It is a Python library for data science which allows user to evaluate mathematical operations including multi-dimensional arrays efficiently. This library mainly comes in handy while building deep learning projects. It works faster on GPU(Graphical Processing Unit) compared to CPU. For solving problems containing huge amounts of data, theano attains high speed and is a tough competitor to C implementations.
It is able to take structures and then convert them into very efficient code which uses NumPy and some other native libraries. It is designed for handling the types of computation required for large neural network algorithms which is used in Deep Learning. In deep learning, it is a popular library.
Many symbols which we use are in the tensor subpackage of Theano. While importing such packages, we give them a handy name.
Why use Theano Python Library?
Theano is a hybrid between numpy and sympy, which makes it a powerful library. Here are some advantages of theano:
- Stability Optimization: Theano is able to find some unstable expressions and can use stable means of evaluation.
- Execution speed optimization: Theano is able to make use of the recent GPUs and execute some parts of the expressions in our CPU or GPU, this makes it much faster.
- Symbolic Differentiation: Theano automatically creates symbolic graphs for computation of gradients.
Basics of Theano:
Theano helps us to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays. Some implementations of theano are as follows.
Subtraction of two scalars:
No output is provided as the assertion of two numbers matches the given number and results in a true value.
Addition of two scalars:
Addition of two matrices:
We will compute logistic curve given by,