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Difference between TensorFlow and Theano

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  • Last Updated : 14 May, 2021
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In this article, we will compare and find the difference between TensorFlow and Theano. Both these modules are used for deep learning and are often compared for their technology, popularity, and much more. Let’s see a detailed comparison between them.


It is a Python library and optimizing compiler for manipulating and evaluating mathematical expressions, especially those with the matrix-values. And Theano can also be defined as a library for Scientific Computing which was available in 2007, it can run on both CPU and GPU.

Advantages of Theano:

  • Keras, Lasagne, and Blocks like packages are built on Theano.
  • Raw Theano is a low-level product.
  • Having high-level modules like Keras, Blocks, Lasgne, etc, makes it more usable.

Drawbacks of Theano:

  • On AWS, it can be complex.
  • It can be run on a single GPU.
  • Requires large compile time for vast and complex models.
  • Error notices are complex, which makes debugging harder.


It is software that is used for open source contribution mainly for machine-learning programmers. TensorFlow is a symbolic math library that is used for machine learning applications.

Advantages of TensorFlow:

  • It works for algorithms like reinforcement learning and others.
  • Provides graph computing abstraction.
  • Parallelism of data and models is available.
  • It can run on a variety of CPUs and GPUs.

Drawbacks of TensorFlow:

  • Since it doesn’t accept matrix operations, copying these huge matrices is a time-consuming method.
  • Compared to other frameworks, it runs slow.
  • No pre-trained models are available.
  • Drops out of the program to load each new training batch in Python.
  • Not so adaptable.
  • On massive development programs, dynamic typing is vulnerable to errors.

Table of Difference between TensorFlow and Theano

  TensorFlow Theano
Execution Speed TensorFlow execution speed is slow when compared to Theano. But in the case of handling the tasks which require multiple GPU TensorFlow is faster. Theano performs tasks way faster than TensorFlow. Mainly the tasks that require a single GPU will run faster in Theano.
Technology TensorFlow lacks native windows support. It doesn’t support Lasagne. Theano has native windows support. It also supports High-Level Wrappers like Lasagne.
Documentation Tensorflow has lesser documentation when in comparison with Theano. Theano has more documentation compared to TensorFlow.
Compatibility TensorFlow runs specifically on Linux, macOS, Windows, and Android. Theano runs on cross-platform.
Popularity TensorFlow is one of the famous Deep Learning libraries and is mostly used for research purposes. Theano is an old Framework that is not used mostly. 
Inbuilt models TensorFlow does not have any pre-trained inbuilt models Theano is compatible with a deep learning library called Keras which contains some pre-trained models.


The interfaces in both frameworks are identical. TensorFlow, on the other hand, is a lot easier to use because it comes with a lot of monitoring tools. In terms of functionality and speed, Theano is better, but TensorFlow is best suited for deployment.

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