How To Fix ‘Failed to load the native TensorFlow runtime’ Error

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If you have started an installation in Windows or you want to use an application, it can happen that the message “Failed to load the native Tensorflow runtime” is displayed. If this is the case, it is usually difficult to find the exact cause. However, there are some solutions that we will provide for you. The error ‘Failed to load the native TensorFlow runtime’ can have several causes.

Possible causes of the Error and Step by Step Guide on how to resolve it

Failed to load the native Tensorflow runtime due to missing library

  • A possible problem is the missing library MSVCP140.dll
  • You can add the library to your system by downloading the MSVCP140.dll file and saving it to the C:\Windows\System32 directory.

Failed to load the native TensorFlow runtime fixed by VC Redistributable

The error can also be caused by a missing Visual C++ Redistributable for Visual Studio 2015 installation.

  • Download Visual C++ Redistributable from the Microsoft website.
  • Install vc_redist.x64.exe. Now the error should no longer occur.
  • Now the error should no longer occur.

Fix TensorFlow error by version downgrade

A third way to fix the bug is to downgrade the Tensorflow version.

  • To do this enter the following command in your console: pip3 install–upgrade tensorflow==1.5.0
  • This will force the current TensorFlow version to be replaced by version 1.5.0.

The latest release available for developers is the version TensorFlow 2.0. It contains four central components:

  • TensorFlow Core – an open source library for the development and training of machine learning models
  • TensorFlow.js – a JavaScript library to train and provide models in the browser and on Node.js,
  • TensorFlow Lite – a lightweight library that allows models to be made available on mobile devices and embedded systems and
  • TensorFlow Extended – a platform for preparing data and training, validating and deploying models in production environments.

Developers who are already running TensorFlow 1.x projects can upgrade to the new version with the migration guide and the upgrade guide. Existing 1.x code can be executed in TensorFlow 2.0 without any changes, but the improvements of the new framework will be obvious :

TensorFlow 2.0 is altogether simpler and tidier than its predecessor versions. The open source Keras API written in Python plays a key role in the development and training of machine learning models. It significantly reduces the amount of code to be written: developers can build neural networks with Keras by using only one line of code per layer or even less if they take advantage of loop constructions. As an open source product, Keras can also be used for other backends such as MXNet (with Gluon), PyTorch, Scikit-learn and Spark MLlib. The latter two are more machine learning frameworks, but less suitable for deep learning. In general, however, users do not have to commit themselves to one framework; they can, for example, prepare data with Scikit-learn and train a model with TensorFlow.

TensorFlow is used in numerous Google products. It offers different abstraction levels to build and train models. With version 2.0 the Eager Execution, which has been offered for a long time, becomes the standard. It is a default feature that ensures that operations are executed immediately after a Python call ( Python 3.6, 3.7, 2.7) and not in a later session after nodes and edges have been added to the graphs.

How to install TensorFlow

Here is a quick guide on how to install Google’s open source software TensorFlow for developing deep learning algorithms using neural networks on your Windows system:

Installation and setup of a virtual machine

  • Download and install Docker Toolbox (Virtual Box)
  • Start the “Docker Quickstart Terminal“, which should be available as a shortcut after installation the initial start takes a relatively long time – all relevant settings are predefined
  • Completion is reached when an entry in the virtual box is possible (cursor after a $ character flashes)
  • Install TensorFlow Image for Docker
  • Here you can install a full version of TensorFlow or a light version:
  • Input at Docker for the light version: docker pull
  • Input at Docker for the full version: docker pull
  • The installation of TensorFlow can be checked by entering the following at Docker: docker images
  • The newly loaded image is displayed:
  • Finally, the following entry must be made to open the new image: winpty docker run -it Tensorflow_Image_Id
  • Update the TensorFlow Git resources
  • Update current container: apt-get update
  • Install Git: apt-get install git
  • change to the main directory: cd /
  • rename old tensorflow-folder: mv tensorflow tensorflow_old
  • get current TensorFlow resources: git clone -recurse-submodules
Video guide on how to install TensorFlow

What is Googles’ TensorFlow

As an open source tool for distributed database systems, Google’s TensorFlow forms an innovative basis for neural networks in the field of speech and image processing tasks. With this, Google is sending a clear signal that machine learning is no longer just seen as one of the many Silicon Valley hypes, but has arrived in reality. And the possibilities are enormous.

In recent decades, the approaches of artificial intelligence (AI) and statistics have been treated rather bad. On the one hand, because the statistics ended up being inaccurate, and on the other hand, because AI did not really produce anything. Not without reason, because for a long time researchers thought, that processes always had to be described and implemented with high precision.

But suddenly everything was different: E-commerce giant Amazon delighted the users during their customer tour with product recommendations that were similar to their individual search. How did they do it? Thanks to machine learning models, Amazon was able to offer additional buying incentives during the course of an information and decision phase for customers. The point is not that one customer is presented with exactly the same preferences as another, but only similar ones. Machine learning works precisely with such accumulations, approximations and error tolerances – in other words, with statistical methods.

Machine learning needs distributed systems

This is where AI in the form of neural networks comes into play. For this purpose, they must consist of many artificial neurons and synapses in order to be able to process even large inputs in a targeted way. So many in fact, that it does not fit onto a single computer. A distributed system is therefore required. Fortunately, large computing resources are now much cheaper than ever before.

In addition, the neural networks are connected one after the other and thus are able to map different levels of perception and understanding. For example, if the first level for image recognition deals with individual pixels, brightness and color, the next level uses the output of the previous level to recognize lines, edges, surfaces or curves. With each layer, the previous information is further abstracted. This means that lines become geometric structures and finally faces. By doing this, TensorFlow is able to learn to create neural networks.

TensorFlow – Accelerator for research

The open source machine learning software library TensorFlow is the direct successor of Google’s first deep learning tool DistBelief. “We hope that TensorFlow will enable the entire machine learning community, from scientific research to engineers and home users, to share ideas via program code in less time,” said Sundar Pichai, CEO of Google. In this way, research and development is accelerated by the technology, which is more refined and improved.

The improved flexibility and performance makes it much easier to train new, less tested models. Compared to other machine learning libraries, which are often offered with preset models, TensorFlow can develop and edit its own models. Furthermore, a translation of the code into other programming languages is not necessary. TensorFlow is not only executable on large distributed systems, but also on a variety of platforms such as smartphones, embedded devices and individual computers.

How does TensorFlow work?

Google Ai logo
Google AI in TensorFlow

TensorFlow allows to represent any neural network by directed cycle-free graphs. While the edges represent the inputs and outputs of the individual calculation steps, the nodes are responsible for processing all inputs to outputs. This means that mathematical operations are performed at the nodes of such a graph, while the graph edges represent the multidimensional data arrays (tensors) that communicate between the individual nodes.

For a so-called input, for example, an input vector (1st order tensor) can be created by sampling with spoken language, i.e. recording sound values at short intervals. Or a black and white image section can be converted into a pixel matrix (2nd order tensor) or a colour image into three pixel matrices with the components red, green and blue (3rd order tensor). Graphics cards are ideally suited for this purpose because they are optimized to enable very fast and large numbers of calculations. No wonder that TensorFlow supports GPU computing and can benefit greatly from it.

But with such graphs alone, image or speech recognition is far from being feasible. This is because the computers have to be trained in advance for the respective task. This is done by iteratively feeding the training data to the computers and simultaneously varying the weightings within the graph. This changes the output so that it increasingly approximates the targeted output value. These optimized procedures for approximating the desired output are one of the most important breakthroughs in neural networks in recent years.

In addition, separate test data should be used regularly to check whether the training is effective for any or other input data. If there is no more improvement in the results, the training is finished. A big plus of TensorFlow: The graph can be displayed and calculated beyond the existing computer limits. This is considered a decisive pioneering achievement by Google!

Application areas for TensorFlow

The TensorFlow library is suitable for a very wide range of applications. For example, for industry, business, the financial sector or medicine. The spectrum ranges, for example, from language translation to the early detection of skin cancer and the prevention of blindness in diabetics. Finally, anyone with the appropriate expertise can apply the intelligent TensorFlow technologies.

TensorFlow is of course also used for Google offers such as “Google Images” (image identification), “Google Maps” (map service) or “Google Translate”. This machine translation service uses a key algorithm of the TensorFlow technology by means of a phrase-based machine translation. Furthermore, the AI project “DeepMind” also uses the TensorFlow library. Within a very short time, TensorFlow has established itself as a generally proven standard that supports researchers, engineers and software developers in the development of innovative and powerful software applications.

More than just learning

Today, neural networks are still in their infancy phase in terms of technology advancements. Nevertheless, these are sometimes already able to learn by themselves and without supervised training. So can machines already learn as we humans do from an apparent – mysterious combination of memory and generalization? This tricky question is not so easy to answer from today’s perspective. To answer it, we would first have to learn to understand our human brain better. There are still a lot of questions unanswered here in particular.

But the Google team firmly believes in the great potential of this combination of “generalization” and “memorization“. At the moment, a series of tests is already underway to simultaneously train a comprehensive linear model (for remembering) with a deep neural network (for generalization). If these two strengths could be combined, the desire for human learning would be a step closer. Google calls such a technology “Wide and Deep Learning“. This approach is particularly suitable for generic regression and classification problems on a large scale – but with very little data. This should enable astonishing results that even go beyond pure recognition and memory into the field of creating new information. These advances will multiply in the future. Thanks to source openness, software-based technologies are helping to improve machine intelligence even faster.

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