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What everybody ought to know about best python compiler-theano


Do you know about theano?

I’m sure you’ve heard about TensorFlow, keras and other libraries.

But what about theano?

What’s the theano in python?

In this blog, I’m going to cover all this stuff.

Let’s start with theano a giant python library.

Theano is a python compiler for solving mathematical and computational expressions.

As a result, it is compatible with the Numpy library.

To clarify, you can say that it uses NumPy symbolic expression.

The expression in it shows using the algebraic form.

So, how does it compute fast?

The reason behind this is that it uses CPU performance.

Meanwhile, it has an easier interaction with the GPU array from python.

So, you can use python code to handle GPU arrays outside of theano function.

Theano function used to compute the value.

As a result, a theano function is a callable object in python.

You can compute using theano.function .

It’s compatible with python 3.9.

History of theano you should know

Theano is an open-source project developed by University de Montreal.

The name indicates the ancient philosopher theano.

It has great features which are showing in the version release.

The first version, theano 1.0.0 released on 15 November 2017.

Its obsolete uses for deep learning research and creating deep learning models.

Features of theano that make perfects for machine learning

Theano features

Theano is a python library providing deep learning tools.

As a result, it’s giving symbolic expression for graphs in machine learning.

Likewise, to make a stable point on the graph you can use it.

To clarify, you can replace unstable points with symbolic expressions of theano.

Python is big and slow.

As a result, it does slow the implementation of models and methods.

To solve this problem theano will inwardly reorganise your computation.

As a result, it tries to compile code into C and give python to handle.

In the case of documentation, it contains a lot more detail.

In conclusion, you don’t have to worry about internal operations. instead, you can focus on output.

Likewise, it had less dependency on another model.

So, it’s easy to install on the server without root privilege.

Theano isn’t a machine learning library, it’s more than that. So, it’s a mathematical library that provides a tool for machine learning models.

As I mention that it provides mathematical tools.

As a result, it does automatic differentiation. you only have to tool the forward part. theano’ll calculate how to figure out various points on differentiation.

Compatablity with other libraries

For compatibility, it’s compatible with Keras as pre-trained models.

It supports high-level wrapper Lasagne.

As a result, the lasagne library builds and train the neural network.

If you want to know more, check out

Alike, it supports a block framework.

Block is a framework that supports making neural networks easier on top of theano.

To know more about blocks you can check out

In conclusion, you can say that it supports a high-level framework in machine learning.

GPU in theano for graphics

Where the system is viewed and programmed. There’s theano’s goal is to make computations on a higher level.

SO, the internal function compiler has the flexibility to take over the computation.

As a result, you can carry out calculations on the graphics cards.

In answer to this, you can use GPU(Graphics Processing Unit).

In conclusion, if you want to know more about it. Check out

Execution speed is Fast

Theano is a less dependent framework.

As a result, it uses Numpy and scipy library for mathematical operation.

For instance, Numpy uses a multi-dimensional array and matrix data structure.

Likewise, the scipy library uses a sparse matrix.

A sparse matrix is a memory-efficient data structure that holds a matrix with fewer non-zero elements.

Community support for you

Community support

Theano as a scientific project started in 2007.

Throughout, that time it spreads university to university and computer Forum.

As a result, it has a larger number of developers community.

The community is bigger than the TensorFlow community.

Meanwhile, this community’s committed to qualitative code.

It launches a small version of theano.

Also, if you’ve any query you can ask in the theano community.

It’s fast responsive.

Non-basic python feature that benefits you

Theano doesn’t use the old python feature.

For instance, theano uses new-style classes where it uses decorators.

As a result, it uses metaclasses for new-style classes.

Similarly, it uses the get and set method which the python automatic invokes.

Requirements for theano in machine learning

Most important, theano only supports installation through the conda environment.

In this section, you’ll see the python and their respective library supported versions.

For python you need

Python 2.4 is up to support with release 0.6.

Similarly, python3.3 up to support the release 0.9.

On 27th July of 2020, theano was supported python 3.9 with the release 1.0.5.

This release has no new feature but fixed many deprecation warnings.

For Numpy

It supports Numpy from 1.9.1 to 1.12

Although, you can use the prior version theano didn’t test it.

For scipy you can use

Theano uses scipy for sparse matrix.

As a result, it supports 0.14 to below 1.71 version in scipy.

Meanwhile, scipy>=0.8 version works but it shows a bug with a sparse matrix.

For BLAS(Basic Linear Algebra Subprogram)

BLAS doing linear algebraic operations such as vector addition, dot multiplication and matrix multiplication.

To install BLAS you can use MKL(Math Kernel Library) which is free through conda environment.

For instance, you can use the mkl-service package in conda.

If you want to know more about requirements check out here.

How it’s better than Numpy and python?

Theano is a mathematical compiler for evaluating complex and symbolic expressions.

Python is slow because it’s a high and interpreted language.

That means it executes line by line.

As a solution, theano uses g++ or nvcc to compile your program into CPU or GPU instruction, which is faster than python.

It builds an automatic symbolic graph for computing gradient.

Theano will replace the unstable expression with a more stable algorithm.

The sympy is a mathematical expression for graph plotting in python.

As a result, it’s near to theano. But theano uses a tensor expression for symbolic expression.

Theano tutorial for you

You can start theano with interactive sessions such as python or python.

In this session, you can start importing the theano with the import keyword.

Earlier, I’d told that it uses tensors for symbolic representation.

As a result, you can use import.theano as T. Here, you can write a handy expression such as T.

This is a starting point of theano.

If you’ve done it precisely you can check out theano/tutorial,

Also, if you’ve any problem with the installation guide. you can check out

API for theano( to communicate with modules)

The API in short application interface programming. It enables the application to share data and functionality easily and securely.

Likewise, theano needs API for interacting modules.

You can use modules to express the graph.

At https://theano-/API/docs/ you can check out the theano’s API.

In conclusion, you can say that API communicates with modules easily.

Subsitute for theano that you can use

Theano is a machine learning library. As a result, it uses graph models.

For this, I’m going to show you alternatives for theano.


Image source:

PyTorch is a machine learning library for python.

As a result, it provides ease of use and flexibility with the help of the torchscript.

Torchscript is an optimised and ordered model written in PyTorch code.

Likewise, for the model’s speed and optimization it transitions to graph models.

As a result, its functionality running in a C++ environment.

Meanwhile, for operation’s speed and optimisation, it uses GPU(Graphics Processing Unit).

In conclusion, you can say that PyTorch is a python library for machine learning.

To know more about the PyTorch you can check out here.


image source:

Scikit-learn is a machine learning library for the python language.

As a result, it’s written in python language.

Meanwhile, it uses Numpy for numeric expression.

Similarly, it uses scipy for scientific calculation and matplotib for plotting a graph.

To improve the performance, it’s some algorithms written in Cython.

Cython is a framework created for increasing the speed of python influenced by C syntax.

As a result, it has some features such as data analytics, model analytics, clustering and regression.

You can check out Scikit-learn here.


Keras is an API deep learning framework.

As a result, it uses a consistent and simple API.

Similarly, it’s deeply integrated with low-level tensor flow functionality.

In error, it provides clear feedback upon user error.

Meanwhile, it runs on TensorFlow or theano.

For clarity, you can say it begins with the model building of data collection.

It enables you to develop high functionality workflow where you can customize functionality.

If you’re curious about Keras you can check out here.



It’s an open-source and deep learning framework.

Firstly, it focused on speed, expression and modularity.

Secondly, for configuration, it doesn’t need hard code.

Thirdly, it easily switches between CPU and GPU.

To know more about it you can check out here.


It’s an end-end open-source platform for machine learning.

As a result, it provides stable python3.7 with CAPIs.

Similarly, it has libraries, a flexible, comprehensive ecosystem of tools and community resources.

With all of the above, you can easily build a model’s API with Keras.

Meanwhile, you can implement a deep neural network with it.

In conclusion, you can say that TensorFlow is a great alternative for theano.

Theano vs TensorFlow what the difference?

Theano was purely created in python. Whereas, TensorFlow was Created in C++ and python.

Meanwhile, theano allows users to optimise and evaluate graphs based upon multi-dimensional arrays.

On the contrary, TensorFlow is for gradient-based machine learning and has a flexible architecture.

Theano is faster than TensorFlow in comparison to user experience.

Whereas, TensorFlow is faster than theano in deployment.

Theano runs on a single GPU. On contrary, TensorFlow runs on multiple CPUs and GPU.

Most importantly, TensorFlow support Keras for API.

Meanwhile, Theano supports Keras, lasagne and blocks.


Now, theano is dead?

Theano is a python library for deep learning framework.

It’s developed by the University of de Montreal in 2007.

Certainly, on 15 November 2017, the release of theano 1.0.0, its development ceased.

As a result, you can say theano is dead.

Secondly, it’s not funded by high techs such as Google and Microsoft.

As a result, its development is slower than other libraries such as TensorFlow and Keras.

Whereas, TensorFlow and Keras is a research project of google.

Although, it’s compatible with Keras and other libraries.

In conclusion, you can say that theano is old and has rich features. But it faced strong competition from industry players.

As a result, it ceased its development in 2017. Although it’s continuously developing.

But it lost its position in the face of other libraries.

Who developed theano library and why?

The theano is a python library developed at the University de Montreal in 2007.

it’s written in python and CUDA.

As a result, it’s a deep learning framework making a model and evaluating the expression.

Most importantly, it’s developed for machine learning algorithms.

What’s theano package for machine learning?

Theano package is a python library for machine learning.

As a result, it uses Numpy symbolic expression for deploying numerical expression.

Similarly, it uses Keras for deploying high-level API.

Does keras use theano?

Theano uses Keras for Its high-level API.

It’s a python network for deep neural networks.

Similarly, Keras supports multiple backends such as theano.

Is TensorFlow based upon theano?

Tensor is a machine learning library created for numerical expression and data modelling.

Meanwhile, it’s created from C++ OR python.

As a result, it uses Multiple GPUs at a time.

Whereas, theano is a python library for mathematical expression and graphs.

It’s written in python and Cuda language.

Also, TensorFlow created some functionality from theano.

But it’s not the inheritance of theano.

Meanwhile, both libraries support Keras for High APIs.

So, you can say that both libraries have similarities.

But in the end, both have different values.

What’s theano 0.9.0 version?

Theano 0.9.0 is the final version of theano.

As a result, it has many improvements. such as interfaces changes and bug fixes.

You can check out this version here.

What’s theano PyMC3 and why you need it?

PyMC3 is an open-source probabilistic framework to execute the graph.

In the case of theano, it uses theano for computing gradient via auto differentiation.

To know more about checking out the docs here.


Theano is a python library for executing a mathematical and symbolic expression.

As a result, it uses Numpy for numerical expression.

Alike, it uses Keras for high-level API.

It uses GPU and CPU for performance speed.

As a result, it’s faster than pure Python.

Also, it uses block and lasagne libraries for compatibility.

It has rich features in deep learning.

But it faced industrial competition from TensorFlow and other libraries.

As a result, its development speed is slow.

Because of this, developers preferring other libraries such as TensorFlow and Keras.

Although, it’s compatible with both libraries.

So, what do you think about theano? It’ll remain in future?OR TensorFlow or other libraries will replace it?

I’m leaving this question for you;- What’s the future of theano?

Comment on it!!

Also, if you liked it please share this blog post. This will help me.

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