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Difference between TensorFlow and Theano
In this article, you will understand the significant differences between Tensorflow and Theano libraries.
It is an open-source end-to-end platform to build machine learning applications and was developed by researchers and developers at Google Brain.
Let us now see the features of TensorFlow −
Ecosystem of powerful add-on libraries − TensorFlow also supports an ecosystem of powerful add-on libraries and models to experiment with, including Ragged Tensors, TensorFlow Probability, Tensor2Tensor and BERT.
TensorFlow Serving − It is a flexible and high-performance serving system for machine learning models, designed for production environments. It runs ML models at production scale on the most advanced processors in the world, including Google's custom Tensor Processing Units (TPUs).
TensorFlow Extended − TensorFlow Extended (TFX) is an end-to-end platform for deploying production Machine Learning pipelines. If you need a full production ML pipeline, use the TensorFlow Extended.
TensorFlow Lite − TensorFlow Lite is a mobile library for deploying models on mobile, microcontrollers and other edge devices. For running inference on mobile and edge devices, use TensorFlow Lite.
State-of-the-art models − Build and train state-of-the-art models without sacrificing speed or performance. TensorFlow gives you the control with features like the Keras Functional API and Model Subclassing API for creation of complex topologies.
Robust ML Production − TensorFlow brings a direct path to production. Even if it is on servers, edge devices, or the web, TensorFlow lets you train and deploy your model easily, irrespective what language or platform you use.
Following are the Advantages of TensorFlow −
It is a general framework, and can be applied to other domains too.
It provides Python, and C++ APIs.
It has comprehensive and flexible tools, libraries, and community to build and deploy state-of-the-art Machine Learning applications.
It is available on Linux, Windows, Android, iOS, and macOS.
It also provides support for reinforcement learning, deep learning, NLP, image recognition, time series, and video detection.
It has excellent documentation, and a supportive community for contributors.
It provides parallelism in terms of data and models.
It supports execution on CPU and GPU.
Matrix operations cannot be performed.
It takes time to execute operations in comparison to other frameworks.
Dynamic typing is prone to errors in high scalability development.
Theano is a Python library that allows you to define, optimise, and evaluate mathematical expressions.
Following are the Advantages of Theano −
These expressions involve multi-dimensional arrays, and Theano works on them efficiently.
It has an efficient integration with NumPy, hence numpy.ndarray works well with Theano-compiled functions.
It allows GPU usage, and helps perform data intensive computations.
It also evaluates derivatives with one or multiple inputs.
It evaluates expressions quickly since it generates C code dynamically.
Irrespective of the value used in the mathematical expression, it evaluates and provides precise solutions.
Following are the disadvantages of Theano −
Debugging is tough since error messages are huge.
Large compilation time for complex models, which makes it difficult to maintain and work on them.
Theano vs Tensorflow
|What?||Theano is a Python library that allows you to define, optimise, and evaluate mathematical expressions.||TensorFlow is an open-source end-to-end platform to build machine learning applications and was developed by researchers and developers at Google Brain.|
|Compilation Time||The compilation time is higher than Tensorflow.||TensorFlow takes lesser compilation time.|
|CPU||Theano uses a single CPU.||TensorFlow uses one or more than one CPU.|
|Native Windows Support.||Theano has native Windows support.||TensorFlow does not have native Windows support.|
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