Tensorflow Js Model Fit

fit_generator() when using a generator) it actually return a History object. Let's say you work with Tensorflow and don't know much about Theano, then you will have to implement the paper in Tensorflow, which obviously will take longer. With TensorFlow. 7 virtualenv or an Anaconda environment and install TensorFlow for CPU (we will not need GPUs at all). js: machine learning for the web and beyond Smilkov et al. But, the fact is TensorFlow. Train a model to predict y-values for a cubic equation using a single layer perceptron. Not sure I understood what you mean by "exporting a TF model from Keras"… Assuming you have a Keras model (for example, in your dev env) and you want to load (and run) it in prod, you can either use: 1. The model is trained on google colab with free GPU and then run on browser using tensorflow. For most practical machine learning tasks, TensorFlow is overkill. The most important element of the application we are creating is the neural network model. , previously we learned about the overview of Convolutional Neural Network and how to preprocess the data for training, In this lesson, we will train our Neural network in Google C olab. They can be used directly or used in a transfer learning setting with TensorFlow. Building effective machine learning models means asking a lot of questions. to_categorical (y, nb_classes). TensorFlow data tensors). js在浏览器中表现相当不错,如果你想见证浏览器内部机器学习的潜力. ) When we set up our. js で遊んでるのは、ブラウザゲームのゲーム AI を作りたくて、js と python で同じ環境を二回実装するのが嫌、という理由なので、そういう理由がなければ python で keras 使っとくのがいいと思う。. js Core, enabling users to build, train, and execute deep learning models directly in browsers. With TensorFlow. js for Node. What about saving the actual model (object instance) to a file, and then reloading it at a later time?. js Layers API to create Keras-like models locally. Supports arbitrary network architectures: multi-input or multi-output models, layer sharing, model sharing, etc. An increasing number of developers are using TensorFlow in their machine learning projects. js @ZackAkil Build & run machine learning models on webpages js. Tensorflow js with model trained from Keras in Python. 如果你还不够了解 TensorFlow. In TensorFlow 2. js model from python code Method1 Method 2 open command and enter below command,…. Declarative views make your code more predictable and easier to debug. At first glance the documentation looks decent but the more I read the more I found myself scratching my head on how to do even the most basic task. We are exploring the possibilities with streaming data and TensorFlow. layers import Input, Dense a = Input(shape=(32,)) b = Dense(32)(a) model = Model(inputs=a, outputs=b) This model will include all layers required in the computation of b given a. With TensorFlow 1. js from this package, the module that you get will be accelerated by the TensorFlow C binary and run on the CPU. Installation from NPM and using a build tool like Parcel, WebPack, or Rollup. This is done by buildCnn function in prediction. fit() or tf. js web-friendly format. js: machine learning for the web and beyond Smilkov et al. js,一个构建在 tensorflow. If not specified a default. TensorFlow. js frontend developers can run machine learning programs in a browser. You can specify the length of training for a network by specifying the number of epochs to train over. This is my first attempt at learning to use TensorFlow, and there are probably much better ways to do many things, so let me know in the comment section!. fit (// X tensors. That's when I found Tensorflow JS. We will implement the model in both Tensorflow and Keras to see how they interoperate with Tensorboard. Usage example:. In this HTML file, we imported data. Creating Model. 0 2018 Model SM-T387 Verizon/Sprint Tablet (Not fit Tab 8. using the Core API with Optimizer. js图层格式 Keras模型通常通过via保存model. In the following section I will show you how to build, train, and make predictions with TensorFlow. TensorFlow. First, create a Python 2. First, we will look at the Layers API, which is a higher-level API for building and training models. After some time training, the model should be smart enough to pick out photos of rock, paper, and scissors symbols that it’s never seen before. The model is saved in h5 format and converted into json for use in js. Keras is a high-level interface for neural networks that runs on top of multiple backends. Saturday May 6, 2017. js 做了细致介绍: 在大会的 Keynote 中,TensorFlow 团队表示基于网页的 JavaScript 库 TensorFlow. 1; win-64 v1. 99 (Official Build) (64-bit) Describe the problem or feature request When fitting a fairly simple dense model (100-. keras, the Keras API integrates seamlessly with your TensorFlow workflows. js的安装和核心概念就结束了,后面会有更多关于tensorflow的文章。. fit() as of now). The subject is too broad to be covered in details in a single blog post, so we may revisit it in a future post. ̸Ҳ̸ҳ[̲̅B̲̅][̲̅7̲̅][̲̅B̲̅ - de-de. Everytime you change the model in the demo, you will use another 5 MB of data. import * as tf from '@tensorflow/tfjs'; model = tf. models import Model from keras. Run Existing models Use TensorFlow. TFLearn: Deep learning library featuring a higher-level API for TensorFlow. linspace(-2, 1, 200) #Return a random matrix with data from the standard normal distribution. 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. In TensorFlow’s GitHub repository you can find a large variety of pre-trained models for various machine learning tasks, and one excellent resource is their object detection API. This will turbo charge collaborations for the whole community. M a t h J a x MathJax /jax/output/HTML-CSS/config. layers import Input, Dense a = Input(shape=(32,)) b = Dense(32)(a) model = Model(inputs=a, outputs=b) This model will include all layers required in the computation of b given a. The models are hosted on NPM and unpkg so they can be used in any project out of the box. Importing a Keras model into TensorFlow. js by building a digit recognizer from scratch in this quick start tutorial https://angularfirebase. jsを使ったリアルタイム姿勢推測について。PoseNet Modelの説明と実装方法が書かれている。. js frontend developers can run machine learning programs in a browser. I started doing this because the concept of Machine Learning intrigued me very much and wanted to see if there was any way this could be done in front end development. js You should run the following command if you run the node. First, highlighting TFLearn high-level API for fast neural network building and training, and then showing how TFLearn layers, built-in ops and helpers can directly benefit any model implementation with Tensorflow. 3 Overview of KERAS Minimalist, highly modular neural networks library Written in Python Capable of running on top of either TensorFlow or Theano Developed with a focus on enabling fast. In this tutorial, we will train a TensorFlow model using the MNIST dataset on an Azure Deep Learning virtual machine. Finally we can predict/test on new results using predict() module What is TensorFlow. js and using it in the browser; Few words on using action classification with LSTM; For this article, we’ll relax the problem to posture detection based on a single frame, in contrast to recognizing an action from a sequence of frames. This creates packages. x: Vector, matrix, or array of training data (or list if the model has multiple inputs). py or you can create and train any model you want. js can be used to add machine learning capabilities to your. TensorFlow deep learning tutorial. js 构建的库,可直接在浏览器环境中创建深度学习模型。使用它可以在浏览器上创建 CNNs,RNNs 等,并使用客户端的 GPU 处理能力训练这些模型。. fit() or LayersModel. Model to train. JSの公式サイトにある「TensorFlow. Before the model can be used in a web application, it needs to be converted into a web-friendly format converted by the TensorFlow. js Execute native TensorFlow with the same TensorFlow. TensorFlow. js(现已加入Tensorflow. Training a model with TensorFlow. 2 to customers. After that we fit the model with some input. js uses WebGL based javascript library for traditional machine learning application and high-level keras. js one can train and deploy ML models in the browser. Data Generation. Once again, you can notice that TensorFlow. js的入门,关于机器学习更多的知识点可参考Google机器学习课程。 让我们开始吧! 安装 直接引入. fit(…) or model. js入门教程,w3cschool。 model. js is added to your web application How TensorFlow. The singleton object will be replaced if the visor is removed from the DOM for some reason. To begin, we need to make pong in. In this talk, you will learn about the TensorFlow. Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. TensorFlow offers many kinds of layers in its tf. tensor() Tensorflow JS has a library of APIs specifically for retrieving and formatting raw data into WebGL optimised tensor objects, that are used to train. The primary interface to the visor is the visor() function. We are exploring the possibilities with streaming data and TensorFlow. Vue — A client-side framework (somewhat similar to React), which has an easy an easy start. Convert a Tensorflow Object Detection SavedModel to a Web Model For TensorflowJS - Convert Tensorflow SavedModel to WebModel for TF-JS names we can run our. js for building a more advanced model. Installation process here, worked without problems (only change I did was using driver 430 instead of 418 (earliest driver that officially supports the 2070S. js是一个开源的基于硬件加速的JavaScript库,用于训练和部署机器学习模型。谷歌推出的第一个基于TensorFlow的前端深度学习框架是deeplearning. developerWorks blogs allow community members to share thoughts and expertise on topics that matter to them, and engage in conversations with each other. js 内部工作优先级很高。. At the moment, it supports types of layers used mostly in convolutional networks. For other types of networks, like RNNs, you may need to look at tf. js to make. js model to recognize handwritten digits with a convolutional neural network. Our complete implementation is located in this file. js library and use it locally. Text classification implementation with TensorFlow can be simple. Prerequisites. 0, eager execution is on by default. Usage of the Model in a Web Application. In this HTML file, we imported data. Training a Tensorflow JS model with tf. There are many processing steps that must be performed, and how this processing is performed is a function of not only the code you write, but also the data you use. とりあえず何してるかは後で見るとして、動かしてみましょう。(コメントをGoogle翻訳してみました) const tf = require('@tensorflow/tfjs'); // Define a model for linear regression. io API for building a custom neural network which is used in building deep learning application. The size of the box itself is calculated like this:. html for detail. This callback is usually passed as a callback to tf. What about saving the actual model (object instance) to a file, and then reloading it at a later time?. This means that Keras is appropriate for building essentially any deep learning model, from a memory network to a neural Turing machine. This latest news makes installing TensorFlow 1. • One must also examine the distribution of residuals--a good model fit should yield residuals. To tackle this classic machine learning task, we are going to build a deep neural network classifier. jsでDeepLearningのチュートリアル「Making Predictions from 2D Data」の詳細解説の前半です。 これはTensorFlow. See the mnist. Again, this is also an async function that uses await till the model make successfull predictions. Run a TensorFlow demo model. With recent advances in image recognition and using more training data, we can perform much better on this data set challenge. js aims to make machine learning approachable for a broad audience of artists, creative coders, and students. In this HTML file, we imported data. M a t h J a x MathJax /jax/output/HTML-CSS/config. 方法と手順はこんな感じです。 まず、準備。 Tensorflowのカレントバージョンがインストールされていることが前提です。 Tensorflowをインストールした環境をActiveにして以下をインストールします。 pip install. It has always been a debatable topic to choose between R and Python. Models can be trained, evaluated, and used for prediction. using the Core API with Optimizer. js model in Node. Conclusion. The focus is on TensorFlow Serving, rather than the modeling and training in TensorFlow, so for a complete example which focuses on the modeling and training see the Basic Classification example. 3 Overview of KERAS Minimalist, highly modular neural networks library Written in Python Capable of running on top of either TensorFlow or Theano Developed with a focus on enabling fast. TensorFlow. js project! Have a look at the new documentation and code. You can ask your peers for advice about what technology to choose, Google the answer, or ask developers which technology they prefer. Now, If the code is written in Keras all you have to do is change the back-end to Tensorflow. In this super-simple tutorial, I'll show you a basic "Hello World. To tackle this classic machine learning task, we are going to build a deep neural network classifier. The library provides access to machine learning algorithms and models in the browser, building on top of TensorFlow. First, we will look at the Layers API, which is a higher-level API for building and training models. Please try again later. Note: If you're not going to use GPU you can just install tensorflow-model-server as: sudo apt-get install tensorflow-model-server. TensorFlow. Build it Yourself — Chatbot API with Keras/TensorFlow Model Is not that complex to build your own chatbot (or assistant, this word is a new trendy term for chatbot) as you may think. Let us begin with the objectives of this lesson. If all inputs in the model are named, you can also pass a list mapping input names to data. Everytime you change the model in the demo, you will use another 5 MB of data. To get my head around the API, I created a "hello world" linear regression model. In TensorFlow 2. , SysML'19 If machine learning and ML models are to pervade all of our applications and systems, then they'd better go to where the applications are rather than the other way round. The Machine Learning world has been divided over the preference of one language over the other. 2 to customers. TensorFlow is a powerful library that’s mostly used for deep learning, although its computational model based on directed graphs certainly allows for a wider range of use cases. TensorFlow Developer Summit 2018にて Webブラウザ上で機械学習のモデルの構築、学習、学習済みモデルの実行などが可能になるJavaScriptライブラリ「TensorFlow. I will describe step by step in this post, how to build TensorFlow model for text classification and how classification is done. Das folgende Beispiel soll die grundlegende Funktionsweise unter Verwendung von Python darstellen: Zunächst wird die TensorFlow-Bibliothek geladen. Convert class vector (integers from 0 to nb_classes) to binary class matrix, for use with categorical_crossentropy. ML is much helped by the use of TensorFlow. You can ask your peers for advice about what technology to choose, Google the answer, or ask developers which technology they prefer. jsで読み込める形式にコンバートしたものを使って、固定のMNIST画像(PNGファイルにしたもの)を読んで、評価して結果を表示する。. Description. Unless you want to do some type of federated training in real time without sending private information to the servers (very unusual case in my opinion). Supports arbitrary network architectures: multi-input or multi-output models, layer sharing, model sharing, etc. Estimators: A high-level way to create TensorFlow models. (2018) proposing a new method for mediation analysis. By Dana Mastropole, Robert Schroll, and Michael Li TensorFlow has gathered quite a bit of attention as the new hot toolkit for building neural networks. I created a tutorial on. Is the model. I want to make a model to convert Celsius to Fahrenheit with Tensorflow. Train a TensorFlow model in the cloud. js在浏览器中表现相当不错,如果你想见证浏览器内部机器学习的潜力. js and using it in the browser; Few words on using action classification with LSTM; For this article, we’ll relax the problem to posture detection based on a single frame, in contrast to recognizing an action from a sequence of frames. First, highlighting TFLearn high-level API for fast neural network building and training, and then showing how TFLearn layers, built-in ops and helpers can directly benefit any model implementation with Tensorflow. This is done by buildCnn function in prediction. json file in the current directory. js and using it in the browser; Few words on using action classification with LSTM; For this article, we'll relax the problem to posture detection based on a single frame, in contrast to recognizing an action from a sequence of frames. To get my head around the API, I created a "hello world" linear regression model. TensorFlow is a powerful library that’s mostly used for deep learning, although its computational model based on directed graphs certainly allows for a wider range of use cases. js file, which should be located in the same folder as index. Background This is a brief post about making my first Shiny App (see also). Image Processing Using Cloundinary (Part 1) In this post, we will build an image object detection system with Tensorflow-js with the pre-trained model. In this talk, you will learn about the TensorFlow. fit function really working? This is the link. This creates packages. js가 공개되었습니다. ; KerasJS — Is a port of Keras for the browser, allowing you to load your model and weight, run predict(). Now, If the code is written in Keras all you have to do is change the back-end to Tensorflow. Hence we can replace a linear regression model with such a neural network model and run MCMC or VI as usual. js (HDF5, Saved Model) and then train and run them in web browsers, or convert them to run on mobile devices using TensorFlow Lite (HDF5, Saved Model). How does this work? We're using Tiny YOLO, a ML model that's been converted to work in Tensorflow. html for detail. js first time. This will turbo charge collaborations for the whole community. Get to know TensorFlow. f_scores import F1Score. The CPU version is much easier to install and configure so is the best starting place especially when you are first learning how to use Keras. Given N pairs of inputs x and desired outputs d, the idea is to model the relationship between the outputs and the inputs using a linear model y = w_0 + w_1 * x where the output of the model y is approximately equal to the desired output d for every pair (x, d). js converter. fit function really working? This is the link. pb and a labels. Editor’s note: This post is part of our Trainspotting series, a deep dive into the visual and audio detection components of our Caltrain project. In this post, I will show you how to turn a Keras image classification model to TensorFlow estimator and train it using the Dataset API to create input pipelines. Tensorflow js with model trained from Keras in Python. feed_dict: dict. The third step involves converting input data into tensors which TensorFlow. Discuss Welcome to TensorFlow discuss. About HTML Preprocessors. Well organized and easy to understand Web building tutorials with lots of examples of how to use HTML, CSS, JavaScript, SQL, PHP, Python, Bootstrap, Java and XML. Develop ML in Node. They're command line tools used to install and keep track of your third party Javascript dependencies. Is capable of running on top of multiple back-ends including TensorFlow, CNTK, or Theano. This creates packages. js (with Node. Model to train. The first. If you wonder how to save a model with TensorFlow, please have a look at my previous article before going on. If you've used Cloud Machine Learning (ML) Engine, you know that it can train and deploy any TensorFlow, scikit-learn, and XGBoost models at large scale in the cloud. To the beginner, it may seem that the only thing that rivals this interest is the number of different APIs which you can use. It is meant to be used by Trainer before starting to fit data. Default: default tf graph. With @addyosmani and @katiehempenius we've discussed using TensorFlow. 今日は皆さん。 待ちに待った瞬間がついに来ました。 あの、tensorflow. If not specified a default. Also explore the app called Headlines. js向けTensorFlow. js to do graph analysis in the terminal or on a web server. TensorFlow. js가 공개되었습니다. We need to get that data to the IBM Cloud platform. js (HDF5, Saved Model) and then train and run them in web browsers, or convert them to run on mobile devices using TensorFlow Lite (HDF5, Saved Model). Model JS660ELES – Slate Model JS660SLSS – Stainless steel JS660ELES JS660SLSS GUARANTEED FIT Replace your old 30" free-standing range with a new 30" slide-in model. Try the sketch-rnn demo. 2 to customers. macOS: Download the. x can be NULL (default) if feeding from framework-native tensors (e. For example, deep learning uses neural networks, which are like a simulation of the human brain. We aim to help students understand the graphical computational model of TensorFlow, explore the functions it has to offer, and learn how to build and structure models best suited for a deep learning project. From TensorFlow 1. In particular, as tf. TensorFlow offers many kinds of layers in its tf. Also, it supports different types of operating systems. 0 has been redesigned with a focus on developer productivity, simplicity, and ease of use. js Crash Course for absolute beginners. js Linear Regression. In the first part TensorFlow. The model is saved in h5 format and converted into json for use in js. TensorFlow. js is a library for developing and training ML models in JavaScript, and deploying in browser or on Node. This feature is not available right now. Be sure to visit js. models import Model from keras. Supports arbitrary network architectures: multi-input or multi-output models, layer sharing, model sharing, etc. fit是我们调用的启动循环的函数。它是一个异步函数,因此我们返回它给我们的特定值,以便调用者可以确定运行结束时间。 为了监控运行进度,我们将一些回调传递给model. js)使我们能够在浏览器中构建机器学习和深度学习模型,而无需任何复杂的安装步骤。 TensorFlow. js is an open source WebGL-accelerated JavaScript library for machine intelligence. predict(newExample). Congratulations! You have just trained a simple machine learning model using Tensorflow. Now that the training data is ready, it is time to create a model for time series prediction, to achieve this we will use TensorFlow. This is important so the prediction model better fit our model and to be faster when there is a lot of data. With the help of TensorFlow. js: Polynomial Regression. The model has two parameters: an intercept term, w_0 and a single coefficient, w_1. In this course, learn how to install Keras and use it to build a simple deep learning model. 由于 TensorFlow. js framework. js is super easy with a single line of code: const model = tf. js で遊んでるのは、ブラウザゲームのゲーム AI を作りたくて、js と python で同じ環境を二回実装するのが嫌、という理由なので、そういう理由がなければ python で keras 使っとくのがいいと思う。. Training a Tensorflow JS model with tf. js (Part 3) Image Processing — Making Custom Filters — React. js for Node. 各位大佬,请问tensorflow中采用model. js是TensorFlow的JavaScript API,主要用于网页端的机器学习应用开发。TensorFlow. js library for makig it easy to show the canvas and get our data points for training the model. h5 model/ 这个步骤将创建一些 权重 文件和包含模型架构的 json 文件。 通过 zip 将模型进行压缩,以便将其下载到本地机器上:. // 線形回帰のモデルを定義し. Now for the fun part. This function can handle ARMAX models through the use of the xreg argument. For more information, refer to the GitHub README. Let us begin with the objectives of this lesson. TensorFlow From the course: Building Deep Learning Applications with Keras 2. You loaded and used a pretrained MobileNet model for classifying images from webcam. Get to know TensorFlow. layers import Input, Dense a = Input(shape=(32,)) b = Dense(32)(a) model = Model(inputs=a, outputs=b) This model will include all layers required in the computation of b given a. TF-LMS enables usage of high-resolution datasets, larger models and/or larger batch sizes by allowing the system memory to be used in conjunction with the GPU memory. https://js. The model. js is a library for developing and training machine learning models in JavaScript, and we can deploy these machine learning capabilities in a web browser. It shows how you can take an existing model built with a deep learning framework and use that to build a TensorRT engine using the provided parsers. The third step involves converting input data into tensors which TensorFlow. js performs a lot of synchronous computations, this can prevent the DOM from being blocked. fit() or LayersModel. js 可以为你提供高性能的、易于使用的机器学习构建模块,允许你在浏览器上训练模型,或以推断模式运行预训练的模型。TensorFlow. The model is saved in h5 format and converted into json for use in js. After some time training, the model should be smart enough to pick out photos of rock, paper, and scissors symbols that it's never seen before. fit(x, y, { batchSize: 1, epochs: 3 }) xとyにはそれぞれTensorが入ります。batchSizeは入力のサイズによって変わりますが、今回は本当に毎回呼ぶように1で設定しています。SGDを正しく使うため入力はnormalizeされます。 model. 1; To install this package with conda run one of the following: conda install -c conda-forge tensorflow. js, is a JavaScript library for training and deploying ML models in the browser. js Web Model Size with Weight Quantization. We need to get that data to the IBM Cloud platform. Existing models can be retrained using sensor data connected to the browser. 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. com TensorFlow. For exanple, when the outoput is a Dense layer with just one node, the entire network model is just doing some form of regression. fit() or LayersModel. Fit the model with the arima function in base R. Fit, Validate and Test. I am trying to fit a model to predict 3 vectors (x, y coordinates) when I show them 28x28px image tensors. 2、TensorFlow. js Tutorial p. This is a small library for in-browser visualization. Models are one of the primary abstractions used in TensorFlow. 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: