- Tensorflow 2 Cheat Sheets
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- Tensorflow 2 Cheat Sheet
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- Tensorflow 2 Cheat Sheet Pdf
- PSet 2 Released tomorrow 4/20 (due 5/5) Help us help you! Fill out class survey to give us feedback. Qiaojing will host Tensorflow on AWS setup session in office hours, Sundar 4/24, 4-6 pm, Gates B24 Will host special TensorFlow help session in my office hours, Tuesday 4/26, 1-3 pm, Huang basement.
- Learn more: Tensorflow 2.0 Image Classification. Get most out of TensorFlow – The Tools 1. As mentioned above, TensorFlow provides an efficient way of abstraction and TensorBoard is a tool to do so. Understanding and visualizing the graphs, parts of the graph, and the flow structure can be done easily with TensorBoard.
TensorFlow is an open source software library developed by Google for numerical computation with data flow graphs. This TensorFlow guide covers why the library matters, how to use it, and more.
Apr 23, 2019 - This cheat sheet covers TensorFlow 2.0 basics, exemplifying how to jump-start a machine learning project within just a few seconds in a cloud environment.
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TensorFlow was originally a deep learning research project of the Google Brain Team that has since become–by way of collaboration with 50 teams at Google–a new, open source library deployed across the Google ecosystem, including Google Assistant, Google Photos, Gmail, search, and more. With TensorFlow in place, Google is able to apply deep learning across numerous areas using perceptual and language-understanding tasks. (Note: This article about TensorFlow is also available as a free PDF download.)
This cheat sheet is an easy way to get up to speed on TensorFlow. We’ll update this guide periodically when news and updates about TensorFlow are released.
SEE: How to build a successful developer career (free PDF) (TechRepublic)
Executive summary
- What is TensorFlow? Google has the single greatest machine learning infrastructure in the world, and with TensorFlow, Google now has the ability to share that. TensorFlow is an open source library of tools that enable software developers to apply deep learning to their products.
- Why does TensorFlow matter? AI has become crucial to the evolution of how users interact with services and devices. Having such a powerful set of libraries available can enable developers to include this powerful deep learning evolution to their products.
- Who does TensorFlow affect? TensorFlow will have a lasting effect on developers and users. Since the library was made open source, it is available to all developers, which means their products can be significantly enhanced to bring a higher level of intelligence and accuracy to their products.
- When was TensorFlow released? TensorFlow was originally released November 9, 2015, and the stable release was made available on February 15, 2017. Google has now released TensorFlow 2.4, which includes a number of new features and profiler tools.
- How do I start using TensorFlow? Developers can download the source code from the TensorFlow GitHub repository. Users are already seeing its effects in the Google ecosystem.
SEE: How to implement AI and machine learning (ZDNet special feature) | Download the free PDF version (TechRepublic)
What is TensorFlow?
When you have a photo of the Eiffel Tower, Google Photos can identify the image. This is possible thanks to deep learning and developments like TensorFlow. Prior to TensorFlow there was a division between the researchers of machine learning and those developing real products; that division made it challenging for developers to include deep learning in their software. With TensorFlow, that division is gone.
TensorFlow delivers a set of modules (providing for both Python and C/C++ APIs) that enable constructing and executing TensorFlow computations, which are then expressed in stateful data flow graphs. These graphs make it possible for applications like Google Photos to become incredibly accurate at recognizing locations in images based on popular landmarks.
SEE: All of TechRepublic’s cheat sheets and smart person’s guides
G13 logitech g hub drivers. In 2011, Google developed a product called DistBelief that worked on the positive reinforcement model. The machine would be given a picture of a cat and asked if it was a picture of a cat. If the machine guessed correctly, it was told so. An incorrect guess would lead to an adjustment so that it could better recognize the image.
TensorFlow improves on this concept by sorting through layers of data called Nodes. Diving deeper into the layers would allow for more and complex questions about an image. For example, a first-layer question might simply require the machine to recognize a round shape. In deeper layers, the machine might be asked to recognize a cat’s eye. The flow process (from input, through the layers of data, to output) is called a tensor…hence the name TensorFlow.
What is TensorFlow 2.0?
Google is in the process of rolling out TensorFlow 2.0, which includes the following improvements:
- Helps make API components integrate better with tf.keras (a high-level interface for neural networks that runs on top of multiple backends).
- Includes TensorFlow.js version 1.0, which allows the use of off-the-shelf JavaScript models, can retrain existing JS models, and enables the building and training of models directly in JavaScript.
- Includes TensorFlow Federated, which is an open source framework for experimenting with machine learning (and other computations) using decentralized data.
- Includes TF Privacy, a library for training machine learning models with a focus on privacy for training data.
- Features eager execution, which is an imperative programming environment that evaluates operations immediately, without building graphs before returning concrete values.
- Uses tf.function, which allows you to transform a subset of Python syntax into portable, high-performance graphs, and improves performance and deployability of eager execution.
- Advanced experimentation will be made possible with new extensions Ragged Tensors (the TensorFlow equivalent of nested variable-length lists), TensorFlow Probability (a Python library built on TensorFlow that makes it easy to combine probabilistic models and deep learning), and Tensor2Tensor (a library of deep learning models and datasets).
- A conversion tool that automatically updates TensorFlow 1.x Python code so that it can be used with TensorFlow 2.0 compatible APIs (and flags cases where said code cannot be automatically converted).
SEE: Free machine learning courses from Google, Amazon, and Microsoft: What do they offer? (TechRepublic Premium)
Why does TensorFlow matter?
Machine learning is the secret sauce for tomorrow’s innovation. Machine learning, also called deep learning, is considered a class of algorithms that:
- Use many layers of nonlinear processing units for feature extraction and transformation; and
- are based on the learning of multiple levels of features or representations of the data; and
- learn multiple levels of representation corresponding to different levels of abstraction.
Tensorflow 2 Cheat Sheets
Thanks to machine learning, software and devices continue to become smarter. With today’s demanding consumers and the rise of big data, this evolution has become tantamount to the success of a developer and their product. And because TensorFlow was made open source, it means anyone can make use of this incredible leap forward brought to life by Google. In fact, TensorFlow is the first serious framework for deep learning to be made available through the Apache 2.0 license.
With developers and companies able to use the TensorFlow libraries, more and more applications and devices will become smarter, faster, and more reliable. TensorFlow will be able to sort through vast numbers of images at an unprecedented rate.
Because Google made TensorFlow open source, the libraries can be both improved upon and expanded into other languages such as Java, Lua, and R. This move brings machine learning (something heretofore only available to research institutes) to every developer, so they can teach their systems and software to recognize images or translate speech. That’s big.
Who does TensorFlow affect?
TensorFlow not only makes it possible for developers to include the spoils of deep learning into their products, but it makes devices and software significantly more intelligent and easier to use. In our modern, mobile, and 24/7 connected world, that means everyone is affected. Software designers, developers, small businesses, enterprises, and consumers are all affected by the end result of deep learning. The fact that Google created a software library that dramatically improves deep learning is a big win for all.
SEE: Research: Companies lack skills to implement and support AI and machine learning (TechRepublic Premium)
When was TensorFlow released?
TensorFlow was originally released November 9, 2015, and the stable release was made available on February 15, 2017. TensorFlow 2.0 alpha is available now, with the public preview coming soon. You can learn more about the TensorFlow 2.0 alpha in the official Get Started with TensorFlow guide.
The libraries, APIs, and development guides are available now, so developers can begin to include TensorFlow into their products. Users are already seeing the results of TensorFlow in the likes of Google Photos, Gmail, Google Search, Google Assistant, and more.
SEE: Git guide for IT pros (free PDF) (TechRepublic)
What new features are found in TensorFlow 2.4?
Tensorflow 2 Cheat Sheet Printable
Among the new features found in the latest release of TensorFlow include:
- The tf.distribute module now includes experimental support for asynchronous training models with ParameterServerStrategy and custom training loops. In order to get started with this strategy, read through this Parameter Server Training tutorial, which demonstrates how to setup ParameterServerStrategy.
- MultiWorkerMirroredStrategy is now a part of the stable API and implements distributed training with synchronous data parallelism.
- The Karas mixed precision API is now part of the stable API and allows for the user of 16-bit and 32-bit floating point types.
- The tf.keras.optimizers.Optimizer has been refactors, enabling the user of model.fit or custom training loops to write code that will work with any optimizer.
- The experimental support of a NumPy API subset, tf.experimental.numpy, has been introduced which enables developers to run TensorFlow accelerated NumPy code.
- New profiler tools have been added so developers can measure the training performance and resource consumption of TensorFlow models.
- TensorFlow now runs with CUDA 11 and cuDNN 8, which enables support for NVIDIA Ampere GPU architecture.
Competitors to TensorFlow
TensorFlow isn’t alone in the deep learning field; in fact, there are a number of other companies with machine learning frameworks, including the following.
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How do I start using TensorFlow?
The first thing any developer should do is read the TensorFlow Getting Started guide, which includes a TensorFlow Core Tutorial. If you’re new to machine learning, make sure to check out the following guides:
Developers can install TensorFlow on Linux, Mac, and Windows (or even install from source), or check out their various tools from the official TensorFlow GitHub page.
Tensorflow 2 Cheat Sheet
Finally, developers can take advantage of all the TensorFlow guides:
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TensorFlow Quick Reference Table – Cheat Sheet.
TensorFlow is very popular deep learning library, with its complexity can be overwhelming especially for new users. Here is a short summary of often used functions, if you want to download it in pdf it is available here:
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Tensorflow 2 Cheat Sheet Pdf
Import TensorFlow: | |
| import tensorflow as tf | |
Basic math operations: | |
| tf.add() | sum |
| tf.subtract() | substraction |
| tf.multiply() | multiplication |
| tf.div() | division |
| tf.mod() | module |
| tf.abs() | absolute value |
| tf.negative() | negative value |
| tf.sign() | return sign |
| tf.reciprocal() | reciprocal |
| tf.square() | square |
| tf.round() | nearest intiger |
| tf.sqrt() | square root |
| tf.pow() | power |
| tf.exp() | exponent |
| tf.log() | logarithm |
| tf.maximum() | maximum |
| tf.minimum() | minimum |
| tf.cos() | cosine |
| tf.sin() | sine |
Basic operations on tensors: | |
| tf.string_to_number() | converts string to numeric type |
| tf.cast() | casts to new type |
| tf.shape() | returns shape of tensor |
| tf.reshape() | reshapes tensor |
| tf.diag() | creates tensor with given diagonal values |
| tf.zeros() | creates tensor with all elements set to zero |
| tf.fill() | creates tensor with all elements set given value |
| tf.concat() | concatenates tensors |
| tf.slice() | extracts slice from tensor |
| tf.transpose() | transpose the argument |
| tf.matmul() | matrices multiplication |
| tf.matrix_determinant() | determinant of matrices |
| tf.matrix_inverse() | computes inverse of matrices |
Control Flow: | |
| tf.while_loop() | repeat body while condition true |
| tf.case() | case operator |
| tf.count_up_to() | incriments ref untill limit |
| tf.tuple() | groups tensors together |
Logical/Comparison Operators: | |
| tf.equal() | returns truth value element-wise |
| tf.not_equal() | returns truth value of X!=Y |
| tf.less() | returns truth value of X<Y |
| tf.less_equal() | returns truth value of X<=Y |
| tf.greater() | returns truth value of X>Y |
| tf.greater_equal() | returns truth value of X>=Y |
| tf.is_nan() | returns which elements are NaN |
| tf.logical_and() | returns truth value of ‘AND’ for given tensors |
| tf.logical_or() | returns truth value of ‘OR’ for given tensors |
| tf.logical_not() | returns truth value of ‘NOT’ for given tensors |
| tf.logical_xor() | returns truth value of ‘XOR’ for given tensors |
Working with Images: | |
| tf.image.decode_image() | converts image to tensor type uint8 |
| tf.image.resize_images() | resize images |
| tf.image.resize_image_with_crop_or_pad() | resize image by cropping or padding |
| tf.image.flip_up_down() | flip image horizontally |
| tf.image.rot90() | rotate image 90 degrees counter-clockwise |
| tf.image.rgb_to_grayscale() | converts image from RGB to grayscale |
| tf.image.per_image_standardization() | scales image to zero mean and unit norm |
Neural Networks: | |
| tf.nn.relu() | rectified linear activation function |
| tf.nn.softmax() | softmax activation function |
| tf.nn.sigmoid() | sigmoid activation function |
| tf.nn.tanh() | hyperbolic tangent activation function |
| tf.nn.dropout | dropout |
| tf.nn.bias_add | adds bias to value |
| tf.nn.all_candidate_sampler() | set of all classes |
| tf.nn.weighted_moments() | returns mean and variance |
| tf.nn.softmax_cross_entropy_with_logits() | softmax cross entropy |
| tf.nn.sigmoid_cross_entropy_with_logits() | sigmoid cross entropy |
| tf.nn.l2_normalize() | normalization using L2 Norm |
| tf.nn.l2_loss() | L2 loss |
| tf.nn.dynamic_rnn() | RNN specified by given cell |
| tf.nn.conv2d() | 2D convolutions given 4D input |
| tf.nn.conv1d() | 1D convolution given 3D input |
| tf.nn.batch_normalization() | batch normalization |
| tf.nn.xw_plus_b() | computes matmul(x,weights)+biases |
High level Machine Learning: | |
| tf.contrib.keras | Keras API as high level API for TensorFlow |
| tf.contrib.layers.one_hot_column() | one hot encoding |
| tf.contrib.learn.LogisticRegressor() | logistic regression |
| tf.contrib.learn.DNNClassifier() | DNN classifier |
| tf.contrib.learn.DynamicRnnEstimator() | Rnn Estimator |
| tf.contrib.learn.KMeansClustering() | K-Means Clusstering |
| tf.contrib.learn.LinearClassifier() | linear classifier |
| tf.contrib.learn.LinearRegressor() | linear regressor |
| tf.contrib.learn.extract_pandas_data() | extract data from Pandas dataframe |
| tf.contrib.metrics.accuracy() | accuracy |
| tf.contrib.metrics.auc_using_histogram() | AUC |
| tf.contrib.metrics.confusion_matrix() | confusion matrix |
| tf.contrib.metrics.streaming_mean_absolute_error() | mean absolute error |
| tf.contrib.rnn.BasicLSTMCell() | basic lstm cell |
| tf.contrib.rnn.BasicRNNCell() | basic rnn cell |
Placeholders and Variables: | |
| tf.placeholder() | defines placeholder |
| tf.Variable(tf.random_normal([3, 4], stddev=0.1) | defines variable |
| tf.Variable(tf.zeros([50]), name=’x’) | defines variable |
| tf.global_variables_initializer() | initialize global variables |
| tf.local_variables_initializer() | initialize local variables |
| with tf.device(“/cpu:0”): | pin variable to CPU |
| v = tf.Variable() | |
| with tf.device(“/gpu:0”): | pin variable to GPU |
| v = tf.Variable() | |
| sess = tf.Session() | run session |
| sess.run() | |
| sess.close() | |
| with tf.Session() as session: | run session(2) |
| session.run() | |
| saver=tf.train.Saver() | Saving and restoring variables. |
| saver.save(sess,’file_name’) | |
| saver.restore(sess,’file_name’) | |
Working with Data: | |
| tf.decode_csv() | converts csv to tensors |
| tf.read_file() | reads file |
| tf.write_file() | writes to file |
| tf.train.batch() | creates batches of tensors |
Tensorflow 2 Cheat Sheet Download
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If you want to look for more information, check some free online courses available at coursera.org, edx.org or udemy.com.
Recommended reading list:
| Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use scikit-learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply practical code examples without acquiring excessive machine learning theory or algorithm details |
| TensorFlow Machine Learning Cookbook This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP. Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production. What you will learn Become familiar with the basics of the TensorFlow machine learning library Get to know Linear Regression techniques with TensorFlow Learn SVMs with hands-on recipes Implement neural networks and improve predictions Apply NLP and sentiment analysis to your data Master CNN and RNN through practical recipes Take TensorFlow into production |
| Learning TensorFlow: A Guide to Building Deep Learning Systems TensorFlow is currently the leading open-source software for deep learning, used by a rapidly growing number of practitioners working on computer vision, Natural Language Processing (NLP), speech recognition, and general predictive analytics. This book is an end-to-end guide to TensorFlow designed for data scientists, engineers, students and researchers. With this book you will learn how to: Get up and running with TensorFlow, rapidly and painlessly Build and train popular deep learning models for computer vision and NLP Apply your advanced understanding of the TensorFlow framework to build and adapt models for your specific needs Train models at scale, and deploy TensorFlow in a production setting |
| TensorFlow for Machine Intelligence: A Hands-On Introduction to Learning Algorithms TensorFlow, a popular library for machine learning, embraces the innovation and community-engagement of open source, but has the support, guidance, and stability of a large corporation. Because of its multitude of strengths, TensorFlow is appropriate for individuals and businesses ranging from startups to companies as large as, well, Google. TensorFlow is currently being used for natural language processing, artificial intelligence, computer vision, and predictive analytics. TensorFlow, open sourced to the public by Google in November 2015, was made to be flexible, efficient, extensible, and portable. Computers of any shape and size can run it, from smartphones all the way up to huge computing clusters. This book is for anyone who knows a little machine learning (or not) and who has heard about TensorFlow, but found the documentation too daunting to approach. It introduces the TensorFlow framework and the underlying machine learning concepts that are important to harness machine intelligence. After reading this book, you should have a deep understanding of the core TensorFlow API. |
| Machine Learning with TensorFlow Being able to make near-real-time decisions is becoming increasingly crucial. To succeed, we need machine learning systems that can turn massive amounts of data into valuable insights. But when you're just starting out in the data science field, how do you get started creating machine learning applications? The answer is TensorFlow, a new open source machine learning library from Google. The TensorFlow library can take your high level designs and turn them into the low level mathematical operations required by machine learning algorithms. Machine Learning with TensorFlow teaches readers about machine learning algorithms and how to implement solutions with TensorFlow. It starts with an overview of machine learning concepts and moves on to the essentials needed to begin using TensorFlow. Each chapter zooms into a prominent example of machine learning. Readers can cover them all to master the basics or skip around to cater to their needs. By the end of this book, readers will be able to solve classification, clustering, regression, and prediction problems in the real world. |