1632379879
Learn Wide Residual Networks with Python
Wide Residual Networks
This code was used for experiments with Wide Residual Networks (BMVC 2016) http://arxiv.org/abs/1605.07146 by Sergey Zagoruyko and Nikos Komodakis.
Deep residual networks were shown to be able to scale up to thousands of layers and still have improving performance. However, each fraction of a percent of improved accuracy costs nearly doubling the number of layers, and so training very deep residual networks has a problem of diminishing feature reuse, which makes these networks very slow to train.
To tackle these problems, in this work we conduct a detailed experimental study on the architecture of ResNet blocks, based on which we propose a novel architecture where we decrease depth and increase width of residual networks. We call the resulting network structures wide residual networks (WRNs) and show that these are far superior over their commonly used thin and very deep counterparts.
For example, we demonstrate that even a simple 16-layer-deep wide residual network outperforms in accuracy and efficiency all previous deep residual networks, including thousand-layer-deep networks. We further show that WRNs achieve incredibly good results (e.g., achieving new state-of-the-art results on CIFAR-10, CIFAR-100, SVHN, COCO and substantial improvements on ImageNet) and train several times faster than pre-activation ResNets.
Update (August 2019): Pretrained ImageNet WRN models are available in torchvision 0.4 and PyTorch Hub, e.g. loading WRN-50-2:
model = torch.hub.load('pytorch/vision', 'wide_resnet50_2', pretrained=True)
Update (November 2016): We updated the paper with ImageNet, COCO and meanstd preprocessing CIFAR results. If you're comparing your method against WRN, please report correct preprocessing numbers because they give substantially different results.
tldr; ImageNet WRN-50-2-bottleneck (ResNet-50 with wider inner bottleneck 3x3 convolution) is significantly faster than ResNet-152 and has better accuracy; on CIFAR meanstd preprocessing (as in fb.resnet.torch) gives better results than ZCA whitening; on COCO wide ResNet with 34 layers outperforms even Inception-v4-based Fast-RCNN model in single model performance.
Test error (%, flip/translation augmentation, meanstd normalization, median of 5 runs) on CIFAR:
| Network | CIFAR-10 | CIFAR-100 |
|---|---|---|
| pre-ResNet-164 | 5.46 | 24.33 |
| pre-ResNet-1001 | 4.92 | 22.71 |
| WRN-28-10 | 4.00 | 19.25 |
| WRN-28-10-dropout | 3.89 | 18.85 |
Single-time runs (meanstd normalization):
| Dataset | network | test perf. |
|---|---|---|
| CIFAR-10 | WRN-40-10-dropout | 3.8% |
| CIFAR-100 | WRN-40-10-dropout | 18.3% |
| SVHN | WRN-16-8-dropout | 1.54% |
| ImageNet (single crop) | WRN-50-2-bottleneck | 21.9% top-1, 5.79% top-5 |
| COCO-val5k (single model) | WRN-34-2 | 36 mAP |
See http://arxiv.org/abs/1605.07146 for details.
bibtex:
@INPROCEEDINGS{Zagoruyko2016WRN,
author = {Sergey Zagoruyko and Nikos Komodakis},
title = {Wide Residual Networks},
booktitle = {BMVC},
year = {2016}}
Pretrained models
ImageNet
WRN-50-2-bottleneck (wider bottleneck), see pretrained for details
Download (263MB): https://yadi.sk/d/-8AWymOPyVZns
There are also PyTorch and Tensorflow model definitions with pretrained weights at https://github.com/szagoruyko/functional-zoo/blob/master/wide-resnet-50-2-export.ipynb
COCO
Coming
Installation
The code depends on Torch http://torch.ch. Follow instructions here and run:
luarocks install torchnet
luarocks install optnet
luarocks install iterm
For visualizing training curves we used ipython notebook with pandas and bokeh.
Usage
Dataset support
The code supports loading simple datasets in torch format. We provide the following:
- MNIST data preparation script
- CIFAR-10 [recommended] data preparation script, preprocessed data (176MB)
- CIFAR-10 whitened (using pylearn2) preprocessed dataset
- CIFAR-100 [recommended] data preparation script, preprocessed data (176MB)
- CIFAR-100 whitened (using pylearn2) preprocessed dataset
- SVHN data preparation script
To whiten CIFAR-10 and CIFAR-100 we used the following scripts https://github.com/lisa-lab/pylearn2/blob/master/pylearn2/scripts/datasets/make_cifar10_gcn_whitened.py and then converted to torch using https://gist.github.com/szagoruyko/ad2977e4b8dceb64c68ea07f6abf397b and npy to torch converter https://github.com/htwaijry/npy4th.
We are running ImageNet experiments and will update the paper and this repo soon.
Training
We provide several scripts for reproducing results in the paper. Below are several examples.
model=wide-resnet widen_factor=4 depth=40 ./scripts/train_cifar.sh
This will train WRN-40-4 on CIFAR-10 whitened (supposed to be in datasets folder). This network achieves about the same accuracy as ResNet-1001 and trains in 6 hours on a single Titan X. Log is saved to logs/wide-resnet_$RANDOM$RANDOM folder with json entries for each epoch and can be visualized with itorch/ipython later.
For reference we provide logs for this experiment and ipython notebook to visualize the results. After running it you should see these training curves:
Another example:
model=wide-resnet widen_factor=10 depth=28 dropout=0.3 dataset=./datasets/cifar100_whitened.t7 ./scripts/train_cifar.sh
This network achieves 20.0% error on CIFAR-100 in about a day on a single Titan X.
Multi-GPU is supported with nGPU=n parameter.
Other models
Additional models in this repo:
- NIN (7.4% on CIFAR-10 whitened)
- VGG (modified from cifar.torch, 6.3% on CIFAR-10 whitened)
- pre-activation ResNet (from https://github.com/KaimingHe/resnet-1k-layers)
Implementation details
The code evolved from https://github.com/szagoruyko/cifar.torch. To reduce memory usage we use @fmassa's optimize-net, which automatically shares output and gradient tensors between modules. This keeps memory usage below 4 Gb even for our best networks. Also, it can generate network graph plots as the one for WRN-16-2 in the end of this page.
Acknowledgements
We thank startup company VisionLabs and Eugenio Culurciello for giving us access to their clusters, without them ImageNet experiments wouldn't be possible. We also thank Adam Lerer and Sam Gross for helpful discussions. Work supported by EC project FP7-ICT-611145 ROBOSPECT.
Download Details:
Author: szagoruyko
Download Link: Download The Source Code
Official Website: https://github.com/szagoruyko/wide-residual-networks
License: BSD-2-Clause License
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1625843760
Python Packages in SQL Server – Get Started with SQL Server Machine Learning Services
Introduction
When installing Machine Learning Services in SQL Server by default few Python Packages are installed. In this article, we will have a look on how to get those installed python package information.
Python Packages
When we choose Python as Machine Learning Service during installation, the following packages are installed in SQL Server,
- revoscalepy – This Microsoft Python package is used for remote compute contexts, streaming, parallel execution of rx functions for data import and transformation, modeling, visualization, and analysis.
- microsoftml – This is another Microsoft Python package which adds machine learning algorithms in Python.
- Anaconda 4.2 – Anaconda is an opensource Python package
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Lambda, Map, Filter functions in python
Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.
Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is
Syntax: x = lambda arguments : expression
Now i will show you some python lambda function examples:
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1624291780
Learn Python - Full Course for Beginners [Tutorial]
This course will give you a full introduction into all of the core concepts in python. Follow along with the videos and you’ll be a python programmer in no time!
⭐️ Contents ⭐
⌨️ (0:00) Introduction
⌨️ (1:45) Installing Python & PyCharm
⌨️ (6:40) Setup & Hello World
⌨️ (10:23) Drawing a Shape
⌨️ (15:06) Variables & Data Types
⌨️ (27:03) Working With Strings
⌨️ (38:18) Working With Numbers
⌨️ (48:26) Getting Input From Users
⌨️ (52:37) Building a Basic Calculator
⌨️ (58:27) Mad Libs Game
⌨️ (1:03:10) Lists
⌨️ (1:10:44) List Functions
⌨️ (1:18:57) Tuples
⌨️ (1:24:15) Functions
⌨️ (1:34:11) Return Statement
⌨️ (1:40:06) If Statements
⌨️ (1:54:07) If Statements & Comparisons
⌨️ (2:00:37) Building a better Calculator
⌨️ (2:07:17) Dictionaries
⌨️ (2:14:13) While Loop
⌨️ (2:20:21) Building a Guessing Game
⌨️ (2:32:44) For Loops
⌨️ (2:41:20) Exponent Function
⌨️ (2:47:13) 2D Lists & Nested Loops
⌨️ (2:52:41) Building a Translator
⌨️ (3:00:18) Comments
⌨️ (3:04:17) Try / Except
⌨️ (3:12:41) Reading Files
⌨️ (3:21:26) Writing to Files
⌨️ (3:28:13) Modules & Pip
⌨️ (3:43:56) Classes & Objects
⌨️ (3:57:37) Building a Multiple Choice Quiz
⌨️ (4:08:28) Object Functions
⌨️ (4:12:37) Inheritance
⌨️ (4:20:43) Python Interpreter
📺 The video in this post was made by freeCodeCamp.org
The origin of the article: https://www.youtube.com/watch?v=rfscVS0vtbw&list=PLWKjhJtqVAblfum5WiQblKPwIbqYXkDoC&index=3
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Why use Python for Software Development
No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas.
By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities.
Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly.
5 Reasons to Utilize Python for Programming Web Apps
Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.
Robust frameworks
Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions.
Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events.
Simple to read and compose
Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building.
The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties.
Utilized by the best
Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player.
Massive community support
Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions.
Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking.
Progressive applications
Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.
The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.
Summary
Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential.
The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.
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1602968400
Python Tricks Every Developer Should Know
Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?
In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.
Let’s get started
Swapping value in Python
Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead
>>> FirstName = "kalebu"
>>> LastName = "Jordan"
>>> FirstName, LastName = LastName, FirstName
>>> print(FirstName, LastName)
('Jordan', 'kalebu')
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