Tensorflow Load Images From Directory

Installing Keras with TensorFlow backend. This folder is usually located on the drive where Windows is installed (for example, C:\users\your name\downloads). Create placeholders, load a bit of data into memory, compute on it, and loop with new data. Here we load the dataset then create variables for our test and training data:. Project utilities provided to simplify loading and using pre-trained models have not yet been extended with Node. Weights are downloaded automatically when instantiating a model. Pygame will automatically determine the image type (e. This means that in principle you could use this method to preload images, however it is not optimized for that purpose, and may in future be implemented in a way that does not fully load/download the image data. It is possible to write code to manually load image data and return data ready for modeling. The TensorFlow team already prepared a tutorial on how to execute the image classification on your machine. This example will involve creating a CIFAR-10 convolutional neural network image classifier. Contributing. We learned how to use Tensorflow to quickly create a neural network and train it easily. - mnist-to-jpg. Later we load these records into a model and do some predictions. 4 pycharm 2019. The only new variable we'll add is a mask for. Nothing special, but i would like to test on my own images. This can be largely attributed to the availability of cheaper hardware, programming libraries and labelled data. So, this is life, I got plenty of homework to do. This would include walking the directory structure for a dataset, loading image data, and returning the input (pixel arrays) and output (class integer). from tensorflow. Create jniLibs folder in main directory and put libtensorflow_inference. How can i load them for using with Tensorflow?. The first step to serve an ML model built in TensorFlow is to make sure it is in the right format. OpenCV comes with many powerful video editing functions. The model achieves 92. First of all, we need to structure our training and validation datasets. Run predict. # Import `skimage` from skimage import transform # Load the test data test_images, test_labels = load_data(test_data_directory) # Transform the images to 28 by 28 pixels test_images28 = [transform. In this post, I will implement a very simple RBM, i. 7% top-5 test accuracy in ImageNet , which is a dataset of over 14 million images belonging to 1000 classes. A list of the files in the folder will appear. However, to serve your trained model is not as easy. 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. js to perform visual recognition on images using JavaScript from Node. One use case of Singularity is to transparently use software in a container as through it were directly installed on the host system. Ensure that it is your current working directory, checkout the branch and check the contents, as follows: cd tensorflow-for-poets-2 ls. First, download this image (Right Click, and. Description. Recently RStudio has released a package that allows to use TensorFlow in R. In this post you will discover how to use data preparation and data augmentation with your image datasets when developing. array(test. < p > It is common to store images in a sub-folder. You use the same type of attribute as the href attribute from the last article, so having used that before will help you get your head around this quicker. and press enter. That’s it! The KerasModelImport is your main entry point to model import and class takes care of mapping Keras to DL4J concepts internally. To accomplish this on our systems, you need to be aware of the shared filesystem locations and bind mount the corresponding directories inside the container, which is more complicated than it seems because we use symbolic links to refer to some of our network. # Import `skimage` from skimage import transform # Load the test data test_images, test_labels = load_data(test_data_directory) # Transform the images to 28 by 28 pixels test_images28 = [transform. [code]├── current directory ├── _data | └── train | ├── test [/code]If your directory flow is like this then you ca. Load the image of my handwritten number. implementation 'org. Run your Keras models in C++ Tensorflow. With my HP laptop when iTunes started the pictures in my iPhone 4s automatically transferred to my Pictures folder. Objectives. It is possible to write code to manually load image data and return data ready for modeling. In some cases it will need to know the file extension (e. While Windows says you can’t restore individual files from these backups, there’s a way to browse the contents of a system image and extract individual files. TensorFlow is a very important Machine/Deep Learning framework and Ubuntu Linux is a great workstation platform for this type of work. A complete guide to using Keras as part of a TensorFlow workflow. Install Tensorflow 2. Different file contents are denoted by the file extension, or letters after the dot, of the file name. (Although there is some variation of behaviour between browsers. This tutorial shows how to use TensorFlow with Horovod on a Deep Learning AMI with Conda. This directory contains the classify_image. it should. I just copied the file in the bin folder and it is still working fine so again, if you know why we should do that, please let me know in the comment section; Sadly, Tensorflow gpu 1. pb file in the project's assets folder. For example one option for image classification could be to have text files with all the images filenames, followed by it's class. TensorFlow is a powerful open-source software library for machine learning developed by researchers at Google. --name tensorflow gives our container the name tensorflow instead of sneaky_chowderhead or whatever random name Docker might pick for us. Processing currently works with GIF, JPEG, and PNG images. How to install and run GPU enabled TensorFlow on Windows In November 2016 with the release of TensorFlow 0. In order to retrieve the image dimensions, the image may first need to be loaded or downloaded, after which it will be cached. I made a secondary image that builds from the official tensorflow image, add the bazel binaries, then installs the serving part. pip install --upgrade tensorflow. How do I transfer pictures from iPhone to dell laptop. png 1 image5. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in…. Now, in TensorFlow Image Recognition Using C++ API y ou can run the same Inception-v3 using the C++ API. This section presents the changes I’ve added to bamos/dcgan-completion. In the AutoPlay dialog box that appears, click Import pictures and videos using Windows. 0 module load cudnn/5. Folder with file extensions showing. from mlxtend. The example folder fruits images should have a structure like this: We will create a ~/tf_files/fruits folder and place each set of jpeg images in subdirectories (such as ~/tf_files/fruits/apple, ~/tf_files/fruits/orange etc) The subfolder names are important. The directory parameter specifies the exact location of the images. Unlike the Keras or Scikit-Learn packages, TensorFlow has no predefined module to load the Fashion MNIST dataset, though by default it has MNIST dataset. TensorFlow is open source ML framework designed for deep Neural Networks and created by the Google Brain Team. Most of the information in this blog post is still valid but the sections about building TensorFlow for iOS are out-of-date. Generates batches of data from images in a directory (with optional augmented/normalized data) , image_load. As we will see later, the original image is 28 x 28 x 1 image, and the transformed image is 7 x 7 x 32. With relatively same images, it will be easy to implement this logic for security purposes. Is almost entirely up to you to load data on tensorflow, which means you need to parse the data yourself. I assume you are using Python with Tensorflow. [[_text]]. For this we will use tensorflow Example Queues, which are well explained in the documentation. png 0 image3. They are extracted from open source Python projects. # Coordinate the loading of image files. Downloading this model requires that you have 200MB of free space available on disk. It's not a free app, like My Lockbox, but it does have excellent configuration options and lots of ways of keeping important and private documents away from prying eyes. Training a deep neural network model could take quite some time, depending on the complexity of your model, the amount of data you have, the hardware you're running your models on, etc. In this tutorial, we are going to work with an image, in order to visualise changes to an array. Stack Exchange network consists of 175 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. tensorflow/tensorflow:nightly says run the nightly image of tensorflow/tensorflow from Docker Hub (a public image repository) instead of latest (by default, the most. These images do not come with the TensorFlow source code -- but the development images have git included, so you can git clone it yourself. It has many pre-built functions to ease the task of building different neural networks. We now have running server on the port 3000 and we can finally load the model inside of the Angular application. 12 we can now run TensorFlow on Windows machines without going through Docker or a VirtualBox virtual machine. Windows Download ImageJ bundled with 64-bit Java 1. They define what label is applied to each image, but the filenames themselves don't. TensorFlow C++ and Python Image Recognition Demo. TFLearn - Quick Start. Building an image classifier in TensorFlow 2. But of course, we can only load saved variables if there is a valid checkpoint. Pygame will automatically determine the image type (e. py file which uses TensorFlow to recognize images. In it, I'll describe the steps one has to take to load the pre-trained Coco SSD model, how to use it, and how to build a simple implementation to detect objects from a given image. The first part of this blog post provides a short discussion of Keras backends and why we should (or should not) care which one we are using. TensorFlow is a powerful open-source software library for machine learning developed by researchers at Google. Doing this is the same process as we've needed to do to train the model, so we'll be recycling quite a bit of code. Image Analysis is a very common field in the area of Computer Vision. 'folder' accepts a root folder with each of his sub-folder representing a class containing the images to classify. Load more. com, and one of the ones I created was a lightweight reverse image search. Loading images. NET Standard bindings for TensorFlow. Projects Community Docs. Through it, you can train a computer to see and interpret images similar to the way humans perceive images. This tutorial shows how to use TensorFlow with Horovod on a Deep Learning AMI with Conda. This is a project straight from our Deep Learning Nanodegree program. One of the advantage of Tensorflow is that it has libraries for Mobile devices such as iOS and Android. @srikar2097, I found the reason for that issue. Dockerfile files in the partials directory, then run assembler. For each image in this dataset, one should predict a probability that the image is a dog (1 = dog, 0. 2), I decided to give it a try anyway. To use a pretrained network for prediction or transfer learning on new images, you must preprocess your images in the same way as the images used to train the imported model were preprocessed. from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import os import tempfile import. and press enter. This will do the trick. TensorFlow is the platform enabling building deep Neural Network architectures and perform Deep Learning. To load the canvas with an image data URL, we can make an AJAX call to get a data URL, create an image object with the URL, and then draw the image onto the canvas with the drawImage() method of the canvas context. the training is performed on the MNIST dataset that is considered a Hello world for the deep learning examples. If you're involved with machine learning, you probably heard the news by now that Google open-sourced their machine learning library TensorFlow a few weeks ago. Let's start with a new flutter project with java and swift as a language choice. However, it does not mean that is the best way to do it and that this way will help in further steps. I further splitted this images into a training, validation and test set (70/15/15) and created. filename_queue = tf. Let us dive into the code! 2. It has not been explained in the Tensorflow documentation how to load images and labels directly from a TXT file. First, all the images are converted to gray-scale images. and extract in the same folder. Github rep. If you want a more customized installation, e. I just copied the file in the bin folder and it is still working fine so again, if you know why we should do that, please let me know in the comment section; Sadly, Tensorflow gpu 1. txt files for each subset containing the path to the image and the class label. If you are familiar with TF, you have probably used the TensorFlow Saver to persist your model's variables. Image classifier scenario – Train your own custom deep learning model with ML. Generating interesting arrays can be difficult, but images provide a great option. This tutorial provides a simple example of how to load an image dataset using tf. Visualize high dimensional data. ref: the reference for the image or video. The image files (provided they are real images and not shapes or autoshapes) are stored in a sub folder called Media and can be extracted and renamed for re-use: You can use this method to extract images from DOC format documents, provided you save them from Word 2007/2010 as DOCX format with the compatibility option unchecked first. Angular Application. You can use: the Share Button App on your KODAK Camera the USB cable that came with your camera; a memory card that contains your pictures; To use the Share Button App, see Use the Share Button App to transfer and share pictures and videos. js) enables us to build machine learning and deep learning models right in our browser without needing any complex installation steps There are two components to TensorFlow. While it's fresh in my head, I wanted to write up an end-to-end description of what it's like to build a machine learning app, and more specifically, how to make your own reverse image search. Later we load these records into a model and do some predictions. Available models. NET library is an open source and low level API library that provides the. This example shows how you can load a pre-trained TensorFlow network and use it to recognize objects in images in C++. Click Import. Interpreter, either on your machine or Colab notebook. models folder: This is where you put your model files. Having to train an image-classification model using very little data is a common situation, in this article we review three techniques for tackling this problem including feature extraction and fine tuning from a pretrained network. Project utilities provided to simplify loading and using pre-trained models have not yet been extended with Node. Read Image from file using Python and OpenCV. To focus this tutorial on the subject of image recognition, I simply used an image of a bird added to the assets folder. ; Sometimes, it will be the other way round, the dimension input feature is too small, we need to do some transformation on the input feature to expand its dimension. More info. Loading images. Next up - let's upload our images to FloydHub as a FloydHub Dataset. Host or bundle your model. js – Core API and Layers API. Coach uses TensorFlow as its main backend framework, but it also supports MXNet. Also recently several trained models for image classification have been released. Supported methods are "nearest", "bilinear. This is a project straight from our Deep Learning Nanodegree program. The image() function draws an image to the display window. Fun with Tensor Boar d In TensorFlow, you collectively call constants, variables, operators as ops. [code]├── current directory ├── _data | └── train | ├── test [/code]If your directory flow is like this then you ca. These models can be used for prediction, feature extraction, and fine-tuning. 0 Docker image. SageMaker TensorFlow CPU images use TensorFlow built with Intel® MKL-DNN optimization. if you want to take advantage of NVIDIA GPUs, see the documentation for install_keras() from the keras R library. Pay attention that we also write the sizes of the images along with the image in the raw. pb file (also called "frozen graph def" which is essentially a serialized graph_def protocol buffer written to disk) and make predictions with it from C# for scenarios like image classification,. This is the basic stuff — just getting the image on your page. docker run --rm -p 8787:8787 rocker/verse the software first checked if this image is available on your computer and since it wasn’t it downloaded the image from Docker Hub. So, this is life, I got plenty of homework to do. Hello all! Many of you have been asking about a recipe for image. While it's fresh in my head, I wanted to write up an end-to-end description of what it's like to build a machine learning app, and more specifically, how to make your own reverse image search. The images stored as Binary data will be fetched as BYTE Array and then the BYTE Array will be converted to BASE64 string and then assigned to the ASP. Another way to learn about the model is to load it with Python tf. Note: if you want to see the kind of graph I save/load/freeze, you can here. This directory should contain three other subdirectories: The android/tflite directory contains all the files necessary to build a simple Android app using TFLite to classify images as it reads them from the. The demand and support for Tensorflow has contributed to host of OSS libraries, tools and frameworks around training and serving ML models. Building an image data pipeline. What about if you wanted to add TensorFlow code that modifies the Inception v3 model architecture to fit your own image classification problem? Sure, you can find some images of the Inception v3 architecture online, but understanding how it's actually implemented in TensorFlow is a whole separate beast. How I built a Convolutional Image classifier using Tensorflow from Scratch. source_dir – Path (absolute or relative) to a directory with any other serving source code dependencies aside from the entry point file. For this example, we will download images of 5 types of flowers with over 700 images for each type. I'm trying to come up with a Keras model based on LSTM layers that would do binary classification on image sequences. 5k images Dataset Good hands-on experience with TensorFlow Desire to get into the MXNet that everyone is talking about 2 GPUs Let’s go! Algorithms and approaches: InceptionV3 & ResNet – we tested both architectures for the best results and capped in 56% accuracy for ResNet and 94%(for MXNet)/82%(TensoFlow) for InceptionV3. Append(mlContext. With my HP laptop when iTunes started the pictures in my iPhone 4s automatically transferred to my Pictures folder. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in…. What is cifar-10? "CIFAR-10 is an established computer-vision dataset used for object recognition. While Windows says you can’t restore individual files from these backups, there’s a way to browse the contents of a system image and extract individual files. pyplot as plt Load data. We can re-use a lot of the existing variables for completion. yml and the *. Host or bundle your model. You use the same type of attribute as the href attribute from the last article, so having used that before will help you get your head around this quicker. In this case, we've loaded 4 Excel workbooks. The only new variable we'll add is a mask for. x_col and y_col are the independent and dependent variables, in this case, the images and the labels. target_size: Either NULL (default to original size) or integer vector (img_height, img_width). The image() function draws an image to the display window. This section describes how to create an image and how to use. Once combined in a stack, you can process the multiple images to produce a composite view that eliminates unwanted content or noise. This wikiHow teaches you how to download one or more images manually from a single webpage to your iPhone or iPad, Android device, or desktop computer. Another way to learn about the model is to load it with Python tf. Here is what a Dataset for images might look like. Serving example. Google Developers is the place to find all Google developer documentation, resources, events, and products. The Tensorflow. See the Troubleshoot extensions, themes and hardware acceleration issues to solve common Firefox problems article for instructions on how to disable or uninstall extensions that may cause images not to load in Firefox. read I am new to tensorflow and i want to make a model for image recognition. This is going to be a tutorial on how to install tensorflow GPU on Windows OS. OpenCV offers support for the image formats Windows bitmap (bmp), portable image formats (pbm, pgm, ppm) and Sun raster (sr, ras). ckpt放置在待训练的目录,这里meta文件保存了graph和metadata,ckpt保存了网络的weights,这几个文件表示预训练模型的初始状态。. models folder: This is where you put your model files. Now for s implicity, we are going to keep "models" and "protobuf" under one folder "Tensorflow". I further splitted this images into a training, validation and test set (70/15/15) and created. Keras - Save and Load Your Deep Learning Models. module load binutils/2. How I built a Convolutional Image classifier using Tensorflow from Scratch. [ML-Heavy] TensorFlow implementation of image completion with DCGANs. Generates batches of data from images in a directory (with optional augmented/normalized data) , image_load. Add any image you want to predict to the assets folder. In the folder where Angular application is created make sure you have run the commands: npm install -g @tensorflow/tfjs npm install ng serve. Loading Images and Labels from csv Files # load images im_content = tf. One use case of Singularity is to transparently use software in a container as through it were directly installed on the host system. If not specified and endpoint_type is 'tensorflow-serving', no source_dir is used. Make your script prepare the folder nicely if needed, for example, the script can create the train/val/test sub-folders if they don’t exist. sketch-rnn is a recurrent neural network model described in Teaching Machines to Draw and A Neural Representation of Sketch Drawings. 上一篇已经配置好了Openfaceswap的环境,这一篇我们来看看怎么使用Openfaceswap。这个压缩包里面包含了一些基本素材,在workspace文件夹里,并且已经将Github上面的新版本faceswap放到里面,支持本身GUI界面,可以根据自己的习惯来选择用哪个。. 'folder' accepts a root folder with each of his sub-folder representing a class containing the images to classify. Upload your photos faster with Google Chrome. py to re-generate the full Dockerfiles before creating a pull request. Ensure that it is your current working directory, checkout the branch and check the contents, as follows: cd tensorflow-for-poets-2 ls. 2 (community edition). I hope tensorflow can be as nice as Torch7 is, unfortunately it is not. Import the MNIST data set from the Tensorflow Examples Tutorial Data Repository and encode it in one hot encoded format. If there isn’t, train from the start. 0 Docker image. In this article, we will play around with a simple Multi-label classification problem. There’s no special method to load data in Keras from local drive, just save the test and train data in there respective folder. keras is better maintained and has better integration with TensorFlow features (eager execution, distribution support and other). 'file' accepts a. By default, the folder name includes the date the pictures and videos are imported and the tag name. I am trying the find the pretrained models (graph. string_input_producer. Deploying a Dog Identification TensorFlow Model With Python and Flask (i. TXT is text, DOCX is Microsoft Word, PNG are images, CSV is comma-separated value data. tfrrecord file and read it without defining a computational graph. Run predict. Note that for this tutorial the image must contain Jupyter. To store images, we should define an array for each of train, validation and test sets with the shape of (number of data, image_height, image_width, image_depth) in Tensorflow order or (number of data, image_height, image_width, image_depth) in Theano order. html file, and script. The table below contains list of supported TensorFlow*-Slim Image Classification Model Library models and required mean/scale values. Getting this working did end up. TensorFlow C++ and Python Image Recognition Demo. Image Segmentation with Tensorflow using CNNs and Conditional Random Fields Tensorflow and TF-Slim | Dec 18, 2016 A post showing how to perform Image Segmentation with a recently released TF-Slim library and pretrained models. js and additional for tfjs-vis. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in…. In this blog post, we demonstrate the use of transfer learning with pre-trained computer vision models, using the keras TensorFlow abstraction library. This event can be sent to any element associated with a URL: images, scripts, frames, iframes, and the window object. So, here I decided to summarize my experience on how to feed your own image data to tensorflow and build a simple conv. tensorflow/tensorflow:nightly says run the nightly image of tensorflow/tensorflow from Docker Hub (a public image repository) instead of latest (by default, the most. The function imwrite saves the image to the specified file. png 1 image5. Doing this is the same process as we've needed to do to train the model, so we'll be recycling quite a bit of code. StandardScaler details: Windows10 TensorFlow 1. Create placeholders, load a bit of data into memory, compute on it, and loop with new data. We also make sure that images that we read back from. Run the following commands:. Load The MNIST Data Set in TensorFlow So That It Is In One Hot Encoded Format. Because you can access GPUs while using a Docker container, it's also a great way to link Tensorflow or any dependencies your machine learning code has so anyone can use your work. To load our image using try/except:. grayscale: Boolean, whether to load the image as grayscale. As of today, it is the most popular and active ML project on GitHub. TensorFlow Hub, MobileNet V2. Training a deep neural network model could take quite some time, depending on the complexity of your model, the amount of data you have, the hardware you're running your models on, etc. In Tutorials. This tutorial provides a simple example of how to load an image dataset using tf. For this example, we will download images of 5 types of flowers with over 700 images for each type. Installing Keras with TensorFlow backend. 0 module load python/3. the folder scripts that contains useful utility functions for loading graphs and transforming images into. Image Recognition with Tensorflow training on Kubernetes The left one is loading the training images and the categories from an Object Store, trains the neural. How I built a Convolutional Image classifier using Tensorflow from Scratch. We will focus on using the. opensource. Once a model is optimized with TensorRT, the traditional Tensorflow workflow is still used for inferencing, including TensorFlow Serving. Once you’ve verified that all the files you want are listed, select the Combine & Load Option. If you want to run TensorFlow in a container, then we need to dig deeper. Path to image file. Downloading this model requires that you have 200MB of free space available on disk. OpenCV comes with many powerful video editing functions. read_data_sets("mnist_data/", one_hot=True) The code uses built-in capabilities of TensorFlow to download the dataset locally and load it into the python variable. Now let's first train a logistic regression and then a couple of neural network models by introducing L2 regularization for both the models. samples/batch_size). The code below illustrates how I achieved it. What you'll Learn. Create a new folder inside the pythonenv folder called tflow and inside that, create a new file called tflow. To load the canvas with an image data URL, we can make an AJAX call to get a data URL, create an image object with the URL, and then draw the image onto the canvas with the drawImage() method of the canvas context. class_mode="binary" specifies that the data consists of only 2 distinct classes which are cats and dogs. In normal cases, you don't need to call this method, since the Image class image. Object Detection Tutorial (YOLO) Description In this tutorial we will go step by step on how to run state of the art object detection CNN (YOLO) using open source projects and TensorFlow, YOLO is a R-CNN network for detecting objects and proposing bounding boxes on them. These images are provided by the TensorFlow project and include images of various flowers. The Tensorflow. Installing TensorFlow on the latest Ubuntu is not straightforward To utilise a GPU it is necessary to install CUDA and CuDNN libraries before compiling TensorFlow Any serious quant trading research with machine learning models necessitates the use of a framework that abstracts away the model. We can download the images of our choice from google. It would be a very bad idea to load all 15,000 images into memory at once. you might have to scroll right to see it all). Initialize the TensorFlow graph and run it for a large number of iterations (we have used 200 iterations), updating the generated image at every step. Loading Images in a Directory As Tensorflow Data set. This will do the trick. Generates batches of data from images in a directory (with optional augmented/normalized data) , image_load. Resizing images, subtracting the average image, and converting the images from RGB to BGR format are the most common preprocessing operations. NET you can load a frozen TensorFlow model. import tensorflow as tf # Make a Dataset of file names including all the PNG images files in # the relative image directory. Windows Download ImageJ bundled with 64-bit Java 1. Below you can see how they fit in the TensorFlow architecture. Files you've downloaded are automatically saved in the Downloads folder. Once the image is loaded, you can do a number of things with it. NET Standard bindings for TensorFlow. Tensorflow Image Recognition Tutorial¶ This tutorial shows how we can use MLDB's TensorFlow integration to do image recognition. Store the MNIST data in a folder. This does not happen with my Dell laptop. txt) files for Tensorflow (for all of the Inception versions and MobileNet) After much searching I found some models in, https://sto. 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: