Torch Summary Pytorch

PyTorch was born from Lua Torch but they're not interchangeable and noting installation issues regarding Lua Torch when the article is discussing PyTorch is likely confusing. This book is an excellent entry point for those wanting to explore deep learning with PyTorch to harness its power. A critical component of fastai is the extraordinary foundation provided by PyTorch, v1 (preview) of which is also being released today. Keep playing around with other features supported with PyTorch TensorBoard. It lives between the torch wrapper and the FixedPrecisionTensor which indicates that the values are now in finite fields. Pytorch dynamic computation graph gif Pytorch or tensorflow - good overview on a category by category basis with the winner of each Tensor Flow sucks - a good comparison between pytorch and tensor flow What does google brain think of pytorch - most upvoted question on recent google brain Pytorch in five minutes - video by siraj I realised I like @pytorch because it's not a deeplearning. You can vote up the examples you like or vote down the ones you don't like. All other subpackages have code relevant to the inner workings of neurophox. Deep learning frameworks offer building blocks for designing, training and validating deep neural networks, through a high level programming interface. import torch from torch. Summary¶ The layer APIs of neurophox are provided via the subpackages: neurophox. And that is the beauty of Pytorch. you can't take a trained model. PyTorch is a relative newcomer to the deep learning framework set. The following are code examples for showing how to use torch. array (the NumPy array). jit_options – A dictionary contains optional arguments for torch. Tensor • To train a network: 1. PyTorch is my personal favourite neural network/deep learning library, because it gives the programmer both high level of abstraction for quick prototyping as well as a lot of control when you want to dig deeper. In this post, I implement the recent paper Adversarial Variational Bayes, in Pytorch. """ from __future__ import (absolute_import, division, print_function, unicode_literals) import copy import json import logging import os from io import open import six import torch from torch import nn from torch. Model summary in PyTorch similar to `model. PyTorch's website has a 60 min. What is tensorboard X? Create a summary writer. VGG16 model summary import torch from torchvision import models from torchsummary import summary device = torch. Now, we should have a good understanding of the torchvision module that is provided by PyTorch, and how we can use Datasets and DataLoaders in the PyTorch torch. AI & Deep Learning Training www. They are extracted from open source Python projects. Jan 6, 2019. parameter classes. We will use a subset of the CalTech256 dataset to classify images of 10 different kinds of animals. Detailed examples for each model architecture (Bert, GPT, GPT-2, Transformer-XL, XLNet and XLM) can be found in the full documentation. This is useful when having long-running ipython notebooks while sharing the GPU with other processes. omit_useless_nodes ( bool ) – Default to true , which eliminates unused nodes. The current buzz in data science and big data is around the promise of deep learning, especially when working with unstructured data. summary() in PyTorch. Summary and Further reading. Machine learning with py torch 1. simple audio I/O for pytorch 2019-07-30: torchvision-cpu: public: image and video datasets and models for torch deep learning 2019-05-24: pytorch-cpu: public: PyTorch is an optimized tensor library for deep learning using GPUs and CPUs. In this course, Natural Language Processing with PyTorch, you will gain the ability to design and implement complex text processing models using PyTorch, which is fast emerging as a popular choice for building deep-learning models owing to its flexibility, ease-of-use, and built-in support for optimized hardware such as GPUs. The purpose of this post was to show how to get up and running defining neural networks with pytorch. Read the official API document here - TORCH. astype(int)], dtype=torch. Granted that PyTorch and TensorFlow both heavily use the same CUDA/cuDNN components under the hood (with TF also having a billion other non-deep learning-centric components included), I think one of the primary reasons that PyTorch is getting such heavy adoption is that it is a Python library first and foremost. Summary of steps: Setup transformations for the data to be loaded. About PyTorchPyTorch is a Python-based scientific computing package for those who want a replacement for NumPy to use the power of GPUs, and a deep learning research platform that provides maximum flexibility and speed. With the 1. fastai isn't something that replaces and hides PyTorch's API, but instead is designed to expand and enhance it. It is also a foundational element for privacy-preserving techniques such as federated learning. - tensor creation methods in common_utils were updated to accept an optional dtype and device. Batch objects to each device. You can also mix pure pytorch and torchfusion layers in the same model. FileWriter. D:\pytorch\pytorch>set TORCH_LIB_DIR=D:/pytorch/pytorch/torch/lib. Module; Use functions in packages like torch. To create a dataset, I subclass Dataset and define a constructor, a __len__ method, and a __getitem__ method. bold[Marc Lelarge] --- # Supervised learning basics. A summary of core features: a powerful N-dimensional array. Next I pasted the URL into a browser and did a Save As when prompted by the browser. Based on the Torch library, PyTorch is an open-source machine learning library. Read the official API document here — TORCH. com: Pytorch Deep Learning By Example (9781096343585) by Benjamin Young and a great selection of similar New, Used and Collectible Books available now at great prices. TensorBoard is a visualization library for TensorFlow that is useful in understanding training runs, tensors, and graphs. It builds upon a few projects, most notably Lua Torch, Chainer, and HIPS Autograd, and provides a high performance environment with easy access to automatic differentiation of models executed on different devices (CPU and GPU). This summarizes some important APIs for the neural networks. Summary Pytoch is a quite powerful, flexible and yet popular deep learning framework. PyTorch Data loaders and abstractions for text and NLP 2019-08-08: pytorch-nightly-cpu: public: PyTorch is an optimized tensor library for deep learning using GPUs and CPUs. Summary PyTorch is better for rapid prototyping in research, for hobbyists and for small scale projects. Let me introduce my readers to the all new "TensorboardX" by pytorch. Under the hood - pytorch v1. Using the code snippets above, you can easily export your trained PyTorch and Keras models to TensorFlow. Let's directly dive in. Course Overview (Music) Hi, my name is Janani Ravi, and welcome to this course on Building Your First PyTorch solution. Future: neurophox. Pourquoi les gradients ne peuvent pas être mis sur le graphique et en demandant à l'utilisateur de zéro explicitement les dégradés? je cherche une bonne explication. Keras style model. device('cuda' if torch. A PyTorch tutorial implementing Bahdanau et al. The normality assumption is also perhaps somewhat constraining. input_to_model (torch. Can be a list, tuple, NumPy ndarray, scalar, and other types. distributed Detect and handle NCCL errors appropriately instead of blocking peers until timeout in ProcessGroupNCCL (25012, 25905) torch. summary() in here. Scalars, images, histograms, graphs, and embedding visualizations are all supported for PyTorch models and tensors as well as Caffe2 nets and blobs. In this course, Natural Language Processing with PyTorch, you will gain the ability to design and implement complex text processing models using PyTorch, which is fast emerging as a popular choice for building deep-learning models owing to its flexibility, ease-of-use, and built-in support for optimized hardware such as GPUs. PyTorch helpfully provides clear installation instructions for each platform and package manager at http://pytorch. News briefs for May 3, 2018. Summary: We train a neural network on encrypted values using Secure Multi-Party Computation and Autograd. Summary of steps: Setup transformations for the data to be loaded. D:\pytorch\pytorch>set TORCH_LIB_DIR=D:/pytorch/pytorch/torch/lib. Read the official API document here - TORCH. 0, TensorBoard was experimentally supported in PyTorch, and with PyTorch 1. summary()` in Keras Skip to main content Switch to mobile version Warning Some features may not work without JavaScript. to(device) summary(vgg, ( 3 , 224 , 224 )) # will display the summary of the model. Pytorch is one of the most powerful Artificial Intelligence and Deep Learning framework in the World. AI & Deep Learning Training www. 2019-08-07: cpuonly: public: No Summary 2019-08-06: magma-cuda92: public: No Summary 2019-08-05: magma-cuda101: public: No Summary 2019-08-05: magma-cuda100: public: No. Build neural network models in text, vision and advanced analytics using PyTorch. Build neural network models in text, vision and advanced analytics using PyTorch Deep learning powers the most intelligent systems in the world, such as Google Voice, Siri, and Alexa. from torch. input_to_model (torch. Copy the command at the end of the table and run it in your terminal. The next fast. 0 and torchvision. Additionally, it provides many utilities for efficient serializing of Tensors and arbitrary types, and other useful utilities. astype(int)], dtype=torch. Deep Learning Tutorial Lessons Use the PyTorch Tensor operation (torch. inits import reset, uniform EPS = 1e-15 [docs] class DeepGraphInfomax ( torch. Making neural nets uncool again. Use PyTorch:- If you want to create of dynamic computation graphs (DCG). One of the most popular frameworks for building deep learning applications is PyTorch, in large part because of their focus on ease of use. The system we have defined in this chapter is a very common/general approach followed by different sorts of companies, with slight changes. We report good results on MNIST. Module class. functional as F import torch. import torch import torch. A much more complete summary is here , but suffice to say puts a lot of new technology within reach for most anyone with some python familiarity. , but seems like, I have no option left apart from moving to other tools. Here is a barebone code to try and mimic the same in PyTorch. 설치: pip install tensorboardX tensorboardX를 사용하기 위해선 tensorboard가 필요하며, tensorboard는 tensorflow가 필요하다. PyTorch is a Python package that provides two high-level features: Tensor computation (like NumPy) with strong GPU acceleration Deep neural networks built on a tape-based autograd system PyTorch is an upcoming competitor to Google's TensorFlow and gains much popularity at the moment, as e. Tensor or list of torch. With this "convention over configuration" approach the location of the graph is always known and variables aren't defined all over in the rest of the code. Awni Hannun, Stanford. pytorch-summaryを使うとKerasのmodel. Use isinstance () or x. functional. Now, we should have a good understanding of the torchvision module that is provided by PyTorch, and how we can use Datasets and DataLoaders in the PyTorch torch. TensorBoard相关资料. Keep playing around with other features supported with PyTorch TensorBoard. The example here is motivated from pytorch examples. SUMMARY Keras – more deployment options (directly and through the TensorFlow backend), easier model export. Thanks! I confirm that this works: 1. Step 2 is a summary of official tutorial by PyTorch. backward() 3. AI & Deep Learning Training www. Awni Hannun, Stanford. During last year (2018) a lot of great stuff happened in the field of Deep Learning. VGG16 model summary import torch from torchvision import models from torchsummary import summary device = torch. So, let's summarize everything! The first factor is PyTorch is a growing deep learning framework for beginners or for research purpose. 1 Autograd mechanics 3. Torch is a framework maintained by various contributors from DeepMind, Facebook, Twitter and others that utilises dynamic graphs. nn import CrossEntropyLoss from torch. A forward() function gets called when the Graph is run. device('cuda' if torch. The aim is to provide information complementary to, what i…. zero_grads() before. yunjey的 pytorch tutorial系列. Read writing about Pytorch in Becoming Human: Artificial Intelligence Magazine. module import _addindent import torch import numpy as np def torch_summarize. The first part of the workshop will be an introduction into the dynamic deep learning library PyTorch. The framework is open source and enjoys a strong community (see fast. 0, TensorBoard was experimentally supported in PyTorch, and with PyTorch 1. 0, it is no longer experimental. You have also learnt how to implement them in the awesome PyTorch deep learning framework - a framework which, in my view, has a big future. One thing that can become annoying when working with einsum is that you have to instantiate parameters manually, take care of their initialization, and registering them with modules. Welcome to tensorboardX's documentation!¶ Contents: tensorboardX; Helper functions; Tutorials. Parameters: indices (array_like) - Initial data for the tensor. Let's do a very quick overview of PyTorch-Transformers. rand ( 2 , 2 ). This tutorial helps NumPy or TensorFlow users to pick up PyTorch quickly. This course will introduce the PyTorch workflow and demonstrate how to use it. One of the most popular frameworks for building deep learning applications is PyTorch, in large part because of their focus on ease of use. prev_inner = torch. Beware, the accuracy reported here is not always representative of the transferable capacity of the network on other tasks and datasets. 0 -c pytorch. I think that the machine learning community is one of the most amazing sharing communities around, and a large part of the reason things are progressing as quickly as they are is that researchers actually provide source, upon which others can build and compare (as you did with Klein's code). I hope it was useful - have fun in your deep learning journey!. bool masks rather than torch. Copy the command at the end of the table and run it in your terminal. Keras or PyTorch as your first deep learning framework. TENSORBOARD. About This Book. PyTorch is grabbing the attention of deep learning researchers and data science practitioners due to its simplicity of use, accessibility, efficiency, and being more native to Python way of development. is_available() else 'cpu') vgg = models. Despite this, it is now being used extensively by Google, Twitter, and Facebook. PyTorch 番外篇:Pytorch中的TensorBoard(TensorBoard in PyTorch) 参考代码. nn import init from import function as fn # pylint: disable=W0235. 0 in which - NVIDIA used their winning MLPerf competition techniques to make the model 4 times faster, - @rodgzilla added a multiple-choice model & how to fine-tune it on SWAG + many others!. At line 63:. pourquoi nous devons faire des gradients zéro explicitement dans pytorch. simple audio I/O for pytorch 2019-07-30: torchvision-cpu: public: image and video datasets and models for torch deep learning 2019-05-24: pytorch-cpu: public: PyTorch is an optimized tensor library for deep learning using GPUs and CPUs. Mmdnn ⭐ 4,134 MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. PyTorch official cheatsheat. The following table provides summary statistics for permanent job vacancies advertised in Yorkshire with a requirement for PyTorch skills. PyTorch is an awesome alternative to TensorFlow. 2018/04/30 - [Machine Learning/PyTorch] - PyTorch 튜토리얼 1 - PyTorch란?. PyTorch takes advantage of the power of Graphical Processing Units (GPUs) to make implementing a deep neural network faster than training a network on a CPU. " Though the old name still works, there is a short summary here about migrating to the new library. The following are code examples for showing how to use torch. import torch torch. The current buzz in data science and big data is around the promise of deep learning, especially when working with unstructured data. pytorch调试过程中遇见的问题及解决方法 [问题点数:20分]. Torch has (more) Loading…. Module class. Pytorch Deep Learning By Example [Benjamin Young] on Amazon. Installing PyTorch in Container Station Assign GPUs to Container Station. Uninstall Miniconda 2. yunjey的 pytorch tutorial系列. Note: the Pytorch version of ResNet152 is not a porting of the Torch7 but has been retrained by facebook. emptyCache() frees the cached memory blocks in PyTorch’s caching allocator. Pytorch, Caffe, Torch, MxNet, Tensorflow Skills in big data analysis. PyTorch Documentation. It might be a linear transformation, convolution, softmax activation etc. If you are close to Munich (say, day-travel-close), and want to learn PyTorch / AI for your own projects from one of the leading PyTorch experts, check out my workshop offering. The webinar was called out by Andrej Karpathy himself in a blog post where he covers a detailed methodology for training neural networks. Module class. GitHub Gist: instantly share code, notes, and snippets. And that is the beauty of Pytorch. If you want to build custom layers or do some heavy sequence2sequence models, I think the framework will be there very soon (with documentation / test cases to describe best practices). Moreover, einsum in PyTorch currently does not support diagonal elements, so the following throws an error: torch. AI & Deep Learning Training www. We can efficiently run a part of the code and inspect it in real-time. The following are code examples for showing how to use torch. functional. It's a small model with around 15 layers of 3D convolutions. Setup network to train. If you're not sure which to choose, learn more about installing packages. BERT, XLNet) implemented in PyTorch. This is not a full listing of APIs. Tags: deep learning, tutorial. Alternatively, we can learn the basics from the greats and focus on greater challenges. nn as nn class ScaledDotProductAttention (PyTorch已经实现啦. PyTorch is an open source deep learning platform with a rich ecosystem that enables seamless integration from research prototyping to production deployment. Facial recognition is a biometric solution that measures. A forward() function gets called when the Graph is run. Introduction In recent years, richly structured probabilistic models have demonstrated promising results on a number of fundamental problems in AI (Ghahramani (2015)). PyTorch takes advantage of the power of Graphical Processing Units (GPUs) to make implementing a deep neural network faster than training a network on a CPU. uint8 Tensors is now deprecated in favor of masking via torch. summary() in here. PyTorch is different from other deep learning frameworks in that it uses dynamic computation graphs. Size([1, 6, 24, 24]) The batch size is still 1. BERT, XLNet) implemented in PyTorch. class: center, middle, title-slide count: false # Regressions, Classification and PyTorch Basics. Please note in all cases, the name of the module to import is torch, not pytorch. However, PyTorch is not a simple set of wrappers to support popular language, it was rewritten and tailored to be fast and feel native. autograd , torch. Download the file for your platform. Let us discuss what we need to do with our dataset. This is useful when having long-running ipython notebooks while sharing the GPU with other processes. PyTorch is an open source deep learning platform with a rich ecosystem that enables seamless integration from research prototyping to production deployment. Based on the Torch library, PyTorch is an open-source machine learning library. TensorBoard相关资料. Let's do a very quick overview of PyTorch-Transformers. ai - Aug 16, 2019. For NCF task, despite the fact that there is no significant difference between all three frameworks, PyTorch is still a better choice as it has a higher inference speed when GPU is the main concerning point. Using the code snippets above, you can easily export your trained PyTorch and Keras models to TensorFlow. A forward() function gets called when the Graph is run. It has a good community and documentation. import torch from pytorch_transformers import * # PyTorch-Transformers has a unified API # for 7 transformer architectures and 30 pretrained weights. Deep Learning with PyTorch Eli Stevens and Luca Antiga Look inside. Summary: Generally, for just the forward pass, Torch > Theano > TensorFlow. Distiller提供了一个PyTorch环境,用于对压缩算法进行 python优先的端到端深度学习平台 Pytorch中文文档 Torch中文文档 Pytorch视频教程 Matplotlib中文文档 OpenCV-Python中文文档 pytorch0. requires_grad_ () to set the requires_grad flag. Detailed examples for each model architecture (Bert, GPT, GPT-2, Transformer-XL, XLNet and XLM) can be found in the full documentation. __version__. Exporting PyTorch models is more taxing due to its Python code, and currently the widely recommended approach is to start by translating your PyTorch model to Caffe2 using ONNX. PyTorch Data loaders and abstractions for text and NLP 2019-08-08: pytorch-nightly-cpu: public: PyTorch is an optimized tensor library for deep learning using GPUs and CPUs. The framework is open source and enjoys a strong community (see fast. device) self. summary() in PyTorch model. module calls pyro. import torch import torch. This makes sense because we wouldn’t expect our batch size to change, and this is going to be the case through the entire forward pass. 0, it is no longer experimental. However, given the way these objects are defined in PyTorch, this would enforce to use exactly the same transforms for both the training and validation sets which is too constraining (think about adding dataset transformation to the training set and not the validation set). Take note that these notebooks are slightly different from the videos as it's updated to be compatible to PyTorch 0. summary()` in Keras. Module; Use functions in packages like torch. Convolution layers nn. In PyTorch, Tensor is the primary object that we deal with (Variable is just a thin wrapper class for Tensor). Note: as of July 2019, the pytorch library called "pytorch-pretrained-bert pytorch-nlp" has been updated and renamed as "pytorch-transformers. init 179 12 torch. tensor) to convert a Python list object into a PyTorch Tensor (tf. Based on the Torch library, PyTorch is an open-source machine learning library. These methods take a layer and decompose it into several smaller layers. ", "A common PyTorch convention is to save models using either a _. Under the hood - pytorch v1. Keras style model. Note: the Pytorch version of ResNet152 is not a porting of the Torch7 but has been retrained by facebook. Some of these notable features include: torch. Some machine learning experiment starter code with neurophox can be found in neurophox-notebooksand/or neurophox. Advancements in powerful hardware, such as GPUs, software frameworks such as PyTorch, Keras, Tensorflow, and CNTK. 2 now officially supports TensorBoard, a function which is no longer experimental and can be activated by simply typing "from torch. Interactive deep learning with Jupyter, Docker and PyTorch on the Data Science Virtual Machine - Learn | Microsoft Docs. 설치: pip install tensorboardX tensorboardX를 사용하기 위해선 tensorboard가 필요하며, tensorboard는 tensorflow가 필요하다. 基本数据类型和基本运算1. pytorch调试过程中遇见的问题及解决方法 [问题点数:20分]. Add an in-place method. Here is a barebone code to try and mimic the same in PyTorch…. The current buzz in data science and big data is around the promise of deep learning, especially when working with unstructured data. Automatic Differentiation in PyTorch Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga. However, given the way these objects are defined in PyTorch, this would enforce to use exactly the same transforms for both the training and validation sets which is too constraining (think about adding dataset transformation to the training set and not the validation set). A very personal story that is both funny and poignant, TORCH SONG TRILOGY chronicles a New Yorker's search for love, respect and tradition in a world that seems not. 3, PyTorch supports an end-to-end workflow from Python to deployment on iOS and Android. For NCF task, despite the fact that there is no significant difference between all three frameworks, PyTorch is still a better choice as it has a higher inference speed when GPU is the main concerning point. """ # pylint: disable= no-member, arguments-differ, invalid-name import torch as th from torch import nn from torch. So, you want to learn deep learning? Whether you want to start applying it to your business, base your next side project on it, or simply gain marketable skills - picking the right deep learning framework to learn is the essential first step towards reaching your goal. zip Download. I've answered this general question several times. shape torch. I am a research scientist in Visa Research focus on security analysis and adversarial learning in biometrics. Summary¶ The layer APIs of neurophox are provided via the subpackages: neurophox. Note: the Pytorch version of ResNet152 is not a porting of the Torch7 but has been retrained by facebook. In any case, PyTorch requires the data set to be transformed into a tensor so it can be consumed in the training and testing of the network. You have also learnt how to implement them in the awesome PyTorch deep learning framework - a framework which, in my view, has a big future. Summary¶ In this post, I have briefly introduced Neural Processes, provided a PyTorch implementation, and provided some examples of undesirable behaviour. You can vote up the examples you like or vote down the ones you don't like. In its essence though, it is simply a multi-dimensional matrix. The normality assumption is also perhaps somewhat constraining. set_printoptions(threshold=10000). A summary of core features: a powerful N-dimensional array; lots of routines for indexing, slicing, transposing, …. GitHub Gist: instantly share code, notes, and snippets. jit_compile – Optional parameter denoting whether to use the PyTorch JIT to trace the log density computation, and use this optimized executable trace in the integrator. Keep playing around with other features supported with PyTorch TensorBoard. With the 1. PyTorch Mobile Running ML on edge devices is growing in importance as applications continue to demand lower latency. data package to streamline ETL tasks. $ conda install pytorch torchvision cudatoolkit=9. PyTorch has seen increasing popularity with deep learning researchers thanks to its speed and flexibility. PyTorch is a machine learning framework with a strong focus on deep neural networks. autograd191 14 Multiprocessing package - torch. configuration_utils import. Everything in PyTorch is tensor. First, you will learn how the load_state_dict and the torch. param on every parameter of a torch. 第五步 阅读源代码 fork pytorch,pytorch-vision等。相比其他框架,pytorch代码量不大,而且抽象层次没有那么多,很容易读懂的。通过阅读代码可以了解函数和类的机制,此外它的很多函数,模型,模块的实现方法都如教科书般经典。. Torch Browser includes media downloading, torrents, and social media and sharing. The official documentation is located here. Focused Forum for AI & ML - PyTorch. Because it emphasizes GPU-based acceleration, PyTorch performs exceptionally well on readily-available hardware and scales easily to larger systems. Can be a list, tuple, NumPy ndarray, scalar, and other types. It is free and open-source software released under the Modified BSD license. TensorBoard is a visualization library for TensorFlow that is useful in understanding training runs, tensors, and graphs. linear_model import LogisticRegression from. torchsummaryに関する情報が集まっています。現在1件の記事があります。また0人のユーザーがtorchsummaryタグをフォローしています。. This seems to be an issue with TH and not pytorch, the same thing happens in lua torch. init 179 12 torch. It is a whole different field and we have just touched on this topic in this chapter. A PyTorch tensor is a specific data type used in PyTorch for all of the various data and weight operations within the network. 2019-08-07: cpuonly: public: No Summary 2019-08-06: magma-cuda92: public: No Summary 2019-08-05: magma-cuda101: public: No Summary 2019-08-05: magma-cuda100: public: No. Here we recommand using conda. Despite this, it is now being used extensively by Google, Twitter, and Facebook. PyTorch has seen increasing popularity with deep learning researchers thanks to its speed and flexibility. Create a new env for PyTorch 4. Hi, I’m Riza 3. 0 and torchvision. Since PyTorch is still in Beta, I expect some more changes and improvements to the usability, docs and performance. This really short tutorial gets you to start with running TensorBoard with latest Pytorch 1. A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. In this course, Natural Language Processing with PyTorch, you will gain the ability to design and implement complex text processing models using PyTorch, which is fast emerging as a popular choice for building deep-learning models owing to its flexibility, ease-of-use, and built-in support for optimized hardware such as GPUs. IntTensor([0,1]) b = torch. TENSORBOARD. PyTorch sells itself on three different features: A simple, easy-to-use interface. 0 AI framework. Actually, There is already python module about model. Model summary in PyTorch similar to `model. VGG16 model summary import torch from torchvision import models from torchsummary import summary device = torch. tensorboard import SummaryWriter. 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: