Tensorflow Keras Layers

TensorFlow includes a special feature of image recognition and these images are stored in a specific folder. #in the functional API you create layers and call them passing tensors to get their output: conc = Concatenate()([model1. This layer has no parameters to learn; it only reformats the data. Now that TensorFlow 2. Why does the Keras version have 32 nodes in the hidden layer. Filters; Stacking Multiple Feature Maps; TensorFlow Implementation; Memory Requirements; Pooling Layers. The strategy that made this happen seems to have been straightforward. Instead, it uses another library to do. Infact, Keras needs any of these backend deep-learning engines, but Keras officially recommends TensorFlow. How do I do that? tf. TimeDistributed layer can't be save to saved Model Tensorflow 2. A fast-paced introduction to Deep Learning that starts with a simple yet complete neural network (no frameworks), followed by an overview of activation functions, cost functions, backpropagation, and then a quick dive into CNNs. implementation() Keras implementation. With relatively same images, it will be easy to implement this logic for security purposes. Keras and TensorFlow. keras package, and the Keras layers are very useful when building your own models. The format is inferred from the file extension you provide: if it is ". However, Keras is used most often with TensorFlow. fit() in Keras! I have tried both allow_growth and per_process_gpu_memory_fraction in Tensorflow as well. optimizers import Adam from keras. Comparing XOR between tensorflow and keras. keras to call it. An Open Source Machine Learning Framework for Everyone - tensorflow/tensorflow. Run Keras models in the browser, with GPU support provided by WebGL 2. input_layer. To use this with Keras, we make a dataset out of elements of the form (input batch, output batch). This function adds an independent layer for each time step in the recurrent model. 1BestCsharp blog 7,766,141 views. You can vote up the examples you like or vote down the ones you don't like. Dense(5, activation='softmax')(y) model = tf. fully-connected layers). Keras' foundational principles are modularity and user-friendliness, meaning that while Keras is quite powerful, it is easy to use and scale. 5 I typed: conda create -n tf-keras python=3. layers import Dropout def mlp_model(layers, units, dropout_rate, input_shape, num_classes): """Creates an instance of a multi-layer perceptron model. Flatten, transforms the format of the images from a two-dimensional array (of 28 by 28 pixels) to a one-dimensional array (of 28 * 28 = 784 pixels). TensorFlow v1. Output shape. Company running summary() on your layer and a standard layer. 04): Windows 10 Mobile device (e. This means that Keras is appropriate for building essentially any deep learning model, from a memory network to a neural Turing machine. Java Project Tutorial - Make Login and Register Form Step by Step Using NetBeans And MySQL Database - Duration: 3:43:32. luvwinnie changed the title tf. Batch normalization layer (Ioffe and Szegedy, 2014). TensorFlow Hub Loading. ● Easy extensibility. The core data structure of Keras is a model, a way to organize layers. If all inputs in the model are named, you can also pass a list mapping input names to data. This function adds an independent layer for each time step in the recurrent model. The model runs on top of TensorFlow, and was developed by Google. Supports arbitrary network architectures: multi-input or multi-output models, layer sharing, model sharing, etc. We will cover the details of every layer in future posts. layers import Dense, Dropout. 0 (we’ll use this today!) Easier to use. x_train and x_test parts contain greyscale RGB codes (from 0 to 255) while y_train and y_test parts contain labels from 0 to 9. instead of from keras. js Layers。. GPU CPU TPU TensorFlow tf. This API makes it easy to build models that combine deep learning. 1BestCsharp blog 7,766,141 views. from __future__ import absolute_import, division, print_function, unicode_literals import numpy as np import tensorflow as tf from tensorflow. Keras Layers. In this part, what we're going to be talking about is TensorBoard. I've already written one tutorial on how to train a Neural Network with TensorFlow's Keras API, focusing on AutoEncoders. Apr 26 2019- POSTED BY Brijesh Comments Off on TensorFlow Keras UNet for Image Image Segmentation Spread the love This tutorial provides a brief explanation of the U-Net architecture as well as implement it using TensorFlow High-level API. Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. Can confirm that updating to >=1. In Keras, we can implement dropout by added Dropout layers into our network architecture. Keras is installed in Python 3. Read writing about Keras in TensorFlow. Tensorflow offers access to the keras layers in tf. 4正式添加了keras和data作为其核心代码(从contrib中毕业),加上之前的estimator API,现在已经可以利用Tensorflow像keras一样方便的搭建网络进行训练。. Keras automatically handles the connections between layers. TensorFlow 2. h5') Weights-only saving using TensorFlow checkpoints. The core data structure of Keras is a model, a way to organize layers. To install both the core Keras library as well as the TensorFlow backend use the install_keras() function: library (keras) install_keras This will provide you with default CPU-based installations of Keras and TensorFlow. You can add layers to the existing model/graph to build the network you want. keras will give developers access to features like eager execution, TPU training, and much better integration between low-level TensorFlow and high-level concepts like Layer and Model. Keras is a high level API built on TensorFlow (and can be used on top of Theano too). callbacks import ModelCheckpoint from keras. class LSTM : Long Short-Term Memory layer - Hochreiter 1997. Next we'll create a neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. TensorFlow includes a special feature of image recognition and these images are stored in a specific folder. In today’s blog post I provide detailed, step-by-step instructions to install Keras using a TensorFlow backend, originally developed by the researchers and engineers on the Google Brain Team. But I want to print out the layer to make sure that the numbers flowing through are correct. Keras is a high-level neural networks API, written in Python and capable of running on top of either TensorFlow or Theano. The following are code examples for showing how to use keras. from tensorflow. In Tensorflow 2. models import Model from keras. fit(), making sure to pass both callbacks. When a filter responds strongly to some feature, it does so in a specific x,y location. Attention-based Image Captioning with Keras. The network is trained for 100 epochs and a batch size of 1 is used. use_implementation() use_backend() Select a Keras implementation and backend. They are extracted from open source Python projects. Keras is now part of the core TensorFlow package; Dataset API become part of the core package; Some enhancements to the Estimator allow us to turn Keras model to TensorFlow estimator and leverage its Dataset API. You can vote up the examples you like or vote down the ones you don't like. In the first part of this tutorial, we’ll discuss the concept of traffic sign classification and recognition, including the dataset we’ll be using to train our own custom traffic sign classifier. I've already written one tutorial on how to train a Neural Network with TensorFlow's Keras API, focusing on AutoEncoders. layer里import,而不是keras. If all inputs in the model are named, you can also pass a list mapping input names to data. Things to try: I assume you have a test program that uses your customer layer. It was developed with a focus on enabling fast experimentation. The main data structure you'll work with is the Layer. x_train and x_test parts contain greyscale RGB codes (from 0 to 255) while y_train and y_test parts contain labels from 0 to 9. Note that if the recurrent layer is not the first layer in your model, you would need to specify the input length at the level of the first layer (e. Batch normalization layer (Ioffe and Szegedy, 2014). Image-style-transfer requires calculation of VGG19 's output on the given images and since I was familiar with the nice API of Keras and keras. TensorFlow is an end-to-end open source platform for machine learning. etc as well as those are not specified in the backend documents but actually supported by Theano and TensorFlow. Keras, on the other hand, is a high-level abstraction layer on top of popular deep learning frameworks such as TensorFlow and Microsoft Cognitive Toolkit—previously known as CNTK; Keras not only uses those frameworks as execution engines to do the math, but it is also can export the deep learning models so that other frameworks can pick them up. Note that save_weights can create files either in the Keras HDF5 format, or in the TensorFlow Checkpoint format. This layer can add rows and columns of zeros at the top, bottom, left and right side of an image tensor. Keras is a neural network API that is written in Python. Keras makes use of TensorFlow's functions and abilities, but it streamlines the implementation of TensorFlow functions, making building a neural network much simpler and easier. Theano expects epsilon >= 1e-5. Table of contents. Think of this layer as unstacking rows of pixels in the image and lining them up. Create new layers, metrics, loss functions, and develop state-of-the-art models. clear_session() # For easy reset of notebook state. luvwinnie changed the title tf. The Architecture of the Visual Cortex; Convolutional Layers. About six months ago, we showed how to create a custom wrapper to obtain uncertainty estimates from a Keras network. We're going to talk about complex multi-input and multi-output models, different nodes from those models, sharing layers and more. The guide Keras: A Quick Overview will help you get started. The objective is to identify (predict) different fashion products from the given images using a CNN model. Without GPU support, so even if you. In Tensorflow 2. Being able to go from idea to result with the least possible delay is key to doing good research. Sequential model. So you can just start using the Keras API at no loss of flexibility. Put another way, you write Keras code using Python. 3D tensor with shape (batch_size, timesteps, input_dim). Keras Preprocessing Layers; TF Transform; The TensorFlow Datasets (TFDS) Project; Exercises; 14. You can vote up the examples you like or vote down the ones you don't like. Keras is a high-level neural networks API, developed with a focus on enabling fast experimentation and not for final products. An Open Source Machine Learning Framework for Everyone - tensorflow/tensorflow. In this post you will discover the dropout regularization technique and how to apply it to your models in Python with Keras. When compared to TensorFlow, Keras API might look less daunting and easier to work with, especially when you are doing quick experiments and build a model with standard layers. The default sigmoid activation function is used for the LSTM blocks. The folder structure of image recognition code implementation is as shown below − The dataset. a Inception V1). Any idea how do I solve this problem?. Can I use the keras layers directly in the tensorflow code? If so, how? Could I also use the tf. In this illustration, you see the result of two consecutive 3x3 filters. We used the Keras Sequential model API, which lets us build a linear stack of layers. The following are code examples for showing how to use keras. Each layer in Keras will have an input shape and an output shape. Sequential model. clear_session() # For easy reset of notebook state. Output shape. An open source Deep Learning library Released by Google in 2015 >1800 contributors worldwide TensorFlow 2. The simplest type of model is the Sequential model, a linear stack of layers. 0 and it is a game-changer! Here's how: Going forward, Keras will be the high level API for TensorFlow and it's extended so that you can use all the advanced features of TensorFlow directly from tf. ゼロからKerasとTensorFlow(TF)を自由自在に動かせるようになる。 そのための、End to Endの作業ログ(備忘録)を残す。 ※環境はMacだが、他のOSでの汎用性を保つように意識。 ※アジャイルで執筆しており、精度を逐次高めていく. But I want to print out the layer to make sure that the numbers flowing through are correct. tensorflow/addons:RectifiedAdam; Usage import keras import numpy as np from keras_radam import RAdam # Build toy model with RAdam optimizer model = keras. TensorFlow has two mobile libraries, TensorFlow Mobile and TensorFlow Lite. 0 and it is a game-changer! Here's how: Going forward, Keras will be the high level API for TensorFlow and it's extended so that you can use all the advanced features of TensorFlow directly from tf. Next we'll create a neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. Keras is a high level API built on TensorFlow (and can be used on top of Theano too). It is designed to be modular, fast and easy to use. via the input_shape argument) Input shape. If you find a mistake or think an important term is missing, please let me know in the comments or via email. TensorFlow or Keras? Which one should I learn? In this blog post, I am only going to focus on Tensorflow and Keras. An Open Source Machine Learning Framework for Everyone - tensorflow/tensorflow. A Keras tensor is a tensor object from the underlying backend (Theano, TensorFlow or CNTK), which we augment with certain attributes that allow us to build a Keras model just by knowing the inputs and outputs of the model. Prerequisites The code in this post depends on the development versions of several of the TensorFlow R packages. , for faster network training. About Keras in R. Keras(Tensorflowバックグラウンド)を用いた画像認識の入門として、MNIST(手書き数字の画像データセット)で手書き文字の予測を行いました。 実装したコード(iPython Notebook)はこちら(Github)をご確認下さい。 Kerasとは、Pythonで書かれ. However, Keras is used most often with TensorFlow. Note that the final layer has an output size of 10, corresponding to the 10 classes of digits. Keras is a high-level neural networks API, developed with a focus on enabling fast experimentation and not for final products. Supports arbitrary network architectures: multi-input or multi-output models, layer sharing, model sharing, etc. padding: int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints. From there, we create a one-shot iterator and a graph node corresponding to its get_next() method. Keras is a powerful and easy-to-use free open source Python library for developing and evaluating deep learning models. 0 Keras will be the default high-level API for building and training machine learning models, hence complete compatibility between a model defined using the old tf. instead of from keras. It uses the popular MNIST dataset to classify handwritten digits using a deep neural network (DNN) built using the Keras Python library running on top of TensorFlow. 5 I typed: conda create -n tf-keras python=3. 4正式添加了keras和data作为其核心代码(从contrib中毕业),加上之前的estimator API,现在已经可以利用Tensorflow像keras一样方便的搭建网络进行训练。. Keras is compact, easy to learn, high-level Python library run on top of TensorFlow framework. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. fully-connected layers). Create a new network with bottom layers taken from VGG. h5') Weights-only saving using TensorFlow checkpoints. Eventually, you will want. applications, I expected that to work easily. Comparing XOR between tensorflow and keras. If you are using the Keras API directly, then you will be required to change to the Keras API implemented in a TensorFlow environment. We use cookies for various purposes including analytics. GlobalAveragePooling2D() Convolutional neural networks detect the location of things. Installation of Keras with tensorflow at the backend. Like Keras, it also abstracts away much of the messy parts of programming deep networks. Fuzz parameter. I’ve heard good things about PyTorch too, though I’ve never had the chance to try it. The content of the local memory of the neuron consists of a vector of weights. Why does the Keras version have 32 nodes in the hidden layer. 1 and Theano 0. Listing 2 shows the implementation in Keras. In this post we will show how to use probabilistic layers in TensorFlow Probability (TFP) with Keras to build on that simple foundation, incrementally reasoning about progressively more. models import Model from keras. They are extracted from open source Python projects. TensorFlow tensorflow. Many machine learning models are expressible as the composition and stacking of relatively simple layers, and TensorFlow provides both a set of many common layers as a well as easy ways for you to write your own application-specific layers either from scratch or as the composition of existing layers. Keras + Tensorflow Guide •For CS 155, please use the Tensorflow backend when using Keras • Gives an overview of layers, parameters, input and output shape. ———- old answer ———- Hi, I am one of the contributors of TensorLayer [1]. You can then use this model for prediction or transfer learning. Image Classification is one of the fundamental supervised tasks in the world of machine learning. Here is a Keras model of GoogLeNet (a. Each layer in Keras will have an input shape and an output shape. TensorFlow is Python's most popular Deep Learning framework. Since Keras is just an API on top of TensorFlow I wanted to play with the underlying layer and therefore implemented image-style-transfer with TF. A few months ago I demonstrated how to install the Keras deep learning library with a Theano backend. layers will return a shallow copy version of the layers list, so actually you don't remove that layer, just remove the layer in the return value. Transformations warp and rotate the input space(I recommend looking up Chris Olah's blog). ゼロからKerasとTensorFlow(TF)を自由自在に動かせるようになる。 そのための、End to Endの作業ログ(備忘録)を残す。 ※環境はMacだが、他のOSでの汎用性を保つように意識。 ※アジャイルで執筆しており、精度を逐次高めていく. 1 and Theano 0. CIFAR-10 dataset. ちょうどあなたの定期的に高. The next layer in our Keras LSTM network is a dropout layer to prevent overfitting. A layer config is a Python dictionary (serializable) containing the configuration of a layer. TensorFlow includes the full Keras API in the tf. I have tried these codes,it worked. keras import layers Padding sequence data. It contains various types of layers that you may use in creating your NN model viz. Because Keras abstracts away a number of frameworks as backends, the models can be trained in any backend, including TensorFlow, CNTK, etc. keras to call it. For example, importKerasLayers(modelfile,'ImportWeights',true) imports the network layers and the weights from the model file modelfile. It uses the popular MNIST dataset to classify handwritten digits using a deep neural network (DNN) built using the Keras Python library running on top of TensorFlow. You can then train this model. layers import Dense from tensorflow. Word Embeddings with Keras. Keras and TensorFlow. I was wondering if there is any other way to write my own Keras layer instead of inheritance way as given in their documentation? My colleague is more comfortable in tensorflow and he gave me a tensorflow function that does the job of the layer. 8/ /usr/lib. Alternatively, you can import layer architecture as a Layer array or a LayerGraph object. By that same token, if you find example code that uses Keras, you can use with the TensorFlow version of Keras too. Sequential model. Keras Layers. Build a chatbot with Keras and TensorFlow. Integrating Keras with the API is easy and straight forward. Tensorflow is low level implementations of the algorithms and a low level API. Deep Computer Vision Using Convolutional Neural Networks. By that same token, if you find example code that uses Keras, you can use with the TensorFlow version of Keras too. from tensorflow. TensorFlow includes a special feature of image recognition and these images are stored in a specific folder. Keras negative sampling with custom layer. Any idea how do I solve this problem?. As mentioned in the introduction to this tutorial, there is a difference between multi-label and multi-output prediction. A fast-paced introduction to Deep Learning that starts with a simple yet complete neural network (no frameworks), followed by an overview of activation functions, cost functions, backpropagation, and then a quick dive into CNNs. 3, because that's broken too since it was made to be similar to tf. x: Vector, matrix, or array of training data (or list if the model has multiple inputs). input_layer. The output of the sigmoid at the last layer produces the fake image. In the first part of this tutorial, we are going to discuss the parameters to the Keras Conv2D class. TensorFlow has announced that they are incorporating the popular deep learning API, Keras, as part of the core code that ships with TensorFlow 1. Image captioning is a challenging task at intersection of vision and language. Regression with Probabilistic Layers in TensorFlow Probability. backend() != 'tensorflow': raise RuntimeError('This example can only run with the ' 'TensorFlow backend, ' 'because. implementation() Keras implementation. This function adds an independent layer for each time step in the recurrent model. Users will just instantiate a layer and then treat it as. Many machine learning models are expressible as the composition and stacking of relatively simple layers, and TensorFlow provides both a set of many common layers as a well as easy ways for you to write your own application-specific layers either from scratch or as the composition of existing layers. To use this with Keras, we make a dataset out of elements of the form (input batch, output batch). Writing your own Keras layers. The default sigmoid activation function is used for the LSTM blocks. More layers makes it easier for the network to learn especially when subsequent layers have more dimensions than the input space. js as well, but only in CPU mode. Read writing about Keras in TensorFlow. TensorFlow Lite for mobile and embedded devices For Production TensorFlow Extended for end-to-end ML components. However, we have set up compatibility interfaces so that your Keras 1 code will still run in Keras 2 without issues (while printing warnings to help you convert your layer calls to the new API). So you can just start using the Keras API at no loss of flexibility. layers will return a shallow copy version of the layers list, so actually you don't remove that layer, just remove the layer in the return value. Filters; Stacking Multiple Feature Maps; TensorFlow Implementation; Memory Requirements; Pooling Layers. You don't have to adopt all of Keras, you can just the layers you need. I was wondering if there is any other way to write my own Keras layer instead of inheritance way as given in their documentation? My colleague is more comfortable in tensorflow and he gave me a tensorflow function that does the job of the layer. convolutional layers, pooling layers, recurrent layers , embedding layers and more. This was a very hard problem before the rise of deep networks and especially Convolutional Neural Networks. layers import Convolution2D. The same layer can be reinstantiated later (without its trained weights) from this configuration. Warning: Saved Keras networks do not include classes. The Keras R interface uses the TensorFlow backend engine by default. is_keras_available() Check if Keras is Available. This layer has no parameters to learn; it only reformats the data. In this example we’ll use Keras to generate word embeddings for the Amazon Fine Foods Reviews dataset. Firstly, we reshaped our input and then split it into sequences of three symbols. Keras is a high-level neural network API written in Python and capable of running on top of Tensorflow, CNTK, or Theano. TensorFlow is an end-to-end open source platform for machine learning. A Comprehensive guide to Fine-tuning Deep Learning Models in Keras (Part I) October 3, 2016 In this post, I am going to give a comprehensive overview on the practice of fine-tuning, which is a common practice in Deep Learning. I've been trying to build a sequential model in Keras using the pooling layer tf. Updated Oct/2019: Updated for Keras 2. Keras doesn't handle low-level computation. Keras is effectively a simplified intuitive API built on top of Tensor Flow or Theano (you select the backend configuration). Defining neural networks is intuitive, where using the functional API allows one to define layers as functions. pyplot as plt: we will be drawing some plots to show some images. TensorFlow Lite for mobile and embedded devices For Production TensorFlow Extended for end-to-end ML components. Input() Input() is used to instantiate a Keras tensor. In this layer, I want to use some other keras layers. In Keras, the syntax is tf. For R users, there hasn’t been a production grade solution for deep learning (sorry MXNET). Traffic Sign Classification with Keras and Deep Learning. py:1140: UserWarning: Skipping loading of weights for layer conv2d_59 due to mismatch in shape ((1, 1, 1024, 54) vs (255, 1024, 1, 1)). keras, model. A keras attention layer that wraps RNN layers. We will assign the data into train and test sets. I have tried these codes,it worked. They are extracted from open source Python projects. Word embedding is a method used to map words of a vocabulary to dense vectors of real numbers where semantically similar words are mapped to nearby points. In this tutorial. If we were a newbie to all this deep learning and wanted to write a new model from scratch, then Keras is what I would suggest for its ease in both readability and writability. However, you motivated me enough to spend the time implementing the LRN layer with Tensorflow-compatible operations. Here we're going to be going over the Keras Functional API. fit(), making sure to pass both callbacks. The first tensor is the output. Welcome to part 4 of the deep learning basics with Python, TensorFlow, and Keras tutorial series. Moving to tf. These are handled by Network (one layer of abstraction above. Fine-tuning Convolutional Neural Network on own data using Keras Tensorflow. The network is trained for 100 epochs and a batch size of 1 is used. Alternatively, you can import layer architecture as a Layer array or a LayerGraph object. This should be input_1 and output_1 respectively, if you named your layers as I did in the previous codes. About six months ago, we showed how to create a custom wrapper to obtain uncertainty estimates from a Keras network. Thanks to all those that pointed out the issue, and to Philip O'Brien for helping to point out the fix. We used the Keras Sequential model API, which lets us build a linear stack of layers. layers import * It's ok to have each branch as a sequential model, but the fork must be in a Model. Essentially it represents the array of Keras Layers. We created two LSTM layers using BasicLSTMCell. It can be installed with:. max_pooling2d is a tensorflow 'native layer'. Normal functions are defined using the def keyword, in Python anonymous functions are defined using the lambda keyword. Transformations warp and rotate the input space(I recommend looking up Chris Olah's blog). Its components are then provided to the network's Input layer and the Model. Keras and in particular the keras R package allows to perform computations using also the GPU if the installation environment allows for it. Keras is an Open Source Neural Network library written in Python that runs on top of Theano or Tensorflow. This thread is a crash course on everything you need to know to use TensorFlow 2. Visit the TensorFlow website for all session recordings:. You are going to learn step by step how to freeze and convert your trained Keras model into a single TensorFlow pb file. The default sigmoid activation function is used for the LSTM blocks. The block diagram is given here for reference. (In the following example we use gradient descent. Keras is a neural network API that is written in Python. These operations require managing weights, losses, updates, and inter-layer connectivity. Keras automatically sets the input shape as the output shape from the previous layer, but for the first layer, you’ll need to set that as a parameter. So, unless you require that customisation or sophistication that comes with a lower level interface, Keras should be sufficient for your purposes. Different types models that can be built in R using Keras; Classifying MNIST handwritten digits using an MLP in R. models import Model, load_model, save_model, Sequential from keras. Building a convolutional neural network using Python, Tensorflow 2, and Keras. Here's what you'll do: Create the Keras TensorBoard callback to log basic metrics; Create a Keras LambdaCallback to log the confusion matrix at the end of every epoch; Train the model using Model. 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: