Introduction to Theano

Theano features:

• Strong NumPy integration
• Transparent GPU use
• Successful symbolic differentiation
• Enhancements to stability and speed
• Generating C code dynamically
• Extensive self-verification and unit testing

Code Implementation with Theano

import numpy
import theano.tensor as K
from theano import function
i = K.dscalar('i')
j = K.dscalar('j')
p = i + j
f = function([i, j], p)
f(5, 7)

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Output

Matrix Multiplication using Theano

import numpy as np
import theano
import theano.tensor as K

i = K.tensor3('i', dtype='int64')
j = K.matrix('j',dtype='int64')
p = K.dot(i,j)

f = theano.function([i,j],p)

x = np.array([[[1,2],[1,3]],[[2,2],[1,1]]], dtype=int)
y = np.array([[1,2],[1,3]], dtype=int)
print (f(x,y))

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Output

Theano vs other Machine language Libraries

Frequently Asked Question

Is Theano based on Python?

We can analyse mathematical operations, including multi-dimensional arrays, so effectively thanks to the Theano Python module.

Does Theano use GPU?

Theano offers a feature that enables the simultaneous usage of many GPUs in a single function.

Is Theano a machine learning tool?

Deep learning challenges including the definition, optimization, and evaluation of mathematical expressions are the focus of Theano, a low-level Python-based framework for scientific computing.

Is Theano a deep learning framework?

Due to the fact that Theano is a deep learning library, it is quite well known among academic researchers.

What is tensor in Theano?

They are specifically numerical arrays in Theano: You may define, optimise, and effectively evaluate mathematical formulas involving multi-dimensional arrays using the Theano Python module.

Conclusion

You understand the Python Library Theano , Theano Features, and Code Implementation with Output.

Below are the mentioned kike that will help you out in gaining more knowledge in Python.

• Popular Python Libraries
• Python Applications and Libraries 
• Basics of Python

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