Keras Deep Learning Library Version 0. Keras in a highlevel API that is used to make deep learning networks easier with the help of backend engine. Recently, I came across this blogpost on using Keras to extract learned features from models and use those to cluster images. Using Keras and Deep QNetwork to Play FlappyBird. We will train the architecture on the popular CIFAR10 dataset which consists of 32x32 images belonging to 10 different classes. Very Simple Example Of Keras With Jupyter Sep 15, 2015. Also, please note that we used Keras' keras. kerasvis is a highlevel toolkit for visualizing and debugging your trained keras neural net models. Scalar training loss (if the model has no metrics) or list of scalars (if the model computes other metrics). To be added, in. Simple Audio Classification with Keras. How to save/load model and continue training using the HDF5 file in Keras? How to save and load model weights in Keras? How to convert. Package 'kerasR' June 1, 2017 Type Package Title R Interface to the Keras Deep Learning Library Version 0. h5 extension is covered in the Save and serialize models guide):. Keras was specifically developed for fast execution of ideas. 
