TensorFlow Cheat Sheet: Why TensorFlow, Function & Tools,

Train your models with TensorFlow

Every tech enthusiast wants to master the complex discipline of Machine Learning. Acquiring and training datasets to allow a computer to learn patterns and make decisions accordingly can be overwhelming sometimes if you don’t know an easy way around. 

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Google came out with a solution and called it TensorFlow. It is an open-source machine learning framework used to tackle and implement some tricky large-scale machine learning and neural networking models to make the job of predicting future results easier. A part of 

ML models that use multi-layer neural networks are called deep learning. It was developed to boost Google’s deep neural network research and can now be seen in the advanced Google search suggestions. The search engine giant with the largest set of data in the world needed some efficient way to scale up to massive models and algorithms.

TensorFlow was launched in 2017 and the current version stands at 2.2. TL has undergone several changes since it was first offered to the public. Some of the changes include added support for deep learning in computer graphics and discontinuation of support for Python 2.

As a top open-source machine learning framework, TensorFlow enables developers and academics to create complicated models and solve challenging issues. A cheat sheet that summarizes important ideas, features, and recommended practices might be a helpful resource for practitioners who want to maximize the power of TensorFlow.

TensorFlow Basics

  • Installation and Setup: Setting up TensorFlow on your development environment is the first step towards harnessing its capabilities. The cheat sheet provides detailed instructions for installation and configuration, catering to different platforms and setups. It covers installing TensorFlow via pip, Anaconda, Docker, and virtual environments, ensuring that practitioners can easily get started with the framework regardless of their preferred setup.
  • Tensors and Operations: TensorFlow revolves around the concept of tensors, which are multidimensional arrays or matrices that contain data. The tensorflow cheat sheet further elaborates on tensors, explaining their importance as the primary data structure used in TensorFlow. It highlights the different types of tensors, including scalar, vector, matrix, and higher-dimensional tensors, along with examples for better comprehension.

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How it Works…

TensorFlow provides an easy-to-work-with Python frontend API to get along with the framework while the core is written in C++ to get the high-level performance. Python is an easy-to-learn and work-with language and has good support for various kinds of libraries to make development faster and convenient. 

It runs on a graph framework, thus making it cross-platform. It can be used from CPUs and GPUs to mobile systems.

The terminology gives a hint of its working

  • Tensor means an array or a matrix containing some data sets. So, you can make a flowchart of how data flows in a graph. 
  • The graph is a widely used data structure employed in various fields of computer science and is often used to handle complex data sets. It has a series of nodes that are connected through edges. 

The nodes describe a series of computation that needs to be performed while the edges are the multidimensional dataset on which the operations need to be performed. 

The graph was picked deliberately as it has many advantages that give the tool its abilities – like being able to run on different platforms, and easily deployable.

Google has its own custom TensorFlow Processing Unit (TPU) specifically designed to render the Tensor models that provide further acceleration to the computation.

Model Building

  • Building Computational Graphs: Extensive examples demonstrate how to build computational graphs using the TensorFlow Python API, highlighting the significance of graph visualization and debugging tools.
  • Layers and Architectures: The cheat sheet demonstrates the variety of pre-built layers and architectures offered by TensorFlow, allowing users to quickly assemble intricate neural networks for various tasks like image classification and reinforcement learning.

Training and Improvement

  • Loss Functions: To assist practitioners in choosing the best loss function for their particular machine learning issue, the TensorFlow cheat sheet gives an overview of popular loss functions, such as categorical cross-entropy, mean squared error, and binary cross-entropy.
  • Optimization Methodologies: The cheat sheet directs users in selecting the most appropriate optimizer depending on the demands of their model by providing a thorough selection of optimization techniques, including stochastic gradient descent (SGD), Adam, and RMSprop.

Deployment and Production

  • TensorFlow Serving: The tensorflow python cheat sheet introduces TensorFlow Serving, a powerful system for deploying trained models in production environments, facilitating scalable and efficient serving of TensorFlow models.
  • Model Conversion and Optimization: To ensure efficient inference on various platforms, the cheat sheet highlights techniques for model conversion and optimization, including quantization, pruning, and model compression.

Advanced Techniques and Tools

  • Transfer Learning: Exploring the concept of transfer learning, the cheat sheet demonstrates how to leverage pre-trained models in TensorFlow to accelerate model development and achieve higher accuracy.
  • TensorBoard Integration: The cheat sheet explains how to utilize TensorBoard, TensorFlow’s visualization tool, to track and visualize metrics, analyze model performance, and debug training processes effectively.

TensorFlow Ecosystem

  • TensorFlow Extended (TFX): Providing an overview of TensorFlow Extended, the cheat sheet introduces TFX as a comprehensive platform for end-to-end machine learning pipelines, encompassing data validation, preprocessing, model training, and model serving.
  • TensorFlow Hub: The cheat sheet showcases TensorFlow Hub, a repository of pre-trained models and reusable components, enabling practitioners to leverage state-of-the-art models and accelerate development.

Why TensorFlow?

  • Imagine you have a bunch of datasets that you wish to model but you can’t think of ways to efficiently do so or cannot figure out the how-to link all the pieces you have even with the plethora of algorithms at your disposal. With TensorFlow, you don’t need to worry about data abstraction. With a bunch of included algorithms and deep neural networks, building an application becomes way easier.
  • One of the most prominent features of TensorFlow is eager execution – an efficient way to debug the operations. Since visualization becomes easier with an interactive web-based dashboard, you can work on each graph operation separately.
  • All the different libraries included in this platform makes scaling much faster even over large datasets and across machines.
  • Being open-source and backed by Google, it is one of the most prominent deep neural network tools you can get your hands on.
  • One of the core ideas behind creating TensorFlow was under limiting processing power. So you can even run it on your mobile systems! 
  • There are tons of open-source models available for the platform that is bundled with both code and model weights to help you understand all the different aspects of this library. You can always find some models related to your workflow and perhaps even tune it using transfer learning.

Learn more: Tensorflow 2.0 Image Classification

Get most out of TensorFlow – The Tools

1. TensorBoard

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. It provides tracking and maintaining metrics such as loss and accuracy, displaying images, texts and model graphs, projecting embedding, and a lot more.

Read: The What’s What of Keras and TensorFlow

2. Neptune

Another way to track metrics through the integration of a library. It has out-of-the-box integration with TensorFlow and is an easy way to track model weights, parameters, and more.

3. What-if tool

A great tool to enhance the workflow with Tensor, What-if works just as it sounds. It can be used to compare multiple models within the same workflow, arrange data points by similarity, visualize inference results, test algorithms fairness results, and many more. A handy tool if you wish to get started with TensorFlow.

4. TensorFlow Playground

Quite the literal name, this tool allows you to ‘play’ with the neural networks of your model right in your browser. Having the functionalities like being able to choose the type of dataset, features, view layers, this tool can take you a good step ahead in training your models.

5. Datalab

If you intend to use Google cloud services to handle and train your models, then Google Datalab provides you with an environment based on Jupyter notebooks incorporating a bunch of tools like NumPy, Matplotlib, pandas in addition to TensorFlow being pre-installed and bundled together to ease out your work process.

Also check out: TensorFlow Project Ideas

6. Facets

Another data visualization tool to help you visualize your massive datasets, form connections, understand how different links interact with each other, compare the different datasets and the outcomes and even the states having the most traffic fatalities.

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Future Prospects

Alphabet CEO, Sundar Pichai has said that AI is more important than electricity or fire. Though unfathomable, the sentence of the leader captures a new reality. Handling data is the current and the next big thing, and anything that will make it easier to do so will stay here for a long time. 

Machine and Deep Learning are here to stay. There is already a debate going on if AI will take over the humans or what results could it lead to in the future – good or bad? But that does not deny the fact that it is the future. Even if there still exists a tiny pocket that is not already on the cloud, it will move there pretty soon and the companies who’ll embrace AI are likely to come out on top. This makes up a huge room for tools like TensorFlow. 

Companies are willing to spend millions to track and train datasets to stay ahead of their competitors. So, don’t be surprised if you see a bunch of TensorFlow like libraries hurdling your way in the near future. 


TensorFlow continues to be at the forefront of enabling professionals to create potent models and precise predictions as machine learning transforms several sectors. This cheat sheet is useful since it compiles key knowledge and recommendations for using TensorFlow. Use TensorFlow’s strength to realize the full potential of deep learning and machine learning. The tensorflow python cheat sheet provides guidance for data scientists and machine learning engineers to get started with TensorFlow.

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