Tf Dataset

It allows you to do the data loading (from file or elsewhere) and some preprocessing in python before feeding. “TensorFlow - Importing data” Nov 21, 2017. How to load a custom dataset with tf. terms docs very good very bad you are great good restaurent nice place to visit doc1 tf-idf tf-idf tf-idf 0 0 doc2 0 tf-idf 0 tf-idf tf-idf I know, how to calculate DocumentTermMatrix of individual words but don't know how to make the corpus separated for each phrase in R. This function supports non-standard evaluation through the tidyeval framework. I use TensorFlow 1. You can read the full detailed explanation about the code used in this repository in my Medium post. This is essential information for those looking to use TensorFlow efficiently for real, large scale, data sets. from_tensor_slices该函数是dataset核心函数之一,它的作用是把给定的元组、列表和张量等数据进行特征切片。切片的范围是从最外层维度开始的。如果有多个 博文 来自: Dr_jiabin的博客. Python scripts to download public datasets and generate tfrecords. Both synthetic sequences generated in Blender as well as real-world sequences captured with an actual fisheye camera are provided. Example protocol buffers which contain Features as a field. feature_extraction module can be used to extract features in a format supported by machine learning algorithms from datasets consisting of formats such as text and image. The dataset contains 1,150 MIDI files and over 22,000 measures of drumming. TFRecordDataset. BLSA_ACTIHEART_TF_SQLDATA ACHQ04A char Comment if No Diary completed upon return BLSA_ACTIHEART_TF_SQLDATA ACHQ05 num Was Post Questionnaire completed upon return? Data dictionary for U:\BLSA_Core_Datasets 11:15 Tuesday, January 2, 2018. For face detection, the areas of interested are all localized. 12 in eager execution. Dynamic RNN (LSTM). Here, you can make use of tf. It is thought to have been created as a result of an ancient gene duplication event that led to generation of homologous C and N-terminal domains each of which binds one ion of ferric iron. We look into how to create TFRecords to and handle images from a custom dataset. data but use whatever you'd like for your model components. As we can see from the output, we iterate over the files in the Steinbeck collection. 75 similar to each other (this prevents very similar variants of the same motif from being taken). TensorFlow Datasets is a collection of datasets ready to use with TensorFlow. JASPAR is an open-access database of curated, non-redundant transcription factor (TF) binding profiles stored as position frequency matrices (PFMs) and TF flexible models (TFFMs) for TFs across multiple species in six taxonomic groups. To pipe data into Estimators we need to define a data importing function which returns a tf. Download the file for your platform. ; A complete example can be found on this gist. One column ( word here) contains the terms/tokens, one column contains the documents ( book in this case), and the last necessary column contains the counts, how many times each document contains each term ( n in this. For small datasets that fit in memory, you can pass batch_size=-1 to get the entire dataset at once as a tf. This site may not work in your browser. All gists Back to GitHub. In this talk, he introduces the tf. correct_prediction = tf. To read a file of TFRecords, use tf. Once created, convert them to state-space form before combining them with other models or performing model transformations. Why every TensorFlow developer should know about TFRecord! On Monday, Jan 8 2018 , by Naveen Honest Raj After few days of Tensorflow , every beginner will meet this crazy awesome Tensorflow’s file format called Tfrecords. Dynamic RNN (LSTM). 75 similar to each other (this prevents very similar variants of the same motif from being taken). This function supports non-standard evaluation through the tidyeval framework. Dataset 예제 형태에 대해서 설명드리도록 하겠습니다. If you only use one regional dataset TF_DATASETNAME can be included in your site configuration. If you see our previous example, we get one example every time we call the dataset object. keras, using a Convolutional Neural Network (CNN) architecture. Import the MNIST data set from the Tensorflow Examples Tutorial Data Repository and encode it in one hot encoded format. from_tensor_slides() is designed for small datasets that fit in memory. fetch_20newsgroups(). ucf101 is failing for Python 3 and TF 1. Note that a smoothing term is applied to avoid dividing by zero for terms outside the corpus. Data can be feed into TensorFlow using iterator. What does this mean? You are free to adapt, copy or redistribute the material, providing you attribute appropriately and do not use the material for commercial purposes. This data set contains WWW-pages collected from computer science departments of various universities in January 1997 by the World Wide Knowledge Base (Web->Kb) project of the CMU text learning group. TF-Ranking is optimized for models that require large datasets and provides a very simple developer experience based on TensorFlow Estimators. I solved the problem by using fit_genertor. 0 Unported licence. This is a big problem when you are using the euclidean distance in K-Means. train_and_evaluate with an Estimator model, and then show how easy it is to do distributed training of the model on Cloud ML Engine, moving between different cluster configurations with just a config tweak. I have a very expensive function which I map onto this dataset using tf. Generates new US-cities name, using LSTM network. If you only use one regional dataset TF_DATASETNAME can be included in your site configuration. If you see our previous example, we get one example every time we call the dataset object. map 😀 datasets in TF eager mode 😓 datasets in non-eager mode; Please take a moment to go through this checklist in your head. data has simple powerful methods for applying a wide variety of standard and custom transformations. Dataset of 60,000 28x28 grayscale images of the 10 digits, along with a test set of 10,000 images. This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. real_valued_column to build it. The following are code examples for showing how to use sklearn. Create an Estimator input function. To get started see the guide and our list of datasets. shuffle(buffer_size=2325000) ' ,the cost of time to load image. Download the file for your platform. I will also mention how I improved the model to change the accuracy of the model from 29% to 90% We load the…. 5 way of doing things. For small datasets that fit in memory, you can pass batch_size=-1 to get the entire dataset at once as a tf. The main difference from any other use of the Dataset API is how we parse out the sample. The dataset we are going to use are archives of few stories, this dataset has lots of documents in different formats. Python scripts to download public datasets and generate tfrecords. Introduction. Data and Dataset API. Dataset API to load the MNIST dataset form the data files. try to make a dataset with image files and labels from tf. repeat method restarts the Dataset when it reaches the end. datasets, 上一篇tensorflow数据读取是基于多线程数据抓取的方式,维护多队列(文件队列,example队列),是比较偏底层的。. Alternatively, if your input data are on disk in the recommended TFRecord format, you can construct a tf. To pipe data into Estimators we need to define a data importing function which returns a tf. Dataset object directly into keras. Learn how to build classifiers with TensorFlow. In the classic vector space model proposed by Salton, Wong and Yang the term-specific weights in the document vectors are products of local and global parameters. range(50) # A feedable iterator is defined by a handle placeholder and its structure. Softmax Regression using TensorFlow. Note that a smoothing term is applied to avoid dividing by zero for terms outside the corpus. The dataset we are going to use are archives of few stories, this dataset has lots of documents in different formats. But I have a large image dataset with 2,325,000 images, if I use the follwing code with 'dataset = dataset. In this article you have learnt hot to use tensorflow DNNClassifier estimator to classify MNIST dataset. The Groove MIDI Dataset (GMD) is composed of 13. We use cookies for various purposes including analytics. the training is performed on the MNIST dataset that is considered a Hello world for the deep learning examples. Pre-trained models and datasets built by Google and the community Tools Ecosystem of tools to help you use TensorFlow. See the MNIST For ML Beginners tutorial for an in-depth explanation of the code in this example. Results: The MEME-ChIP web service is designed to analyze ChIP-seq 'peak regions'—short genomic regions surrounding declared ChIP-seq 'peaks'. Python scripts to download public datasets and generate tfrecords. feature_extraction module can be used to extract features in a format supported by machine learning algorithms from datasets consisting of formats such as text and image. Note: In the video, I quickly went through testing. The bind_tf_idf function in the tidytext package takes a tidy text dataset as input with one row per token (term), per document. Binary files are sometimes easier to use, because you don’t have to specify different directories for images and groundtruth annotations. You are using the latest 7th release (2018) of JASPAR. TF is one method of extracting features from text: counting words. Dataset (solution). Transform) es una biblioteca para preprocesar datos con TensorFlow. from_tensor_slices() 来创建 Dataset;如果你的数据是 tfrecord 格式的,那么请使用 tf. If you don't need to share a network dataset, or the people you share with use the same ArcGIS release. Dataset comes with a couple of options to make our lives easier. TF-Slim and its components are already widely used within Google, and many of these improvements have already been integrated into tf. Differently from its predecessors. load_dataset(). data to build efficient data…. See Getting started for a quick tutorial on how to use this extension. It is a minimalist proposal, based on SPARQL's RDF Dataset, compatible with existing SPARQL implementations, and in line with the "Named Graphs" concept. This is a set of zero or more pairs (named graphs), plus one unnamed (default) g-snap. Learn how to use the TensorFlow Dataset API to create professional, high performance input data pipelines. Tensor 对象 dataset = dataset. We look into how to create TFRecords to and handle images from a custom dataset. Given a input tensor, returns a new tensor with the same values as the input tensor with shape shape. data but use whatever you'd like for your model components. Tensors / Transformations. MNIST database of handwritten digits. 75 similar to each other (this prevents very similar variants of the same motif from being taken). This is a re-implementation of Word2Vec relying on Tensorflow Estimators and Datasets. It loads data from the disk (images or text), applies optimized transformations, creates batches and sends it to the GPU. Highlight Str. Tensor to a given shape. Given a input tensor, returns a new tensor with the same values as the input tensor with shape shape. Data can be feed into TensorFlow using iterator. For example, in the textual dataset, each word in the corpus becomes feature and tf-idf score becomes its value. fetch_20newsgroups(). We saw that DNNClassifier works with dense tensor and require integer values specifying the class index. data has simple powerful methods for applying a wide variety of standard and custom transformations. The code for TF-Ranking including samples and tutorials is available on GitHub. You can then convert the resulting models back to transfer function form for inspection purposes. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. 0-rc1 and after. data to build efficient data…. For extra points, I added progress bars and MD5 checksums. This function supports non-standard evaluation through the tidyeval framework. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. Reading and transforming data are TensorFlow graph operations, so are executed in C++ and in parallel with model training. Build the network dataset. An overview of tf. I know we can ues dataset. To begin, just like before, we're going to grab the code we used in our basic. This gene encodes a glycoprotein with an approximate molecular weight of 76. Transcription factors specifically recognize short DNA segments, also known as transcription factor binding sites, at promoter or enhancer regions to stimulate or repress the transcriptional process. where do in the code do i nee to change it so it loads the data from my own directory ? i have a folder that contains 2 subduer of classes of images i want to use to train a neural net. Access to finance is frequently identified as a critical barrier to growth for MSMEs. The function. from_tensor_slices(). The assignment will serve two primary objectives - (a) understand and apply TF-IDF on a realistic task, (b) see what solving an NLP problem looks like end-to-end and (c) understand the fundamentals of how a search engine works. Implementing on a real world dataset. I am a great fan of the flexibility provided by tf. datasets, 上一篇tensorflow数据读取是基于多线程数据抓取的方式,维护多队列(文件队列,example队列),是比较偏底层的。. train_and_evaluate with an Estimator model, and then show how easy it is to do distributed training of the model on Cloud ML Engine, moving between different cluster configurations with just a config tweak. Tensor to a given shape. Transform) es una biblioteca para preprocesar datos con TensorFlow. Pre-trained models and datasets built by Google and the community Tools Ecosystem of tools to help you use TensorFlow. The Encyclopedia of DNA Elements (ENCODE) Consortium is an international collaboration of research groups funded by the National Human Genome Research Institute (). By continuing to use Pastebin, you agree to our use of cookies as described in the Cookies Policy. tfidf - Tf-idf library in python - Google Project Hosting. Dataset object directly into keras. Note that a smoothing term is applied to avoid dividing by zero for terms outside the corpus. data has simple powerful methods for applying a wide variety of standard and custom transformations. For face detection, the areas of interested are all localized. map 😀 datasets in TF eager mode 😓 datasets in non-eager mode; Please take a moment to go through this checklist in your head. Based on the official TF programmer’s guide, Dataset API introduces two abstractions: tf. Example protocol buffers which contain Features as a field. It predicts the probability of occurrence of an event by fitting data to a logit function. Skip to content. First, we need some values to be provided. To get started see the guide and our list of datasets. \] There are several variants on the definition of term frequency and document frequency. Update June 09, 2018. Alternatively, if your input data are on disk in the recommended TFRecord format, you can construct a tf. Note that a smoothing term is applied to avoid dividing by zero for terms outside the corpus. I will also mention how I improved the model to change the accuracy of the model from 29% to 90% We load the…. Dataset 사용 방법 텐서플로우 dataset 만들기 Estimator 모델에서 사용되는 데이터 입력 파이프라인인 tf. Transform) es una biblioteca para preprocesar datos con TensorFlow. from_tensor_slices该函数是dataset核心函数之一,它的作用是把给定的元组、列表和张量等数据进行特征切片。 切片的范围是从最外层维度开始的。. Its iterator() method is expected to generate a dataset iterator object, the next() method of which is expected to produce data batches for evaluation. Yes, you are confusion a lot of things. 5 way of doing things. Creating opportunities for MSMEs in emerging markets is a key way to advance economic development and reduce poverty. Apply an LSTM to IMDB sentiment dataset classification task. The code could probably be further improved by using the numpy_input_fn. range(0, 10, 2) ds1 = tf. A post showing how to perform Upsampling and Image Segmentation with a recently released TF-Slim library and pretrained models. Each of these values are added as columns. This scenario shows how to use TensorFlow to the classification task. data API of Tensorflow is a great way to build a pipeline for sending data to the GPU. Iterator allows you to extract items from your dataset one-by-one. Next the data will be converted from numphy arrays into TensorFlow tensors (tf. Even though libraries existed for a few years before collections. Stopword removal can be seen as feature selection. Dataset with the high level model API tf. Our dataset will be a set of 25,000+ Wikipedia articles. The bind_tf_idf function in the tidytext package takes a tidy text dataset as input with one row per token (term), per document. To get started see the guide and our list of datasets. Dynamic RNN (LSTM). We'll build a custom model and use Keras to do it. map() provide a way to tune the performance of your input pipeline: both arguments tell TensorFlow to create a buffer of at most buffer_size elements, and a background thread to fill that buffer in the background. Bi-Directional RNN (LSTM). The 8,282 pages were manually classified into the following categories. The files associated with this dataset are licensed under a Attribution-NonCommercial 3. Dataset: parse files and prepare training and validation datasets" This cell used the tf. parse_single_example decoder. It calls the tf_mnist. 6 Aug 2017 6 Aug 2017 indianpythonista Leave a comment. But then we'll convert that Keras model to a TensorFlow Estimator and feed TFRecord using tf. Simple and efficient data augmentations using the Tensorfow tf. The dataset we are going to use are archives of few stories, this dataset has lots of documents in different formats. data API of Tensorflow is a great way to build a pipeline for sending data to the GPU. A gif of a Latino man with the caption “It’s not working”. You can vote up the examples you like or vote down the ones you don't like. The main difference from any other use of the Dataset API is how we parse out the sample. To read a file of TFRecords, use tf. At DeepMind, the Research Platform Team builds infrastructure to empower and accelerate our AI research. So, you’ve got a cool idea for Actions on Google you want to build. In R, this API is implemented with the tensorflow R package. TFRecordReader with the tf. Note that a smoothing term is applied to avoid dividing by zero for terms outside the corpus. Reshapes a tf. real_valued_column to build it. For completeness, we describe each of these in Table 8. OK, I Understand. Fun with tf. fit? steps_per_epoch=len(x_train) // _BATCH_SIZE, This comment has been minimized. TensorFlow Transform (tf. But often with these higher level applications, you cannot access the little inbetween bits of the code,. Dataset Micro, Small and Medium Enterprises (MSMEs) are one of the strongest drivers of economic development, innovation and employment. Import the MNIST data set from the Tensorflow Examples Tutorial Data Repository and encode it in one hot encoded format. Starting from Tensorflow 1. Hi I am learning TensorFlow. In this blog, we will learn how to use TensorFlow's Dataset module tf. Data can be feed into TensorFlow using iterator. from_tensor_slices该函数是dataset核心函数之一,它的作用是把给定的元组、列表和张量等数据进行特征切片。切片的范围是从最外层维度开始的。如果有多个 博文 来自: Dr_jiabin的博客. The important understanding that comes from this article is the difference between one-hot tensor and dense tensor. Download the dataset and open your notebooks, Jupyter Notebooks i mean 😜. The function. What we've covered 🤔 tf. I use TensorFlow 1. Transcription factors specifically recognize short DNA segments, also known as transcription factor binding sites, at promoter or enhancer regions to stimulate or repress the transcriptional process. The Tensor Processing Unit (TPU) hardware accelerators we will be using in this lab are very fast. Models & datasets Pre-trained models and datasets built by Google and the community Tools Ecosystem of tools to help you use TensorFlow. Apply a bi-directional LSTM to IMDB sentiment dataset classification task. 这一部分主要参考tensorflow的官方教程:tf. ordereddict (I am the author of the latter package, which a. Dataset: parse files and prepare training and validation datasets" This cell used the tf. This scenario is introduces the high level tf. Dataset comes with a couple of options to make our lives easier. This is a re-implementation of Word2Vec relying on Tensorflow Estimators and Datasets. map 😀 datasets in TF eager mode 😓 datasets in non-eager mode; Please take a moment to go through this checklist in your head. Derek Murray discusses tf. Dataset stores your dataset and a tf. Summary: The tf. prefetch() and the output_buffer_size argument in tf. Here, you can make use of tf. ucf101 is failing for Python 3 and TF 1. map() provide a way to tune the performance of your input pipeline: both arguments tell TensorFlow to create a buffer of at most buffer_size elements, and a background thread to fill that buffer in the background. In this talk, he introduces the tf. batch, shuffle) also return a Dataset object. An in depth look at LSTMs can be found in this incredible blog post. It is a minimalist proposal, based on SPARQL's RDF Dataset, compatible with existing SPARQL implementations, and in line with the "Named Graphs" concept. placeholder(tf. Given a input tensor, returns a new tensor with the same values as the input tensor with shape shape. For face detection, the areas of interested are all localized. Access to finance is frequently identified as a critical barrier to growth for MSMEs. To feed the placeholders tf_train_dataset and tf_train_label,. real_valued_column to build it. Dataset) A dataset object. Our Estimator needs a new input function that read the TFRecord Dataset file, we call the tf. mllib, we separate TF and IDF to make them flexible. You check the accuracy by comparing your results with a test dataset. A post showing how to perform Image Classification and Image Segmentation with a recently released TF-Slim library and pretrained models. " However when I tried to run take(1) fu Skip to content. shuffle(buffer=10000) to shuffle dataset. (如果你的数据在内存中,请使用tf. Reviews have been preprocessed, and each review is encoded as a sequence of word indexes (integers). Genetic regulation depends to a great extent on sequence-specific transcription factors. 5 fold change and an adjusted pvalue ≤0. So, you’ve got a cool idea for Actions on Google you want to build. list_files 🤔 tf. Our Estimator needs a new input function that read the TFRecord Dataset file, we call the tf. Models & datasets Pre-trained models and datasets built by Google and the community Tools Ecosystem of tools to help you use TensorFlow. The TF-IDF measure is simply the product of TF and IDF: \[ TFIDF(t, d, D) = TF(t, d) \cdot IDF(t, D). TensorFlow is an open source software library created by Google that is used to implement machine learning and deep learning systems. Softmax Regression using TensorFlow. Dataset comes with a couple of options to make our lives easier. Visual Genome is a dataset, a knowledge base, an ongoing effort to connect structured image concepts to language. Download the file for your platform. What if we do not have x_train in memory but use tf. from_tensors and Dataset. log (y), reduction_indices = [1])) This is the cost function of the model – a cost function is a difference between the predicted value and the actual value that we are trying to minimize to improve the accuracy of the model. Fun with tf. If you see our previous example, we get one example every time we call the dataset object. To open the New Network Dataset wizard, right-click a feature dataset or a line shapefile in the catalog tree and choose New > Network Dataset. data API of Tensorflow is a great way to build a pipeline for sending data to the GPU. I have a dataset represented as a NumPy matrix of shape (num_features, num_examples) and I wish to convert it to TensorFlow type tf. Once created, convert them to state-space form before combining them with other models or performing model transformations. 0 end! 2、高维数据集使用. batch, shuffle) also return a Dataset object. zip((ds0, ds1)). Summary: The tf. ) [and/or] their transformations, the latter provides a way how to extract them in various ways depending on your needs. I have code that runs basic TF-IDF vectorizer on a collection of documents, returning a sparse matrix of D X F where D is the number of documents and F is the number of terms. Download files. Inside this function — which I developed by simply for-looping over the dataset in eager execution — I convert the tensors to NumPy arrays using EagerTensor. The following are code examples for showing how to use tensorflow. 05 (if applicable). SMS Spam Collection Data Set Download: Data Folder, Data Set Description. Build the network dataset. Both synthetic sequences generated in Blender as well as real-world sequences captured with an actual fisheye camera are provided. it still produces IndexError: list index out of range exception. It explains loading of your own data set, defining and training a CNN model, evaluating and testing the model,Visualizing intermediate output layers,plotting loss and accuracy curve, plotting. Introduction In the previous post, we saw how to do Image Classification by performing crop of the central part of an image and making an inference using one of the standart classification models. uniform(size=(5, 2))) 传入的数值是一个矩阵,它的形状为(5, 2),tf. One column ( word here) contains the terms/tokens, one column contains the documents ( book in this case), and the last necessary column contains the counts, how many times each document contains each term ( n in this. Deciding which observations from each dataset refer to the same doctor is more generally known as entity resolution (ER). But often with these higher level applications, you cannot access the little inbetween bits of the code,. This gene encodes a glycoprotein with an approximate molecular weight of 76. By continuing to use Pastebin, you agree to our use of cookies as described in the Cookies Policy. First, to get the data, run python run. from_generator. Calculate and bind the term frequency and inverse document frequency of a tidy text dataset, along with the product, tf-idf, to the dataset. map 😀 datasets in TF eager mode 😓 datasets in non-eager mode; Please take a moment to go through this checklist in your head. As a convenience for users that want simple NumPy arrays in their programs, you can use tfds. Tested the scripts with the ImageNet dataset. 0-rc1 and after. Dataset and tf. map 😀 datasets in TF eager mode 😓 datasets in non-eager mode; Please take a moment to go through this checklist in your head. from_tensors() or tf. Both figures show how the performance of the C4. from_generator. It calls the tf_mnist. It loads data from the disk (images or text), applies optimized transformations, creates batches and sends it to the GPU. This article discusses the basics of Softmax Regression and its implementation in Python using TensorFlow library. We learned about the TensorFlow core API in Chapter 1. - tmattio/tf_datasets. In this post we will cover how to convert a dataset into. It allows you to do the data loading (from file or elsewhere) and some preprocessing in python before feeding. Intuitively, it down-weights columns which appear frequently in a corpus. dataset, and in another "train" function to use these datasets to feed into the model, but I don't know how to feed these data in different function. Both synthetic sequences generated in Blender as well as real-world sequences captured with an actual fisheye camera are provided. 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: