TensorFlow: Advances in Machine Learning by Kemal and Others
Author’s brief introduction: a program with a virgo, domestic Internet circles, a prominent blogger, excellent creators in the field of artificial intelligence, the world’s largest IT community CSDN blog in Chinese, Chinese experts on the open source community, huawei community experts, the Denver nuggets, 51 cto community experts, CSDN developers ecological alliance members, was invited to interview and review ten times. In particular, CSDN platform has been awarded the top four of CSDN Blog Star in 2018, the top six of CSDN Blog Star in 2019 and the top six of CSDN Blogger Star in 2020. In 2020, the weekly ranking remained no.1 in China throughout the year, with 24 million blog views and more than 500,000 followers on CSDN alone in China. Domestic major Internet platforms accumulated more than 1 million fans.
TensorFlow is the next generation of Web application machine learning using Tensorflow.js. Tensorflow.js is the next generation of Web application machine learning. Easy cross-platform computer vision with Model Maker, easy deployment of TF Lite models to web pages; Use TensorFlow Cloud to train Cloud TensorFlow model, etc.
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The history of TensorFlow
TensorFlow of actual combat
1. Build a neural network
2. Build and generate adversarial network
(1) Create a model
(2) Understand Data — Know Your Data
(3) Training model
(4) Deployment model
(5) Analyze and optimize the model
3, TensorFlow Lite
4, TensorFlow Extended1.0
Vertex AI platform: a new hosted ML platform on Google Cloud
TensorFlow Forum BBS
The history of TensorFlow
TensorFlow has been open source for almost five years, and to date, TensorFlow has been downloaded more than 200 million times, tutorials and tutorials have been viewed 52 million times, blog posts have been read more than 9 million times, YouTube has been viewed 10 million times, TensorFlow has evolved into a vast ecosystem of tools and frameworks for developers.
TensorFlow of actual combat
The Sequential API is very easy to use. From data to deployment, it is a set of tools that span developer workflows, which can also be interpreted as a platform for end-to-end processing of these workflows.
1. Build a neural network
Build a neural network with just a few lines of code!
2. Build and generate adversarial network
Keras can help us implement these advanced use cases using the same coding techniques, but they are not unique. Numpy can also be built. In TensorFlow 2.5, TF.Numpy combines the simple features of the Numpy API, Both gpus and TPUS can be used with only acceleration, both of which are freely available on the Web in tools like Colab.
TensorFlow is an ecosystem whose goal is to make ML easier at every stage. From creating models, analyzing models, deploying and running them, it is a life-cycle tool that remains reliable at the cutting edge of AI.
(1) Create a model
The TensorFlow Hub provides you with a repository of over 10,000 pre-trained models that you can reuse and build and test in a browser. For example, if you have a model trained to recognize birds, you can drag the image in your browser to test the model, and it will show you what kind of bird it is.
(2) Understand Data — Know Your Data
A new Data set exploration tool, Know Your Data, is a Web-based tool that helps you understand rich Data sets, such as images and text, to initially discover potential biases or imbalances. Know Your Data (KYD) is a new tool that helps ML researchers and product teams understand rich Data sets (images and text) to improve Data and model quality while displaying and mitigating fairness and bias issues.
(3) Training model
The next step is to load the data and define the characteristics and tags. TensorFlow Lite Model Maker can automate these functions, simplifying the process of training and retraining models using defined data. If you need to transfer learning, with Model Maker, you only need a few lines of code!
(4) Deployment model
To deploy the model on mobile, you can use TensorFlow Lite; To deploy the model on the Web side, you can use tensorflow.js. TensorFlow Cloud implements a Cloud deployment model that allows training to be delivered to a scalable Cloud infrastructure without leaving the terminal. As a Python package, it provides apis for seamless transitions from native debugging to distributed training and parameter tuning on the TensorFlow Cloud. For example, code examples based on simple image classification models on CIFAR datasets typically use Keras Model.pile and Model.fit. If you want to run the same code on a larger data set, the AI Platform on the cloud can add just a few lines of code and train the model seamlessly. If you want to get results quickly by distributing on multiple accelerators during training, you only need to configure accelerators, work quantity and distribution strategy, which is automatically set for more than 10 T4 Gpus.
(5) Analyze and optimize the model
There are two main tools to analyze models, namely TensorBoard and TensorFlow Profiler. TensorBoard is a visual toolkit for understanding experiments, tracking metrics, visualizing models, exploring model parameters, embedding, and more. The TensorFlow Profiler analyzes the execution of TensorFlow code.
The tool for Model Optimization is the Model Optimization Toolkit, which is a set of tools and techniques for optimizing models to run smaller and faster, such as quantization and pruning, with minimal loss of accuracy.
Another tool is TensorFlow Lite, which is used to deploy the TensorFlow model on mobile. It now includes built-in support for Systrace and integrates seamlessly with Perfetto in Android Studio.
3, TensorFlow Lite
TensorFlow Lite is an open source machine learning framework for model reasoning on devices, currently used by over 4 billion devices, using TensorFlow Lite Micro to touch billions of microcontrollers and embedded systems. TensorFlow Lite for microcontrollers helps you run ML models on microcontrollers and other devices with only a few K bytes of memory. You can now buy a pre-stored Arduino development board that connects to your browser via Bluetooth. You can use these development boards to try new Experiments With Google so you can make gestures, even create your own classifier, and run your own custom TensorFlow model. If developers are up to the challenge, we are also up to the challenge of running the new TensorFlow Lite for microprocessors. Tensorflow.js, a JavaScript machine learning library that allows developers to deploy machine learning in browsers and node.js backends, is growing at a robust rate of three times a year.
Developers can deploy machine learning on the Web using Tensorflow.js. Machine learning based on microcontrollers and embedded systems has great transformative potential.
4, TensorFlow Extended1.0
TFX 1.0 is the official enterprise-level ML. It is suitable for producing ML on an enterprise scale. Google created TFX because we needed to build a powerful framework for ML products and services, and then open source it for others to use. This includes support for training models for mobile and Web applications, as well as server-based applications. After a successful Beta release with a number of partners, Google announced TFX 1.0, which is ready for the official enterprise release ML. TFX1.0 goes beyond TesnsorFlow and includes other frameworks that you might use to train models. It includes everything you need for a production framework, such as enterprise-level support, security patches, bug fixes, and guaranteed backward compatibility. It also has a steady release pace and strong support for running well on the Google Cloud, as well as support for mobile, Web and NLP applications.
Vertex AI platform: a new hosted ML platform on Google Cloud
ML models are only valuable if you actually put them into production. As you know, it can be challenging to produce efficiently and on a large scale. For this reason, Google Cloud has released Vertex AI. This is a new hosted machine learning platform that helps you experiment and deploy AI models faster. Vertex AI’s tools involve all phases of a developer’s workflow, from tagging data to using notebooks and models, to prediction tools and continuous monitoring, all in one interface. While you may already be familiar with much of this, what really sets Vertex AI apart is the introduction of new MLOps capabilities. You can now use our MLOps tools, such as Vertex Pipeline and Vertex Feature Store, to manage models with confidence, making maintenance and repeated iteration of models less complex. Vertex AI is a managed machine learning platform that helps speed up the experimentation and deployment of AI models, making every stage of the workflow easier. Vertex AI has everything you need to deploy AI efficiently, including Notebook, data annotation, feature management, built-in Tensorboard, Managed training service, model prediction, continuous monitoring, etc.
Special emphasis is placed on the new MLOps capabilities introduced by Vertex AI to safely manage models with this suite of tools, which eliminates the complexity of complex user – defined model maintenance, improves reliability and repeatability, and enables team tracking and management of experiments. It also monitors feature quality and avoids common causes of training service skew, such as data distribution skew, enabling a reliable ML production deployment.
Vertex Piplines is a reusable training pipeline that helps data scientists share components and iterate quickly. Vertex Piplines is built with the Python SDK, so it is easy to write Kubeflow pipelines and TFX pipelines. It automatically tracks all metadata that can play a key role in debugging. Vertex AI also provides the flexibility to automate model training using AutoML, which allows for faster construction of complex models using custom model training and interpretable AI using table, text, image, or video data. In addition, Vertex AI integrates widely used open source frameworks such as SciKit-Learn and TensorFlow. In addition to AutoML, any ML framework of the user can also support training and prediction through our custom container.
TensorFlow Forum BBS
TensorFlow forum, where you can learn how to use TensorFlow to apply ML to your project.
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