Keras Tutorial

The simplest model in Keras is the sequential, which is built by stacking layers sequentially. 0 library is still only in alpha release. This post will. Tutorial Previous situation. The Sequential model is a linear stack of layers. Train and register a Keras classification model with Azure Machine Learning. We do not currently distribute AWS credits to CS231N students but you are welcome to use this snapshot on your own budget. Keras seems to be an easy-to-use high-level library, which wraps over 3 different backend engine: TensorFlow, CNTK and Theano. Practical Guide of RNN in Tensorflow and Keras Introduction. When I was researching for any working examples, I felt frustrated as there isn’t any practical guide on how Keras and Tensorflow works in a typical RNN model. In PyTorch we have more freedom, but the preferred way is to return logits. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. Step 2: Install Keras. Keras is a simple-to-use but powerful deep learning library for Python. One Shot Learning and Siamese Networks in Keras I’m not too worried about this because this tutorial was more about introducing one-shot learning in general. Create a Keras neural network for anomaly detection. Keras also comes with various kind of network models so it makes us easier to use the available model for pre-trained and fine-tuning our own network model. Note: This post assumes that you have at least some experience in using Keras. Today’s Keras tutorial is designed with the practitioner in mind — it is meant to be a practitioner’s approach to applied deep learning. "Keras tutorial. CAUTION! This code doesn't work with the version of Keras higher then 0. We will use tfdatasets to handle data IO and pre-processing, and Keras to build and train the model. Tutorial registration includes coffee breaks. Prepare train/validation data. It allows for an easy and fast prototyping, supports convolutional, recurrent neural networks and a combination of the two. Before we begin, we should note that this guide is geared toward beginners who are interested in applied deep learning. The source code. Basic Regression — This tutorial builds a model to predict the median price of homes in a Boston suburb during the mid-1970s. BERT implemented in Keras. The simplest model in Keras is the sequential, which is built by stacking layers sequentially. In a nutshell, you'll address the following topics in today's tutorial:. The framework used in this tutorial is the one provided by Python's high-level package Keras, which can be used on top of a GPU installation of either TensorFlow or Theano. It was the last release to only support TensorFlow 1 (as well as Theano and CNTK). One Shot Learning and Siamese Networks in Keras I’m not too worried about this because this tutorial was more about introducing one-shot learning in general. Keras does all the work of subtracting the target from the neural network output and squaring it. Keras is a simple-to-use but powerful deep learning library for Python. This tutorial goes through how to set up your own EC2 instance with the provided AMI. It wraps the efficient numerical computation libraries Theano and TensorFlow and allows you to define and train neural network models in a few short lines of code. Kerasライブラリは、レイヤー(層)、 目的関数 (英語版) 、活性化関数、最適化器、画像やテキストデータをより容易に扱う多くのツールといった一般に用いられているニューラルネットワークのビルディングブロックの膨大な数の実装を含む。. Welcome everyone to an updated deep learning with Python and Tensorflow tutorial mini-series. It's been developed by Google to meet their needs. Valerio Maggio gave a great tutorial presentation at PyData London 2017, titled "Ten Steps to Keras. This tutorial illustrates how to simply and quickly spin up a Ubuntu-based Azure Data Science Virtual Machine (DSVM) and to configure a Keras and CNTK environment. Our CBIR system will be based on a convolutional denoising autoencoder. Simple Audio Classification with Keras. The spaCy user survey has been full of great feedback about the library. 0 release will be the last major release of multi-backend Keras. Keras for Sequence to Sequence Learning. In this article, we will do a text classification using Keras which is a Deep Learning Python Library. In this post, we’ll see how easy it is to build a feedforward neural network and train it to solve a real problem with Keras. GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. It's fine if you don't understand all the details, this is a fast-paced overview of a complete Keras program with the details explained as we go. The form collects information we will use to send you updates about. Now that we know what Convolutional Neural Networks are, what they can do, its time to start building our own. If you have a high-quality tutorial or project to add, please open a PR. I've learned basics of convolutional neural networks (and how to set a machine on) during workshop at Polish Children's Fund tutored by Piotr Migdał. It wouldn't be a Keras tutorial if we didn't cover how to install Keras. You will also explore image processing with recognition of hand written digit images, classification of. This course introduces you to Keras and shows you how to create applications with maximum readability. A notebook with slightly improved code is available here. This guide uses tf. Please create a /home/docs/checkouts/readthedocs. For that reason you need to install older version 0. It's been developed by Google to meet their needs. Practical Guide of RNN in Tensorflow and Keras Introduction. Text Classification — This tutorial classifies movie reviews as positive or negative using the text of the review. Train and register a Keras classification model with Azure Machine Learning. It’s fine if you don’t understand all the details, this is a fast-paced overview of a complete Keras program with the details explained as we go. A less circular explanation is that activation functions combine the neuron inputs to produce an output. First off, Keras is built on top of Theano and you can use theano in tandem with keras as well. http://ankivil. layers import Dense. Initialising the CNN. Keras with MXNet. An Azure DSVM is a. In this article, I will take you through the Keras Tutorial and Introduction to its Implementation. For this tutorial you also need pandas. It’s fine if you don’t understand all the details, this is a fast-paced overview of a complete Keras program with the details explained as we go. A complete guide to using Keras as part of a TensorFlow workflow. If you have already worked on keras deep learning library in Python, then you will find the syntax and structure of the keras library in R to be very similar to that in Python. keras: R Interface to 'Keras' Interface to 'Keras' , a high-level neural networks 'API'. Welcome to the first assignment of week 2. save_model to store it as an hdf5 file, but all these won't help when we want to store another object that references. This tutorial assumes that you are slightly familiar convolutional neural networks. The simplest model in Keras is the sequential, which is built by stacking layers sequentially. Introductory neural network concerns are covered. In PyTorch we have more freedom, but the preferred way is to return logits. You could call low level theano functions even while working with Keras. The tutorial explains. It's nowhere near as complicated to get started, nor do you need to know as much to be successful with. You'd probably need to register a Kaggle account to do that. It had been on my "To Do" list for about a year now, and while I had done some reading and tutorials, I hadn. This article is an introductory tutorial to deploy keras models with Relay. NMT-Keras Documentation, Release 0. Keras seems to be built 'on top of' Theano in the sense that it hides all the Theano code behind an API (which looks almost exactly like the Torch7 API). The tutorial explains. See all of our Oriole Online Tutorials. The first step in creating a Neural network is to initialise the network using the Sequential Class from keras. Or copy & paste this link into an email or IM:. Prepare train/validation data. It's common to just copy-and-paste code without knowing what's really happening. This project demonstrates how to use the Deep-Q Learning algorithm with Keras together to play FlappyBird. First off, Keras is built on top of Theano and you can use theano in tandem with keras as well. Has over 250,000 users. pdf), Text File (. pyscript or via command-line-interface. I read about how to save a model, so I could load it later to use again. In this assignment, you will: Learn to use Keras, a high-level neural networks API (programming framework), written in Python and capable of running on top of several lower-level frameworks including TensorFlow and CNTK. Cifar10 dataset can be found in keras. Please use a supported browser. A Tutorial on Autoencoders for Deep Learning December 31, 2015 Despite its somewhat initially-sounding cryptic name, autoencoders are a fairly basic machine learning model (and the name is not cryptic at all when you know what it does). The second part of this tutorial will show you how to load custom data into Keras and build a Convolutional Neural Network to classify them. Converting free-form text into a nice clean integer-coded vocabulary is what this post is all about. Keras is a neural network API that is written in Python. For example: > library. We will use tfdatasets to handle data IO and pre-processing, and Keras to build and train the model. You will learn about building a regression model using the Keras library. The functional API in Keras. Text Classification — This tutorial classifies movie reviews as positive or negative using the text of the review. Kerasライブラリは、レイヤー(層)、 目的関数 (英語版) 、活性化関数、最適化器、画像やテキストデータをより容易に扱う多くのツールといった一般に用いられているニューラルネットワークのビルディングブロックの膨大な数の実装を含む。. Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. This article is a keras tutorial that demonstrates how to create a CBIR system on MNIST dataset. Using Keras and Deep Q-Network to Play FlappyBird. Using the Keras library to train a simple Neural Network that recognizes handwritten digits For us Python Software Engineers, there's no need to reinvent the wheel. An HDF5 file is a container for two kinds of objects: datasets, which are array-like collections of data, and groups, which are folder-like containers that hold datasets and other groups. The model runs on top of TensorFlow, and was developed by Google. R interface to Keras. 0] I decided to look into Keras callbacks. For Keras 2 with an MXNet backend on Python 3 with CUDA 9 with cuDNN 7:. It provides support for multiple backends such as TensorFlow, Theano or CNTK and allows for training on CPU or GPU. Some simple background in one deep learning software platform may be helpful. The winners of ILSVRC have been very generous in releasing their models to the open-source community. layers import Convolution2D from keras. Using Keras is like working with Logo blocks. To learn how to use PyTorch, begin with our Getting Started Tutorials. Introduction In this tutorial we will build a deep learning model to classify words. Learn how to use Keras, from beginner basics to advanced techniques, with online video tutorials taught by industry experts. Keras is a high-level neural networks API, written in Python and capable of running on top of either TensorFlow or Theano. To help you gain hands-on experience, I’ve included a full example showing you how to implement a Keras data generator from scratch. This guide uses tf. In this tutorial, it will run on top of TensorFlow. Most of the Image datasets that. Learn how it works and how to use it. Keras Tutorial for Beginners with tutorial and examples on HTML, CSS, JavaScript, XHTML, Java,. " Each tutorial is a thought-by-thought tour of the instructor's approach to a specific problem, presented in both narrative and executable code. Kerasはplot_model()を使うと簡単にネットワークモデルの簡約図が作成できる from keras. You can create a Sequential model by passing a list of layer instances to the constructor: from keras. [Update: The post was written for Keras 1. Working with Keras in Windows Environment View on GitHub Download. skvideo is python package which is very useful to read vid. In this tutorial, we will build a neural network with Keras to determine whether or not tic-tac-toe games have been won by player X for given endgame board configurations. Basic Regression--- This tutorial builds a model to predict the median price of homes in a Boston suburb during the mid-1970s. For a beginner-friendly introduction to machine learning with tf. Keras Keras Tutorial. TensorFlow is an open source software library for machine learning. Python is a general-purpose high level programming language that is widely used in data science and for producing deep learning algorithms. Use with Keras model¶ In this tutorial, we'll convert ResNet50 classification model pretrained in Keras into WebDNN execution format. pip install -U keras. Keras was developed to enable deep learning engineers to build and experiment with different models very quickly. Welcome to PyTorch Tutorials¶. Prerequisites. Time series analysis has a variety of applications. This tutorial shows how to train a neural network on AI Platform using the Keras sequential API and how to serve predictions from that model. The code is from keras. I think I raised important questions that no one even deems to think about yet. We do not currently distribute AWS credits to CS231N students but you are welcome to use this snapshot on your own budget. All organizations big or small, trying to leverage the technology and invent some cool solutions. It's nowhere near as complicated to get started, nor do you need to know as much to be successful with. utils import plot_model plot_model ( model , to_file = '. In this tutorial, it will run on top of TensorFlow. If you’d like to check out more Keras awesomeness after reading this post, have a look at my Keras LSTM tutorial or my Keras Reinforcement Learning tutorial. A less circular explanation is that activation functions combine the neuron inputs to produce an output. This is a directory of tutorials and open-source code repositories for working with Keras, the Python deep learning library. Download files. Continuing the series of articles on neural network libraries, I have decided to throw light on Keras - supposedly the best deep learning library so far. Now that we know what Convolutional Neural Networks are, what they can do, its time to start building our own. Good software design or coding should require little explanations beyond simple comments. txt) or read online for free. In the previous tutorial on Deep Learning, we've built a super simple network with numpy. In this guide, we will train a neural network model to classify images of clothing, like sneakers and shirts. Prototyping of network architecture is fast and intuituive. This Edureka Keras Tutorial TensorFlow video (Blog: https://goo. The tutorial explains. 04 LTS Python: 3. As the name suggests, that tutorial provides examples of how to implement various kinds of autoencoders in Keras, including the variational autoencoder (VAE). We will go through this example because it won't consume your GPU, and your cloud budget to. The current release is Keras 2. One such application is the prediction of the future value of an item based on its past values. For more information, please visit Keras Applications documentation. The RStudio team has developed an R interface for Keras making it possible to run different deep learning backends, including CNTK, from within an R session. The tutorials will be completely example driven to make sure the readers learn the concepts and how to apply them on real datasets. I've loaded MNIST dataset in Keras and checked it's dimension. dl Keras implementation Deep Learning Tutorial for. keras, a high-level API to. Keras is a Deep Learning library written in Python with a Tensorflow/Theano backend. Some simple background in one deep learning software platform may be helpful. Environment. Time series analysis refers to the analysis of change in the trend of the data over a period of time. For example: > library. Get started with reinforcement learning in less than 200 lines of code with Keras (Theano or Tensorflow, it's your choice). This is an excerpt from the Oriole Online Tutorial, "Getting Started with Deep Learning using Keras and Python. In this tutorial, we will present a simple method to take a Keras model and deploy it as a REST API. However, one of the biggest limitations of WebWorkers is the lack of (and thus WebGL) access, so it can only be run in CPU mode for now. For this tutorial we will use cifar10 dataset from Keras. TensorFlow is an end-to-end open source platform for machine learning. pyscript or via command-line-interface. The goal for this tutorial is to give computer an array of numbers (image above) and to get probabilities of each class we gave at the beginning. A complete guide to using Keras as part of a TensorFlow workflow. You will learn about supervised deep learning models, such as convolutional neural networks and recurrent neural networks, and how to build a convolutional neural network using the Keras library. This site may not work in your browser. then, Flatten is used to flatten the dimensions of the image obtained after convolving it. Or copy & paste this link into an email or IM:. Before we begin, we should note that this guide is geared toward beginners who are interested in applied deep learning. If you have already worked on keras deep learning library in Python, then you will find the syntax and structure of the keras library in R to be very similar to that in Python. Learn how to use Keras, from beginner basics to advanced techniques, with online video tutorials taught by industry experts. For TensorFlow versions 1. Download files. This a Keras tutorial, so I don't want to spend too long on the NN specific details. We will build a simple architecture with just one layer of inception module using keras. November 18, 2016 November 18, 2016 Posted in Research. Also, there are a lot of tutorials and articles about using Keras from communities worldwide codes for deep learning purposes. In this tutorial, we implement Recurrent Neural Networks with LSTM as example with keras and Tensorflow backend. Just as TensorFlow is a higher-level framework than Python, Keras is an even higher-level framework and provides additional abstractions. Deep Learning is everywhere. Overfitting and Underfitting — In this tutorial, we explore two common regularization techniques (weight regularization and dropout) and use them to improve our movie review classification results. Core concepts¶. For this tutorial you also need pandas. This is Part 2 of a MNIST digit classification notebook. I think I raised important questions that no one even deems to think about yet. TensorFlow and Keras. C++ Debugging Final-Storm Indie-Game-Dev itch. R interface to Keras. About the following terms used above: Conv2D is the layer to convolve the image into multiple images Activation is the activation function. Introductory neural network concerns are covered. This article is a keras tutorial that demonstrates how to create a CBIR system on MNIST dataset. Now you are finally ready to experiment with Keras. 0中有什幺区别?但是,随着深度学习的普及,许多开发人员,程序员和机器学习从业人员都因其易于使用的API而蜂拥而至Keras。. The main focus of Keras library is to aid fast prototyping and experimentation. In this tutorial, I’ll concentrate on creating LSTM networks in Keras, briefly giving a recap or overview of how LSTMs work. I have been working on deep learning for sometime. Train and register a Keras classification model with Azure Machine Learning. Enroll in this python keras tutorial that will help you learn deep learning & machine learning with keras and python from scratch. Regression; Sequence to sequence @(Cabinet)[ml_dl_theano|ml_dl_recurrent|published_gitbook] Keras for Sequence to Sequence Learning. Keras is a high-level neural networks API developed with a focus on enabling fast experimentation. Getting Started with Keras : 30 Second. Auto-Keras is an open source software library for automated machine learning (AutoML). TensorFlow is an open source software library for machine learning. Now you are finally ready to experiment with Keras. Now that we know what Convolutional Neural Networks are, what they can do, its time to start building our own. This post is part of the series on Deep Learning for Beginners, which consists of the following tutorials : Neural Networks : A 30,000 Feet View for Beginners; Installation of Deep Learning frameworks (Tensorflow and Keras with CUDA support ) Introduction to Keras; Understanding Feedforward Neural Networks. Today there are a variety of tools available at your disposal to develop and train your own Reinforcement learning agent. This tutorial goes through how to set up your own EC2 instance with the provided AMI. It was developed with a focus on enabling fast experimentation. edu) Research Center, RMI Group Leader Applied Physics Laboratory The Johns Hopkins University Johns Hopkins Road Laurel, MD 20707 (301) 953-6231 (c) Date received: 9 May 1990 3. Make sure you have already installed keras beforehand. This is an excerpt from the Oriole Online Tutorial, "Getting Started with Deep Learning using Keras and Python. In this article, we will do a text classification using Keras which is a Deep Learning Python Library. Regression; Sequence to sequence @(Cabinet)[ml_dl_theano|ml_dl_recurrent|published_gitbook] Keras for Sequence to Sequence Learning. Title: Pima Indians Diabetes Database 2. It’s fine if you don’t understand all the details, this is a fast-paced overview of a complete Keras program with the details explained as we go. This is an excerpt from the Oriole Online Tutorial, "Getting Started with Deep Learning using Keras and Python. In this article I'll demonstrate how to perform binary classification using a deep neural network with the Keras code library. Being able to go from idea to result with the least possible delay is key to doing good research. Things have been changed little, but the the repo is up-to-date for Keras 2. Keras Keras Tutorial. You can create a Sequential model by passing a list of layer instances to the constructor: from keras. This blog post titled Keras as a simplified interface to TensorFlow: tutorial is a nice introduction to Keras. Good software design or coding should require little explanations beyond simple comments. A Generative Adversarial Networks tutorial applied to Image Deblurring with the Keras library. You'll discover how to shorten the learning curve, future-proof your career, and land a high-paying job in data science. Posted by: Chengwei 9 months ago () In this quick tutorial, you will learn how to setup OpenVINO and make your Keras model inference at least x3 times faster without any added hardware. It can use Theano or Tensorflow as backend, so there are even chances to accelerate your computations using GPUs. The Keras census sample is the introductory example for using Keras on AI Platform to train a model and get predictions. keras, a high-level API to. For Keras 2 with an MXNet backend on Python 3 with CUDA 9 with cuDNN 7:. Keras is a high-level python API which can be used to quickly build and train neural networks using either Tensorflow or Theano as back-end. This article shows you how to train and register a Keras classification model built on TensorFlow using Azure Machine Learning. This post is intended for complete beginners to Keras but does assume a basic background knowledge of neural networks. The main focus of Keras library is to aid fast prototyping and experimentation. Basic Regression--- This tutorial builds a model to predict the median price of homes in a Boston suburb during the mid-1970s. Enter your email address and click the button below to get your FREE Deep Learning sample chapter. Keras is a high-level neural networks API, written in Python and capable of running on top of either TensorFlow or Theano. This has been demonstrated in numerous blog posts and tutorials, in particular, the excellent tutorial on Building Autoencoders in Keras. Below are the topics covered in this tutorial: 1. There are many different binary classification algorithms. These models can be used for feature extraction, fine-tuning and prediction. Using Keras is like working with Logo blocks. Keras is a simple, high-level neural networks library, written in Python that works as a wrapper to Tensorflow [1] or Theano [2]. The good news is that in Keras you can use a tf. This blog post titled Keras as a simplified interface to TensorFlow: tutorial is a nice introduction to Keras. Introduction In this tutorial we will build a deep learning model to classify words. EliteDataScience. fit_generator functions work, including the differences between them. Official high-level API of TensorFlow. In PyTorch we have more freedom, but the preferred way is to return logits. Another interesting aspect to Keras is the concept of backends. ; Tensorboard integration. All organizations big or small, trying to leverage the technology and invent some cool solutions. Please create a /home/docs/checkouts/readthedocs. This Edureka Keras Tutorial TensorFlow video (Blog: https://goo. In fact, the keras package in R creates a conda environment and installs everything required to run keras in that environment. Keras-users Welcome to the Keras users forum. To dive more into the API, see the following set of guides that cover what you need to know as a TensorFlow Keras power user: Guide to the Keras functional API. Keras and PyTorch differ in terms of the level of abstraction they operate on. Once the model is trained we will use it to generate the musical notation for our music. https://matthewearl. We will us our cats vs dogs neural network that we've been perfecting. In this tutorial, you'll build a deep learning model that will predict the probability of an employee leaving a company. I'm playing with the reuters-example dataset and it runs fine (my model is trained). Keras vs tf. keras之间的区别,包括TensorFlow 2. What is Keras?. com) submitted 1 year ago by ledilb 3 comments. Keras 2 “You have just found Keras” Felipe Almeida Rio Machine Learning Meetup / June 2017 First Steps 1 2. keras-vis is a high-level toolkit for visualizing and debugging your trained keras neural net models. This course introduces you to Keras and shows you how to create applications with maximum readability. This article is an introductory tutorial to deploy keras models with Relay. Keras is a powerful and easy-to-use free open source Python library for developing and evaluating deep learning models. 0版本对我意味着什么?. What is Keras?. There is a famous MNIST dataset, containing grayscale images of the handwritten digits from 0 to 9. Keras is an open source neural network Python library which can run on top of other machine learning libraries like TensorFlow, CNTK or Theano. Like (4) Comment (0) Note the default back-end for Keras is Tensorflow. Create a Keras neural network for anomaly detection. Below are the topics covered in this tutorial: 1. I read about how to save a model, so I could load it later to use again. [Update: The post was written for Keras 1. https://matthewearl. com helps busy people streamline the path to becoming a data scientist. Kerasはplot_model()を使うと簡単にネットワークモデルの簡約図が作成できる from keras. js can be run in a WebWorker separate from the main thread. To register for a tutorial, check it in the relevant section when completing your registration. It's common to just copy-and-paste code without knowing what's really happening. Simple Audio Classification with Keras. 0 release will be the last major release of multi-backend Keras. Keras seems to be an easy-to-use high-level library, which wraps over 3 different backend engine: TensorFlow, CNTK and Theano. Keras Documentation, Release latest This is an autogenerated index file. In this post, we'll build a simple Convolutional Neural Network (CNN) and train it to solve a real problem with Keras. All tutorials have been executed from the root nmt-keras folder. Throughout the tutorial, bear in mind that there is a Glossary as well as index and modules links in the upper-right corner of each page to help you out. Both of those tutorials use the IMDB dataset, which has already been parsed into integers representing words. Valerio Maggio gave a great tutorial presentation at PyData London 2017, titled "Ten Steps to Keras. "Keras tutorial. Deep Learning with Keras – Part 7: Recurrent Neural Networks. Same instructors. Sun 24 April 2016 By Francois Chollet. The Keras Tutorial - Introduction 14 Dec 2016 in Blog / Neural_networks / Keras / Tutorial on Neural , Networks , Keras , Tutorial Keras is a high-level neural networks library written in Python and built on top of Theano or Tensorflow. See all of our Oriole Online Tutorials. Deep Learning with Keras – Part 7: Recurrent Neural Networks. It is more of a front-end library, unlike Tensorflow which is a back-end library. " Each tutorial is a thought-by-thought tour of the instructor's approach to a specific problem, presented in both narrative and executable code. If TensorFlow is your primary framework, and you are looking for a simple & high-level model definition interface to make your life easier, this tutorial is for you. This is a nice toy application. js performs a lot of synchronous computations, this can prevent the DOM from being blocked. We have 3 layers with drop-out and batch normalization between each layer. An Azure DSVM is a. conda install linux-64 v2. In this tutorial, you will see how you can use a simple Keras model to train and evaluate an. pip install -U keras. GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. 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: