Keras Model

Evaluate model on test data. They are extracted from open source Python projects. After you create and train a Keras model, you can save the model to file in several ways. Keras is a neural network API that is written in Python. Conclusion and Further reading. from keras. To learn a bit more about Keras and why we're so excited to announce the Keras interface for R, read on! Keras and Deep Learning Interest in deep learning has been accelerating rapidly over the past few years, and several deep learning frameworks have emerged over the same time frame. To begin, here's the code that creates the model that we'll be using. Your First Keras Model. You have the Sequential model API which you are going to see in use in this tutorial and the functional API which can do everything of the Sequential model but it can be also used for advanced models with complex network. 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. Being able to go from idea to result with the least possible delay is key to doing good research. Assuming that you have your Keras model trained and ready to go, you should convert freeze the graph to a. Compiling a model can be done with the method compile, but some optional arguments to it can cause trouble when converting from R types so we provide a custom wrapper keras_compile. Load the pre-trained model. Keras is a high-level neural networks API developed with a focus on enabling fast experimentation. The ultimate goal of AutoML is to provide easily accessible deep learning tools to domain experts with limited data science or machine learning background. The first layer in the network, as per the architecture diagram shown previously, is a word embedding layer. Essentially it represents the array of Keras Layers. Internally, Keras is using the following process when training a model with. Essentially it represents the array of Keras Layers. Run on web browser¶. predict() generates output predictions based on the input you pass it (for example, the predicted characters in the MNIST example). # convert keras to tensorflow estimator estimator_model = keras. When you try to move from Keras to Pytorch take any network you have and try porting it to Pytorch. Layers are also first-class citizens in Lasagne, and a model is usually referred to simply by its output layer or layers. Getting started: 30 seconds to Keras. Model can be trained with the tf. Otherwise, output at the final time step will. Flexible Data Ingestion. Training Keras model with tf. Keras is a model-level library, providing high-level building blocks for developing deep learning models. So, like this amazing article by Yoni, I decided to dump my experience here. Hello everyone, this is part two of the two-part tutorial series on how to deploy Keras model to production. Gitlab CI log pushing your Keras model server to Heroku using a docker image. MobileNet V2 for example is a very good convolutional architecture that stays reasonable in size. Keras model. Use Keras Pretrained Models With Tensorflow. I came up with above model after some trials with vocab size, epochs, and Dropout layers. Model(x, z) Other cheap tricks Small 3x3 filters. Here’s a single-input model with 2 classes (binary classification):. In this Word2Vec Keras implementation, we'll be using the Keras functional API. We assume that the reader is familiar with the concepts of deep learning in Python, especially Long Short-Term Memory. Beginning Application Development with TensorFlow and Keras: Learn to design, develop, train, and deploy TensorFlow and Keras models as real-world applications [Luis Capelo] on Amazon. Creating a sequential model in Keras. Getting started: Import a Keras model in 60 seconds. Sequential model. The Sequential model is a linear stack of layers. models import Model from keras. Keras Applications is the applications module of the Keras deep learning library. Here is some snippet of fit and test accuracy. This project demonstrates how to use the Deep-Q Learning algorithm with Keras together to play FlappyBird. As the title suggest, this post approaches building a basic Keras neural network using the Sequential model API. Course Outline. The Keras Deep Learning Cookbook shows you how to tackle different problems encountered while training efficient deep learning models, with the help of the popular Keras library. Here is the Sequential model:. You can then train this model. Evaluate our model using the multi-inputs. Discover how to develop deep learning. compile: Boolean, whether to compile the model after loading. Essentially it represents the array of Keras Layers. I think both the libraries are fascinating with their pros one over the other. While there are many ways to convert a Keras model to its TenserFlow counterpart, I am going to show you one of the easiest when all you want is to make predictions with the converted model in deployment situations. py Loads pre-trained word embeddings (GloVe embeddings) into a frozen Keras Embedding layer, and uses it to train a text classification model on the 20 Newsgroup dataset. Keras! It's a high level deep learning library that makes it really easy to write deep neural network models of all sorts. Before building the CNN model using keras, lets briefly understand what are CNN & how they work. Keras (and Torch7) treat each 'operation' as a separate stage instead, so a typical fully connected layer has to be constucted as a cascade of a dot product and an elementwise nonlinearity. Configure a Keras model for training fit() Train a Keras model evaluate() Evaluate a Keras model predict() Predict Method for Keras Models summary() Print a summary of a model save_model_hdf5() load_model_hdf5() Save/Load models using HDF5 files get_layer() Retrieves a layer based on either its name (unique) or index. The R function you pass takes a model argument, which provides access to the underlying Keras model object should you need it. Keras is a code library for creating deep neural networks. It has been obtained through the following method: vgg-face-keras:directly convert the vgg-face matconvnet model to keras model; vgg-face-keras-fc:first convert vgg-face caffe model to mxnet model,and then convert it to keras model. For example: model = Model(inputs=visible, outputs=hidden) The Keras functional API provides a more flexible way for defining models. Getting started: Import a Keras model in 60 seconds. py, an object recognition task using shallow 3-layered convolution neural network (CNN) on CIFAR-10 image dataset. Run Keras models in the browser, with GPU support using WebGL. layers is a flattened list of the layers comprising the model graph. Use the global keras. By default, Keras shuffles (permutes) the samples in and the dependencies between and are lost. In part one of the tutorial series, we looked at how to use Convolutional Neural Network (CNN) to classify MNIST Handwritten digits using Keras. In Keras this can be done via the tf. Ok, let us create an example network in keras first which we will try to port into Pytorch. custom_objects: Optional dictionary mapping names (strings) to custom classes or functions to be considered during deserialization. Package ‘keras’ April 5, 2019 Type Package Title R Interface to 'Keras' Version 2. Starting with installing and setting up Keras, the book demonstrates how you can perform deep learning with Keras in the TensorFlow. Evaluate our model using the multi-inputs. Keras is undoubtedly my favorite deep learning + Python framework, especially for image classification. Here are the instructions for you to follow. json file), the second is the path to its weights stored in h5 file. As the title suggest, this post approaches building a basic Keras neural network using the Sequential model API. Otherwise, output at the final time step will. The pre-trained classical models are already available in Keras as Applications. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as. Keras is an easy-to-use and powerful library for Theano and TensorFlow that provides a high-level neural networks API to develop and evaluate deep learning models. Welcome - [Narrator] Let's use the ResNet 50 deep neural network model included with Keras to recognize objects and images. Since Keras is just an API on top of TensorFlow I wanted to play with the underlying layer and therefore implemented image-style-transfer with TF. Flexible Data Ingestion. Because Keras abstracts away a number of frameworks as backends, the models can be trained in any backend, including TensorFlow, CNTK, etc. 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). You can use model. applications import VGG16 #Load the VGG model vgg_conv = VGG16(weights='imagenet', include_top=False, input_shape=(image_size, image_size, 3)) Freeze the required layers. We have seen the in-depth detailed implementation of neural networks in Keras and Theano in the previous articles. The specific task herein is a common one (training a classifier on the MNIST dataset), but this can be considered an example of a template for approaching any such similar task. About fine-tune and VGG16, please check the following articles. First up, we have to import the callback functions: from keras. The ultimate goal of AutoML is to provide easily accessible deep learning tools to domain experts with limited data science or machine learning background. Creating a CNN with batchnorm (which will help keep gradients in range) is quite easy in Keras. Classification output will be multiclass. Since our model is now an Estimator, we'll train and evaluate it a bit differently than we did in Keras. Import libraries and modules. Predict on Trained Keras Model. The first layer in the network, as per the architecture diagram shown previously, is a word embedding layer. Args: layer: The keras layer to use. A Keras model is made up of a sequence or a standalone graph. In this part, we're going to cover how to actually use your model. Layers are also first-class citizens in Lasagne, and a model is usually referred to simply by its output layer or layers. Join Coursera for free and transform your career with degrees, certificates, Specializations, & MOOCs in data science, computer science, business, and dozens of other topics. Check if the number of parameters of your network is the same as Keras'. Take a look at Figure 1 to see where this column is headed. In this illustration, you see the result of two consecutive 3x3 filters. If an optimizer was found as part of the saved model, the model is already. Deep learning with Keras - Part 8: Create confusion matrix for Keras model predictions blkholedetector ( 30 ) in deep-learning • 2 years ago This eighth video in the Deep learning with Keras series demonstrates how to create a confusion matrix to visually observe how well a Keras model was able to predict on new data. With Keras, you can build simple or very complex neural networks within a few minutes. Run Keras models in the browser, with GPU support using WebGL. 2 ): VGG16,. applications import VGG16 #Load the VGG model vgg_conv = VGG16(weights='imagenet', include_top=False, input_shape=(image_size, image_size, 3)). When you try to move from Keras to Pytorch take any network you have and try porting it to Pytorch. In this tutorial, we will present a simple method to take a Keras model and deploy it as a REST API. They are extracted from open source Python projects. h5') To load weights, you need to first build the model and then load weights. This method works well when one needs to keep the starting state of the model the same, though this comes up with an overhead of maintaining the saved weights file. Compiling a model can be done with the method compile, but some optional arguments to it can cause trouble when converting from R types so we provide a custom wrapper keras_compile. Here are the steps for building your first CNN using Keras: Set up your environment. Sep 24, 2017 or Generative Adversarial Network is a type of generative model – a model that looks at the training data drawn from a. The simple fact is that most organizations have data that can be used to target these individuals and to understand the key drivers of churn, and we now have Keras for Deep Learning available in R (Yes, in R!!), which predicted customer churn with 82% accuracy. Because Keras abstracts away a number of frameworks as backends, the models can be trained in any backend, including TensorFlow, CNTK, etc. Learn how to define a Keras model. As the title suggest, this post approaches building a basic Keras neural network using the Sequential model API. Dense(5, activation='softmax')(y) model = tf. In order to do so you need to import the model_from_json package and use json instead of yaml in latter part of the. The examples covered in this post will serve as a template/starting point for building your own deep learning APIs — you will be able to extend the code and customize it based on how scalable and robust your API endpoint needs to be. As the Caffe-Keras conversion tool is still under development, I would like to share with the community the VGG-16 pretrained model, from the paper:. 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. You can create a Sequential model by passing a list of layer instances to the constructor:. To import a Keras model, you need to create and serialize such a model first. 0, called "Deep Learning in Python". Train an end-to-end Keras model on the mixed data inputs. We assume that the reader is familiar with the concepts of deep learning in Python, especially Long Short-Term Memory. This post is a personal notes (specificaly for keras 2. One Keras function allows you to save just the model weights and bias values. inputs is the list of input tensors. Working with Keras in Windows Environment View on GitHub Download. Join Jonathan Fernandes for an in-depth discussion in this video Building the Keras model, part of Neural Networks and Convolutional Neural Networks Essential Training. Predict on Trained Keras Model. 5; To install this package with conda run one of the following: conda install -c conda-forge keras. For example: model = Model(inputs=visible, outputs=hidden) The Keras functional API provides a more flexible way for defining models. In order to do so you need to import the model_from_json package and use json instead of yaml in latter part of the. Load the model weights. Since Keras is just an API on top of TensorFlow I wanted to play with the underlying layer and therefore implemented image-style-transfer with TF. Well, you can actually do it quite easily, by using the History objects of Keras along with Matplotlib. While there is still feature and performance work remaining to be done, we appreciate early feedback that would help us bake Keras support. Callback, which already has those on_{train, epoch, batch}_{begin, end} functions. * Dense (fully connected) layer with input of 20 dimension vectors, which means you have 20 columns in your data. Menu Running a Keras / TensorFlow Model in Golang 02 April 2018 on MachineLearning, Golang, Deep Learning, TensorFlow, Keras. October 11, 2016 300 lines of python code to demonstrate DDPG with Keras. We'll train an image classifier in Keras using a Tensorflow backend, then serve it to the browser using a super simple Flask backend. It can use several popular backends like Tensorflow and CNTK. They are extracted from open source Python projects. Input layer. Keras to single TensorFlow. Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible. Through Keras, users have access to a variety of different state-of-the-art deep learning frameworks, such as TensorFlow, CNTK, and others. fit_generator() when using a generator) it actually return a History object. Models can be run in Node. Final approach is to save the architecture of the model. Keras有两种类型的模型,序贯模型(Sequential)和函数式模型(Model),函数式模型应用更为广泛,序贯模型是函数式模型的一种特殊情况。. It would look something. In our previous tutorial, we learned how to use models which were trained for Image Classification on the ILSVRC data. Example: importKerasNetwork(modelfile,'OutputLayerType','classification','Classes',classes) imports a network from the model file modelfile, adds an output layer for a classification problem at the end of the Keras layers, and specifies classes as the classes of the output layer. Once the model is fully defined, we have to compile it before fitting its parameters or using it for prediction. Otherwise, output at the final time step will. Training Keras model with tf. Anyone Can Learn To Code an LSTM-RNN in Python (Part 1: RNN) Baby steps to your neural network's first memories. It would look something. The simple fact is that most organizations have data that can be used to target these individuals and to understand the key drivers of churn, and we now have Keras for Deep Learning available in R (Yes, in R!!), which predicted customer churn with 82% accuracy. Use Keras Pretrained Models With Tensorflow. Keras is an open-source neural network API library, written in Python (but also available for R) and designed to run on top of TensorFlow, CNTK, or Theano. - Also supports double stochastic attention. 0 API on March 14, 2017. Configure a Keras model for training fit() Train a Keras model evaluate() Evaluate a Keras model predict() Predict Method for Keras Models summary() Print a summary of a model save_model_hdf5() load_model_hdf5() Save/Load models using HDF5 files get_layer() Retrieves a layer based on either its name (unique) or index. Preprocess input data for Keras. Keras is undoubtedly my favorite deep learning + Python framework, especially for image classification. Keras was specifically developed for fast execution of ideas. 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. Mar 13, 2017 · You can use model. Here are 2 Keras callbacks that will save you time. These models are trained on ImageNet data set for classifying images into one of 1000 categories or classes. import keras from keras_self_attention import SeqSelfAttention model = keras. Useful attributes of Model. Run on web browser¶. We also show how to actually bypass Keras, and build the models directly in Theano/Tensorflow syntax (although this is quite complex!). Beginning Application Development with TensorFlow and Keras: Learn to design, develop, train, and deploy TensorFlow and Keras models as real-world applications [Luis Capelo] on Amazon. The core data structure of Keras is a model, a way to organize layers. Posted by iamtrask on November 15, 2015. SimpleRNN is the recurrent neural network layer described above. model_to_estimator(keras_model=model) Bit confusing point for me was the setting of input data. Keras, TensorFlow, and Theano. Output layer. Keras will evaluate the model on the validation set at the end of each epoch and report the loss and any metrics we asked for. Build a Keras model for inference with the same structure but variable batch input size. Configure a Keras model for training fit() Train a Keras model evaluate() Evaluate a Keras model predict() Predict Method for Keras Models summary() Print a summary of a model save_model_hdf5() load_model_hdf5() Save/Load models using HDF5 files get_layer() Retrieves a layer based on either its name (unique) or index. layers import Dense from keras. It's nowhere near as complicated to get started, nor do you need to know as much to be successful with. Classification output will be multiclass. About fine-tune and VGG16, please check the following articles. When you are using model. It would look something. Keras saves models in the hierarchical data format (HDF) version 5, which you can think of as somewhat similar to a binary XML. 5; osx-64 v2. Well, you can actually do it quite easily, by using the History objects of Keras along with Matplotlib. Use Keras Pretrained Models With Tensorflow. Let's first import the libraries that we are going to need in order to create our model: from keras. model_from_json) and so are the weights (model. Useful attributes of Model. We refer such model as a pre-trained model. # Create the model by specifying the input and output tensors. Keras is an open-source neural-network library written in Python. Since Keras is just an API on top of TensorFlow I wanted to play with the underlying layer and therefore implemented image-style-transfer with TF. Prune your pre-trained Keras model. Pickling Keras Models. The simple fact is that most organizations have data that can be used to target these individuals and to understand the key drivers of churn, and we now have Keras for Deep Learning available in R (Yes, in R!!), which predicted customer churn with 82% accuracy. Creating a keras model. Train the TPU model with static batch_size * 8 and save the weights to file. 1 Description Interface to 'Keras' , a high-level neural networks 'API'. Each of the layers in the model needs to know the input shape it should expect, but it is enough to specify input_shape for the first layer of the Sequential model. pop_layer() Remove the. So first we need some new data as our test data that we're going to use for predictions. Keras is a popular programming framework for deep learning that simplifies the process of building deep learning applications. It is a great entry. The model returned by load_model() is a compiled model ready to be used (unless the saved model was never compiled in the first place). Keras! It's a high level deep learning library that makes it really easy to write deep neural network models of all sorts. 0 API on March 14, 2017. Example: importKerasNetwork(modelfile,'OutputLayerType','classification','Classes',classes) imports a network from the model file modelfile, adds an output layer for a classification problem at the end of the Keras layers, and specifies classes as the classes of the output layer. Preprocess input data for Keras. Jun 19, 2016 · I trained a neural network in Keras to perform non linear regression on some data. October 11, 2016 300 lines of python code to demonstrate DDPG with Keras. In this tutorial, we will present a simple method to take a Keras model and deploy it as a REST API. h5') To load weights, you need to first build the model and then load weights. First, let's write the initialization function of the class. The following are code examples for showing how to use keras. Since Keras is just an API on top of TensorFlow I wanted to play with the underlying layer and therefore implemented image-style-transfer with TF. 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. To create a custom Keras model, you call the keras_model_custom() function, passing it an R function which in turn returns another R function that implements the custom call() (forward pass) operation. Are you interested in learning how to use Keras? Do you already have an understanding of how neural networks work? Check out this lean, fat-free 7 step plan for going from Keras newbie to master of its basics as quickly as is possible. The core data structure of Keras is a model, a way to organize layers. Exercise 3. Model can be trained with the tf. Keras is a minimalist, highly modular neural networks library written in Python and capable on running on top of either TensorFlow or Theano. png', show_shapes=True, show_layer_names=True) Output: You can see that we have 6 different output layers. Training a YOLO model takes a very long time and requires a fairly large dataset of labelled bounding boxes for a large range of target classes. Once the model is fully defined, we have to compile it before fitting its parameters or using it for prediction. Deep learning models can take hours, days or even weeks to train. Adjust accordingly when copying code from the comments. Keras has a model visualization function, that can plot out the structure of a model. The simple fact is that most organizations have data that can be used to target these individuals and to understand the key drivers of churn, and we now have Keras for Deep Learning available in R (Yes, in R!!), which predicted customer churn with 82% accuracy. This is the 18th article in my series of articles on Python for NLP. Check if the number of parameters of your network is the same as Keras'. Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Alternatively, you can import layer architecture as a Layer array or a LayerGraph object. layers is a flattened list of the layers comprising the model graph. This will lead us to cover the following Keras features: fit_generator for training Keras a model using Python data generators; ImageDataGenerator for real-time data augmentation; layer freezing and model fine-tuningand more. In this tutorial, we walked through how to convert, optimized your Keras image classification model with TensorRT and run inference on the Jetson Nano dev kit. In this code lab, you will see how to call keras_to_tpu_model in Keras to use them. 0, called "Deep Learning in Python". Compiling a model can be done with the method compile, but some optional arguments to it can cause trouble when converting from R types so we provide a custom wrapper keras_compile. 1 Description Interface to 'Keras' , a high-level neural networks 'API'. save('my_model. Open up createmodel. The Sequential model is a linear stack of layers. I use Keras in production applications, in my personal deep learning projects, and here on the PyImageSearch blog. One Keras function allows you to save just the model weights and bias values. In this post, we’ll show you how to build a simple model to predict the tag of a Stack Overflow question. When you are using model. For more complex architectures, you should use the Keras functional API, which allows to build arbitrary graphs of layers. The importer for the TensorFlow-Keras models would enable you to import a pretrained Keras model and weights. h5') This single HDF5 file will contain: the architecture of the model (allowing the recreation of the model). One of the major points for using Keras is that it is one user-friendly API. The simplest type of model is the Sequential model, a linear stack of layers. outputs is the list of output tensors. For more complex architectures, you should use the Keras functional API, which allows to build arbitrary graphs of layers. I came up with above model after some trials with vocab size, epochs, and Dropout layers. models import Model from keras. Last week I published a blog post about how easy it is to train image classification models with Keras. With the stateful model, all the states are propagated to the next batch. First up, we have to import the callback functions: from keras. - Also supports double stochastic attention. The ability to convert a Keras model into a TensorFlow Estimator was introduced in TensorFlow 1. Keras was specifically developed for fast execution of ideas. load_weights('my_model_weights. It's pretty annoying that Keras doesn't support Pickle to serialize its objects (Models). The first layer in the network, as per the architecture diagram shown previously, is a word embedding layer. 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. Creating a keras model. Welcome everyone to an updated deep learning with Python and Tensorflow tutorial mini-series. model = Model(input=[a1, a2], output=[b1, b3, b3]) For a detailed introduction of what Model can do, read this guide to the Keras functional API. In this illustration, you see the result of two consecutive 3x3 filters. Since Keras is just an API on top of TensorFlow I wanted to play with the underlying layer and therefore implemented image-style-transfer with TF. # Keras provides a "Model" class that you can use to create a model # from your created layers. 5; osx-64 v2. Keras is an easy-to-use and powerful library for Theano and TensorFlow that provides a high-level neural networks API to develop and evaluate deep learning models. We’ll solve this text classification problem using Keras, a high-level API built in to TensorFlow. SimpleRNN is the recurrent neural network layer described above. Try to load the model in Keras first to check that your model was saved correctly from keras. png', show_shapes=True, show_layer_names=True) Output: You can see that we have 6 different output layers. py Restores a character-level sequence to sequence model from disk (saved by lstm_seq2seq. This post shows how easy it is to port a model into Keras. Here is an example of Creating a keras model:. In this Word2Vec Keras implementation, we'll be using the Keras functional API. We'll feed the produced arrays (word_target, word_context) into our Keras model later - now onto the Word2Vec Keras model itself. They are extracted from open source Python projects. Here I would like to give a piece of advice too. custom_objects: Optional dictionary mapping names (strings) to custom classes or functions to be considered during deserialization. The first layer in the network, as per the architecture diagram shown previously, is a word embedding layer. Existing Guides. Configure a Keras model for training fit() Train a Keras model evaluate() Evaluate a Keras model predict() Predict Method for Keras Models summary() Print a summary of a model save_model_hdf5() load_model_hdf5() Save/Load models using HDF5 files get_layer() Retrieves a layer based on either its name (unique) or index. preprocessing. Evaluate model on test data. In this tutorial, we will present a simple method to take a Keras model and deploy it as a REST API. Predict with the inferencing model. 5; win-32 v2. We will also demonstrate how to train Keras models in the cloud using CloudML. conda install linux-64 v2. Cloud TPUs are available in a base configuration with 8 cores and also in larger configurations called "TPU pods" of up to 512 cores. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, Theano, or PlaidML. The Machine Learning world has been divided over the preference of one language over the other. estimator API by converting the model to an tf. Define model architecture. Exercise 3. For more complex architectures, you should use the Keras functional API, which allows to build arbitrary graphs of layers. If an optimizer was found as part of the saved model, the model is already. Create a convert. 1 Description Interface to 'Keras' , a high-level neural networks 'API'. Evaluate our model using the multi-inputs. Keras and PyTorch differ in terms of the level of abstraction they operate on. Retriggering the initializer. Evaluate model on test data. Since our model is now an Estimator, we'll train and evaluate it a bit differently than we did in Keras. inputs is the list of input tensors. EarlyStopping(). 5; To install this package with conda run one of the following: conda install -c conda-forge keras. Preprocess class labels for Keras. Let’s now start using Keras to develop various types of models for Natural Language Processing. It's pretty annoying that Keras doesn't support Pickle to serialize its objects (Models). Keras is a high level library, used specially for building neural network models. The R function you pass takes a model argument, which provides access to the underlying Keras model object should you need it. Well, you can actually do it quite easily, by using the History objects of Keras along with Matplotlib. predict() generates output predictions based on the input you pass it (for example, the predicted characters in the MNIST example). You can create a Sequential model by passing a list of layer instances to the constructor:. In Keras this can be done via the tf. In this tutorial, you'll build a deep learning model that will predict the probability of an employee leaving a company. Each of the layers in the model needs to know the input shape it should expect, but it is enough to specify input_shape for the first layer of the Sequential model. Using Keras and Deep Deterministic Policy Gradient to play TORCS. input_names: [str] | str. We will us our cats vs dogs neural network that we've been perfecting. # Keras layers track their connections automatically so that's all that's needed. The fact-checkers, whose work is more and more important for those who prefer facts over lies, police the line between fact and falsehood on a day-to-day basis, and do a great job. Today, my small contribution is to pass along a very good overview that reflects on one of Trump’s favorite overarching falsehoods. Namely: Trump describes an America in which everything was going down the tubes under  Obama, which is why we needed Trump to make America great again. And he claims that this project has come to fruition, with America setting records for prosperity under his leadership and guidance. “Obama bad; Trump good” is pretty much his analysis in all areas and measurement of U.S. activity, especially economically. Even if this were true, it would reflect poorly on Trump’s character, but it has the added problem of being false, a big lie made up of many small ones. Personally, I don’t assume that all economic measurements directly reflect the leadership of whoever occupies the Oval Office, nor am I smart enough to figure out what causes what in the economy. But the idea that presidents get the credit or the blame for the economy during their tenure is a political fact of life. Trump, in his adorable, immodest mendacity, not only claims credit for everything good that happens in the economy, but tells people, literally and specifically, that they have to vote for him even if they hate him, because without his guidance, their 401(k) accounts “will go down the tubes.” That would be offensive even if it were true, but it is utterly false. The stock market has been on a 10-year run of steady gains that began in 2009, the year Barack Obama was inaugurated. But why would anyone care about that? It’s only an unarguable, stubborn fact. Still, speaking of facts, there are so many measurements and indicators of how the economy is doing, that those not committed to an honest investigation can find evidence for whatever they want to believe. Trump and his most committed followers want to believe that everything was terrible under Barack Obama and great under Trump. That’s baloney. Anyone who believes that believes something false. And a series of charts and graphs published Monday in the Washington Post and explained by Economics Correspondent Heather Long provides the data that tells the tale. The details are complicated. Click through to the link above and you’ll learn much. But the overview is pretty simply this: The U.S. economy had a major meltdown in the last year of the George W. Bush presidency. Again, I’m not smart enough to know how much of this was Bush’s “fault.” But he had been in office for six years when the trouble started. So, if it’s ever reasonable to hold a president accountable for the performance of the economy, the timeline is bad for Bush. GDP growth went negative. Job growth fell sharply and then went negative. Median household income shrank. The Dow Jones Industrial Average dropped by more than 5,000 points! U.S. manufacturing output plunged, as did average home values, as did average hourly wages, as did measures of consumer confidence and most other indicators of economic health. (Backup for that is contained in the Post piece I linked to above.) Barack Obama inherited that mess of falling numbers, which continued during his first year in office, 2009, as he put in place policies designed to turn it around. By 2010, Obama’s second year, pretty much all of the negative numbers had turned positive. By the time Obama was up for reelection in 2012, all of them were headed in the right direction, which is certainly among the reasons voters gave him a second term by a solid (not landslide) margin. Basically, all of those good numbers continued throughout the second Obama term. The U.S. GDP, probably the single best measure of how the economy is doing, grew by 2.9 percent in 2015, which was Obama’s seventh year in office and was the best GDP growth number since before the crash of the late Bush years. GDP growth slowed to 1.6 percent in 2016, which may have been among the indicators that supported Trump’s campaign-year argument that everything was going to hell and only he could fix it. During the first year of Trump, GDP growth grew to 2.4 percent, which is decent but not great and anyway, a reasonable person would acknowledge that — to the degree that economic performance is to the credit or blame of the president — the performance in the first year of a new president is a mixture of the old and new policies. In Trump’s second year, 2018, the GDP grew 2.9 percent, equaling Obama’s best year, and so far in 2019, the growth rate has fallen to 2.1 percent, a mediocre number and a decline for which Trump presumably accepts no responsibility and blames either Nancy Pelosi, Ilhan Omar or, if he can swing it, Barack Obama. I suppose it’s natural for a president to want to take credit for everything good that happens on his (or someday her) watch, but not the blame for anything bad. Trump is more blatant about this than most. If we judge by his bad but remarkably steady approval ratings (today, according to the average maintained by 538.com, it’s 41.9 approval/ 53.7 disapproval) the pretty-good economy is not winning him new supporters, nor is his constant exaggeration of his accomplishments costing him many old ones). I already offered it above, but the full Washington Post workup of these numbers, and commentary/explanation by economics correspondent Heather Long, are here. On a related matter, if you care about what used to be called fiscal conservatism, which is the belief that federal debt and deficit matter, here’s a New York Times analysis, based on Congressional Budget Office data, suggesting that the annual budget deficit (that’s the amount the government borrows every year reflecting that amount by which federal spending exceeds revenues) which fell steadily during the Obama years, from a peak of $1.4 trillion at the beginning of the Obama administration, to $585 billion in 2016 (Obama’s last year in office), will be back up to $960 billion this fiscal year, and back over $1 trillion in 2020. (Here’s the New York Times piece detailing those numbers.) Trump is currently floating various tax cuts for the rich and the poor that will presumably worsen those projections, if passed. As the Times piece reported: