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Tensorflow - a feature rich & easy to use distributed open source ML frameworkTensorflow (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.,10,Tensorflow (TF) has really simplified building complex models in a few lines of manageable code. TF Serving makes deployment very easy too. TensorBoard makes monitoring a pleasing task for features like charts, embeddings, histograms, what-if tools, etc. The minimal learning curve is absolutely worth the effort for all the benefits.,Tensor Flow ReviewsOur 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.,8,Positive Impact- As I mentioned before its open source. Very easy to learn for average programmer/ developer. We were able to design a POC model for understanding the patient appointment cancellation snd reasons behind it in 3 week time frame. Negative Impact- If you are using tensor flow for small project it works fine. If you are trying to build a model for face recognition it will be hard to program and train the system. It needs data to be processed before hand cannot learn on the go.,,TheanoA must have thing for deep learningI 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.,9,Learning is s bit difficult takes lot of time. Developing or implementing the whole neural network is time consuming with this, as you have to write everything. Once you have learned this, it make your job very easy of getting the good result.,Keras and MATLAB,Keras, MATLAB, Wolfram MathematicaMost advanced deep learning libraryI 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.,9,Helped me to develop building the chatbot. It takes time to learn and understand its concept of tensor and graph.,Keras and MATLAB,Splunk Enterprise, IntelliJ IDEA, JIRA SoftwareMy perception of the first year with TensorFlowCurrently, 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,7,Less modeling time More certainty about a model, and therefore fewer levels of modeling,Keras
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TensorFlow
23 Ratings
Score 9.0 out of 101
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TensorFlow Reviews

TensorFlow
23 Ratings
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Score 9.0 out of 101
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Nitin Pasumarthy profile photo
February 15, 2019

Review: "Tensorflow - a feature rich & easy to use distributed open source ML framework"

Score 10 out of 10
Vetted Review
Verified User
Review Source
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.
Read Nitin Pasumarthy's full review
Nisha murthy profile photo
December 18, 2018

TensorFlow: "Tensor Flow Reviews"

Score 8 out of 10
Vetted Review
Verified User
Review Source
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.
Read Nisha murthy's full review
Shambhavi Jha profile photo
November 08, 2018

TensorFlow Review: "A must have thing for deep learning"

Score 9 out of 10
Vetted Review
Verified User
Review Source
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.
Read Shambhavi Jha's full review
Rounak Jangir profile photo
November 05, 2018

TensorFlow Review: "Most advanced deep learning library"

Score 9 out of 10
Vetted Review
Verified User
Review Source
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.
Read Rounak Jangir's full review
Jose Machicao, MSc profile photo
September 25, 2018

Review: "My perception of the first year with TensorFlow"

Score 7 out of 10
Vetted Review
Verified User
Review Source
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.
Read Jose Machicao, MSc's full review
Ajay Shewale profile photo
October 30, 2018

TensorFlow Review: "Best deep learning tool"

Score 9 out of 10
Vetted Review
Verified User
Review Source
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.
Read Ajay Shewale's full review
No photo available
January 18, 2019

User Review: "TensorFlow, what else?"

Score 9 out of 10
Vetted Review
Verified User
Review Source
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.
Read this authenticated review
Kevin Perkins profile photo
August 17, 2018

TensorFlow Review: "A must for deep learning"

Score 8 out of 10
Vetted Review
Verified User
Review Source
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.
Read Kevin Perkins's full review
Gaurav Yadav profile photo
August 16, 2018

TensorFlow Review: "Best deep learning library which comes with lots of prebuilt features and visualisation tools"

Score 9 out of 10
Vetted Review
Verified User
Review Source
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.
Read Gaurav Yadav's full review

TensorFlow Scorecard Summary

About TensorFlow

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

TensorFlow Technical Details

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Mobile Application:No