Comments (2)
Hi Franken,
If you're using some real non scaled data like me then you'll be facing that problem.
In lines 23, 24
b_gradient += (-2/N) * (y - ((m_current * x) + b_current))
m_gradient += (-2/N) * x * (y - ((m_current * x) + b_current))
the gradient values are increasing each time with such great quantities because of the large real values stored in the variables x and y. Hence both b_gradient and m_gradient will not be sufficient in order to have such large values stored inside them even if you converted then into int64.
So, in order to solve such a problem,
you'll need to scale your data set values so they would range in between 1 and -1 or any other small values.
from linear_regression_live.
Hi Franken,
If you're using some real non scaled data like me then you'll be facing that problem.
In lines 23, 24
b_gradient += (-2/N) * (y - ((m_current * x) + b_current))
m_gradient += (-2/N) * x * (y - ((m_current * x) + b_current))
the gradient values are increasing each time with such great quantities because of the large real values stored in the variables x and y. Hence both b_gradient and m_gradient will not be sufficient in order to have such large values stored inside them even if you converted then into int64.So, in order to solve such a problem,
you'll need to scale your data set values so they would range in between 1 and -1 or any other small values.
Thank you It worked
from linear_regression_live.
Related Issues (13)
- Overflow when learning rate is too small
- Function compute_error_for_line_given_points not required.
- RuntimeWarning: overflow encountered in double_scalars
- csv file doesnt have proper column name HOT 2
- better way to compute loss
- After 1000 iterations b = nan, m = nan, error = nan HOT 4
- syntax error line 42 HOT 1
- Linear regression through matrix solution... need review and enhancements
- Not working in Tensor Flow HOT 2
- print function was not work without parenthesis in python3 HOT 1
- b/m gradient calculation HOT 3
- Check out my Notebook
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from linear_regression_live.