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What is TensorFlow?

TensorFlow is an open-source machine learning software library for numerical computation using data flow graphs. It was originally developed by Google.

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TensorFlow has proven to be a versatile tool for solving a wide range of problems across various industries. In the healthcare sector, …
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Tensor Flow Reviews

8 out of 10
December 18, 2018
Our organization was using it when it was 6 months old. It's a open source software by Google pretty robust. We use this AI to solve our …
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What is TensorFlow?

TensorFlow is an open-source machine learning software library for numerical computation using data flow graphs. It was originally developed by Google.

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What is TensorFlow?

TensorFlow is an open-source machine learning software library for numerical computation using data flow graphs. It was originally developed by Google.

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The TensorFlow community is thriving. We're thrilled to see the adoption and the pace of machine learning development by people all around the world. TensorFlow is an open-source project for everyone and we're looking forward to building it into something more useful in collab...
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TensorFlow Technical Details

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Reviews and Ratings


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(1-14 of 14)
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Score 9 out of 10
Vetted Review
Verified User
We use it for state of the art machine learning / deep learning models for real time inference in our products.
  • Model saving and prediction.
  • Training is faster.
  • It's well documented.
  • Create more high level APIs like keras.
  • Provide better support for keras.
  • Try to decrease model size.
In some cases, Java users can use it but it needs more support from Java developers because many companies are using Java in their existing products.
Score 7 out of 10
Vetted Review
Verified User
Tensorflow is a good intermediate level for building neural networks, or more generally, differentiable programming. Tensorflow v1 and Tensorflow v2 have very significant architectural differences: v1 is about defining a computational graph, upon which operations are performed (like "do one step of backprop" or "batch-evaluate on this data"), while v2 does more computations "live" and is built more like, essentially, a heavy-duty calculator with a differentiable history. v2 is tightly integrated with Keras, so if you intend to use industry-standard layers and architectures from Keras, then Tensorflow is probably your best bet. Both v1 and v2 allow you to define your own layers, or do other differentiable programming tasks; for instance, differentiable physics engines have been written in Tensorflow.
  • Integrating with Keras.
  • Working on CPU/GPU/TPU neutrally.
  • Exporting to TFLite for browsers or edge computing.
  • The massive changes between v1 and v2 can be confusing when looking at examples online.
  • TensorFlow is losing market ground to PyTorch and JAX.
If you're doing NN training, in particular, or if you have reasons why you might need customs layers or unusual architectures, then TF is probably your best bet. TF is also basically your only bet if you're planning on using any TPU edge devices, such as the Coral.
Score 5 out of 10
Vetted Review
Verified User
TensorFlow is used as a development platform for deep learning algorithms, in particular for:
1. Recommendations: selecting the best templates to recommend to users via email in the various countries the company has a market in, over 100 languages supported,
2. User feedback classification: when users provide feedback, natural language processing algorithms implemented in TensorFlow and Keras are used to classify issues so that stakeholders can identify the major issues with a product/product release,
3. Learning-to-rank for search: there is some development on improving search results by switching to deep learning algorithms from a gradient boosting one, and TensorFlow provides that capability, and
4. Computer vision: some experimentation performed on object detection and image classification.
  • TensorFlow is fairly easy to use, with adequate tutorials to get any user started quickly.
  • Tooling around TensorFlow, such as TensorBoard, is a gold standard: it has made the training and debugging process so much easier compared to most other deep learning platforms.
  • Community support for TensorFlow is very good. If there is a problem, there usually is an answer by just a little Googling. Also the documentation for TensorFlow is often top notch.
  • Prior to TensorFlow 2.0, setting up data ingestion for TensorFlow can be a huge pain. So much so that TensorFlow Lite and alternatives such as Keras make it more palatable. Things are changing with TensorFlow 2.0 though.
  • Some error messages from TensorFlow can be quite difficult to understand. For instance, a recent error using the dot product layer in TensorFlow 2.0 made it seem like there was a problem with data ingestion, but by downgrading to TensorFlow 1.14.0, the problem disappears.
  • Tooling with Bazel (our choice for a build tool) in our monorepo is a bit of a nightmare, partly because Bazel has poor Python support. However, we were able to integrate PyTorch easily with Bazel, but not TensorFlow.
  • Would love to have better bindings with the JVM, rather than just Python, considering that many companies have a JVM-based stack, making it easier to integrate.
TensorFlow is great for most deep learning purposes. This is especially true in two domains:
1. Computer vision: image classification, object detection and image generation via generative adversarial networks
2. Natural language processing: text classification and generation.

The good community support often means that a lot of off-the-shelf models can be used to prove a concept or test an idea quickly. That, and Google's promotion of Colab means that ideas can be shared quite freely. Training, visualizing and debugging models is very easy in TensorFlow, compared to other platforms (especially the good old Caffe days).

In terms of productionizing, it's a bit of a mixed bag. In our case, most of our feature building is performed via Apache Spark. This means having to convert Parquet (columnar optimized) files to a TensorFlow friendly format i.e., protobufs. The lack of good JVM bindings mean that our projects end up being a mix of Python and Scala. This makes it hard to reuse some of the tooling and support we wrote in Scala. This is where MXNet shines better (though its Scala API could do with more work).
Score 7 out of 10
Vetted Review
Verified User
Obviously, TensorFlow is a great opportunity for everyone who is interested in ML and DL area. We wanted to use TensorFlow in our company, majorly focusing on helping the Operation and Planning domains. Also, it is used as POC for Clearance domain. The purpose is quite similar, by using the DL Technics, through injecting large amount of historical data, learning the patterns, predicting the future trend or advice the best candidate suggestions. Some examples include Commercial Invoice recognition and classification, HS Code prediction, Transportation Time Prediction, Volume Density Prediction, Dimension Prediction.
  • Data pipeline implementation is quite good, loading large amounts of data and pre-process it in an efficient way is no more issue for us
  • It supports all major DL algorithms and network layouts such as ConvNets, RNN, LSTMs, Word2Vec, and even the latest transformer architecture
  • The abstraction for the device is perfectly done and its support seamlessly for multiple GPU and even TPU will bring a lot of performance gain for enterprise scoped solution while still keep the flexibility
  • The TensorBoard is amazing. I haven't seen a similar thing in other frameworks on the market. It allows us to quickly understand and debug the model with the info visualization which makes understanding much better
  • A very supportive community, which is the key for sharing the ideas and find the quick and best solutions
  • TensorFlow has its own model and terminology, which is not quite the same as the normal Python styled other frameworks, so in order to master it, the learning curve is a little bit steep, and as a by-product of the fast iteration and release, sometimes the documentation is not quite catching up
  • TensoFlow is based on Design Model Then Run Model concept, which means the model itself is static. Maybe it could also borrow some ideas from PyTorch, which is more intuitive and supports dynamic model building
I think TensorFlow is very good for people who want to dive deeper and have full control of the NN layer of details. It is a production-ready design and supports the distributed environment, so it is very good for mature and enterprise production. If the user is looking for reusing some standard models and wants to do some quick POC without too much in-depth understanding of the NN, then maybe something like Keras would be the better wrapper to begin with.
Score 8 out of 10
Vetted Review
Verified User
TensorFlow is the best deep learning library for visualization, training and tuning the model with a large dataset. We are using TensorFlow in the research and development department for the training of natural language, image processing and for the application of specific predictive models. It is also used by the production department to support and host the trained models at the application level.
  • Detailed and more functional implementation of various algorithms.
  • Great visualization under TensorFlow board for training models.
  • Multiple GPU support and availability of TPU to train large models.
  • Regular updates.
  • Large user community.
  • Performance issues on a low scale system.
  • Complex to debug for multi GPU training of a large model.
  • It is not easy to use for new developers compared to other libraries.
  • Implementation for complex architecture is difficult.
TensorFlow is well-suited for complex model training with a large dataset using multiple GPU's and provides training time mode visualization for fast debugging of the architecture. If you are doing a proof of concept for new architecture then it would not be a good choice considering implementation complexity and development time.
Anupam Mittal | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
We use TensorFlow for machine learning implementations. Primarily for predictive analysis and recommendation engines. It is being used at an organization level. Our objective is to use a large amount of publicly available data and make meaningful insights from it. It has helped us make better predictions and save costs.
We also use it for time series analysis to make predictions in the equity market. TensorFlow has been a powerful and easy to deploy tool for various algorithms.
  • Support for many libraries and programming languages.
  • Ability to use GPU and TPU - hence faster execution.
  • Low effort in getting started in development, hence ease of learning.
  • Graphic interface to create layers can help beginners.
  • Detailed tutorials on what goes behind the scenes in each layer. Currently, the tutorials don't focus on that.
  • Better support to integrate with files on the cloud.
Best suited for deployment on the cloud with the subscription-based model for execution infrastructure. For startups or for companies that do not have a strong data science staff, learning Tensorflow is easy because of the libraries and online tutorials availability.

It can be avoided when your development stack is Microsoft, as using Azure may provide better integration. Also, if the work requires detailed customization of the algorithm, it may be easier to work directly with Python code and TensorFlow may not help.
Nitin Pasumarthy | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Tensorflow (TF) is one of the Machine Learning (ML) libraries at LinkedIn. The necessary plumbing needed to deploy, maintain and monitor a TF project is under active development. It is currently used for building Wide and Deep Neural Networks, where training data is in the order of millions. However, in production, tree-based models or logistic regression are still popular.
  • A vast library of functions for all kinds of tasks - Text, Images, Tabular, Video etc.
  • Amazing community helps developers obtain knowledge faster and get unblocked in this active development space.
  • Integration of high-level libraries like Keras and Estimators make it really simple for a beginner to get started with neural network based models.
  • Profiling the TensorFlow (TF) graph for performance optimizations is still a challenge due to lack of proper documentation.
  • In our experiments with using TF-GPU on Kubernetes, we see constant memory issues causing nodes to crash.
  • There is still a significant learning curve and it's not as simple as other popular Python libraries. Having said that, the TF team and community are actively working on this problem.
  1. Whenever the problem has the demand for a neural networks based solution, Tensorflow (TF) is a great fit.
  2. The tf.dataset API makes it really simple to create complex data pipelines in a few lines of code.
  3. tf.estimators API abstracts all the complex computation graph creation logic making it very simple to get started.
  4. Eager execution makes it simple to develop a TF graph as debugging the code would be like any other imperative Python program.
  5. TF abstracts all the complexities of scaling it to multiple machines. It has various code and data distribution algorithms ready to use.
  6. Projects like TensorBoard make monitoring the training process really easy. It also gives the ability to view embeddings without any extra code. Their What-If is extremely useful for poking and understanding a black box model. It also has tools to visualize data to quickly check for anomalies.
  7. TF Autograph aims to covert any normal Python code into a distributed program which is quite handy to scale an existing code base.
December 18, 2018

Tensor Flow Reviews

Nisha murthy | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Our organization was using it when it was 6 months old. It's a open source software by Google pretty robust. We use this AI to solve our healthcare problems when it comes to patient monitoring, appointment cancellation, scheduling, and registration.
  • Multi-GPU support. It works; the documentation is simple and clear. You’ll still need to figure out how to divide and conquer your problem, but isn’t that part of the fun?
  • Training across distributed resources (i.e., cloud). As of v0.8, distributed training is supported.
  • Queues for putting operations like data loading and preprocessing on the graph.
  • Visualize the graph itself using TensorBoard. When building and debugging new models, it is easy to get lost in the weeds. For me, holding mental context for a new framework and model I’m building to solve a hard problem is already pretty taxing, so it can be really helpful to inspect a totally different representation of a model; the TensorBoard graph visualization is great for this.
  • Logging events interactively with TensorBoard. In UNIX/Linux, I like to use tail -f to monitor the output of tasks at the command line and do quick sanity checks. Logging events in TensorFlow allows me to do the same thing, by emitting events and summaries from the graph and then monitoring output over time via TensorBoard (e.g., learning rate, loss values, train/test accuracy).
  • Model checkpointing. Train a model for a while. Stop to evaluate it. Reload from checkpoint, keep training.
  • Performance and GPU memory usage are similar to Theano and everything else that uses CUDNN. Most of the performance complaints in the earlier releases appear to have been due to using CUDNNv2, so TensorFlow v0.8 (using CUDNNv4) is much improved in this regard.
  • RNNs are still a bit lacking, compared to Theano.
  • Cannot handle sequence inputs
  • Theano is perhaps a bit faster and eats up less memory than TensorFlow on a given GPU, perhaps due to element-wise ops. Tensorflow wins for multi-GPU and “compilation” time.
Tensor Flow can be used for training the Machine Learning model and mobile application that utilizes trained model and the built-in camera for medical images analysis.
It's improving imaging analytics and pathology. Machine learning can supplement the skills of human radiologists by identifying subtler changes in imaging scans more quickly, potentially leading to earlier and more accurate diagnoses.
Shambhavi Jha | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
I personally use TensorFlow for my work only. I used this software for about a year in my college during a research project on deep learning. Most of the time, I used this tool to develop a deep learning algorithm which operates around image and videos. Some of the examples where I have used this tool is image classification, video classification, etc.
  • TensorFlow is the best when you are doing some work around deep learning
  • You can also use this for natural language processing as it has lot of inbuilt functionality for this.
  • It also can be used to clean up the data and for data processing, as it provides lots of functionality for that too.
  • It would be much better if they could provide good documentation and easy ways to understand concepts.
  • It is difficult to understand the concept behind for example, Tensor Graph, which takes a lot of time.
  • As you have to write everything, it is time consuming to write the implementation of whole neural network. It would be better if they can provide some wrapper library to make things easier.
There are lots of scenarios where TensorFlow can be used efficiently. One of them is image processing and video processing that include classification, recognition, etc. It can also be used for natural language processing and building chatbots. As TensorFlow has LSTM in built, it will be easy to use this for doing NLP stuff.
Rounak Jangir | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
I have used TensorFlow during my college time and for some time in my professional career. Most of the time, I have used this to implement deep learning algorithms. More specifically, to build the classification algorithm and some NLP algorithms. In my company role I have used it to build a simple chatbot which can answer some question which is related to the trained document. And it is not used across the whole organisation but just by a few of us.
  • Fast to implement deep learning algorithm
  • Fast to train the big model, and easy to deploy on GPU as well
  • Provides a lot of inbuilt functionality which helps your development move faster
  • You can see dynamic graph, tensor graph, etc. which is helpful
  • Long learning curve—it takes a lot of time to learn its basics
  • Everything is not easy in this product. It takes a lot of time to develop algorithms, and it's difficult too.
If someone wants to develop or work around deep learning (artificial neural network), then it is a good choice to use. It is also useful for natural language processing. Implementing the LSTM is easy with this. Some examples of where it can be used are image classification, video classification, creating chatbots, etc.
October 30, 2018

Best deep learning tool

Ajay Shewale | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
I have used this tool for building a recommendation system. We have built a system to recommend flights to users so we used TensorFlow to build that. I personally have used this tool in a different sector like doing image processing, building an image recognition algorithm and implementing neural networks etc. This is being used by a part of our company not by the whole or across the company.
  • TensorFlow is very powerful to build the neural networks, it gives you the power to write your own implementation with full customisation.
  • It has inbuilt methods which helps a lot when it comes to writing your own implementation of neural networks
  • It has lots of inbuilt methods to do the data processing like reading data from a directory as classes using an image data generator etc.
  • First and biggest con is that it has a very tense learning curve. Understanding the concept of a tensor, a dynamic graph is difficult and takes a lot of time to learn
  • As compared to Keras, TensorFlow takes a lot of time to build and implement a neural network. You have to write everything by yourself.
There are lots of use cases to use this tool and also lot of cases where you should look to this. Like when it comes to building or playing with a deep learning algorithm like neural networks you should choose this one. But if you are implementing some other machine learning algorithm then definitely you have to check whether TensorFlow is a good choice or not. And you can also use this for NLP as well.
Jose Machicao, MSc | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
Currently, we use machine-learning models to develop solutions for our clients. But sometimes the usual models (decision tree, naive Bayes, random forest) are not helping us to find a suitable model, or it generates too many levels of modeling. Sometimes we use the pre-build neural networks included in some libraries. We are not yet experts in TensorFlow, but using Keras, it helped us to arrive to predictive models in a shorter time and with more accuracy.
  • Modeling for complex problems with large amounts of data
  • Modeling when the client is not interested in building the model patiently in levels
  • Guiding what we are doing wrong with other models
  • Too many lines of code for some actions
  • Not very intuitive for non-programming engineers
It is better when there is a lot of data available and the complexity of variables is high—for instance, when nobody has modeled that problem before. If there is not enough data, it does not work, or if it works, it is not going to help to model reality. It is also very good to test the performance of any other model even if the TensorFlow model itself is not going to be the solution for the client.
Kevin Perkins | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
We use TensorfFow to solve challenging machine learning problems at scale. TensorfFow fills in the gaps where other machine learning paradigms such as scikit learn fail. Tensorflow is used by several departments in our organization on many user facing business problems. Tensorflow provides an intuitive way to generate and train neural networks. There are also nice visualizations with TensorBoard.
  • Visualizing learning
  • Ease of use
  • Good documentation
  • Simplify distributed learning examples in the Github repo
  • Provide more tutorials on distributed training
TensorFlow is a must for deep learning. If deep learning is not necessary then other machine learning packages such as scikit-learn are a more appropriate choice. We have found that TensorFlow can be very useful in performing anomaly detection on time series data. TensorFlow provides easy aAPI for generating LSTM and CNN neural networks.
Gaurav Yadav | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
I have used TensorFlow to develop deep learning models. Recently, I have used TensorFlow to write deep neural network implementation to predict the product category(E-commerce product category) from a product image. Other than that, I have used TensorFlow many times, mostly to develop machine learning models. This is being used by one department of my organisation. In my current organisation, we have used TensorFlow to automate some tasks for an e-commerce merchant. In our case, merchants have to upload the product image and all the categories (like category, then sub-category, and then sub-sub-category), so we have developed a machine learning model using Tensorflow which will predict the product category using the product image.
  • First of all, it is fast. This machine library is faster as compared to other machine learning libraries like Theano.
  • It has lots of prebuilt tools in it for data processing, neural network layers like convolution layer, pooling layer etc. It also hase great prebuilt tools for data visualization.
  • Easy to deploy its model on GPU. We can train the model created by tensor flow on GPU.
  • It can be easily used with wrapper library like Keras which makes it easier to write a machine learning model.
  • Initially understanding this library is bit difficult. It has a steep learning curve.
  • Sometime the error messages are difficult to understand and debug. So that should be made clear such that even a beginner can solve the issue quickly.
  • Writing models with TensorFlow only is a bit difficult. So, it's easier to use this with a wrapper library like Keras.
The best suited scenario is when you want to develop a deep learning model consisting of a deep neural network, like doing something around images/video, which may include convolution network. Other than this, it can also be used to develop NLP models. But if you are developing conventional machine learning, I don't think this is much required as that can be done using Python libraries like sciPy.
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