https://github.com/pankajarm/dl_framework
https://github.com/pankajarm/dl_framework
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/pankajarm/dl_framework
- Owner: pankajarm
- Created: 2017-03-13T18:26:12.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2017-03-13T18:30:14.000Z (about 8 years ago)
- Last Synced: 2025-03-02T09:39:41.172Z (3 months ago)
- Language: Jupyter Notebook
- Size: 13.7 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# dl_framework
A library that demonstrates training of data using stochastic gradient descent method
The dl_framework library is a part of Udacity's Nanodegree Program and has been prepared while pursuing the same
# Table of Contents
dl_framework.py
This file consists of the set of functions used to perform a basic back propogation in a neural network
nn_test.ipynb
This file consists of a sample neural network test
boston_housing_with_custom_dl_framework.ipynb
This file consists predetion of Boston Housing Data using our dl_framework
# Usage
Include the dl_framework.py in your root project and use the following classess as follows:
Input()
Use it to declare input nodes of neural network
Linear()
Use to declare a node performing the task of linear activation of form Y = XW+ b
Sigmoid()
Use to declare a node performing sigmodial activation
MSE
Use this node to calculate Mean Square Error
# Further functions are included to perform forward pass, backward pass and gradient descent
In boston_housing_with_custom_dl_framework.ipynb sci-kit learn library is used to resample the dataset to perform a Stochastic Gradient Descent.
The dl_framework library is designed for Stochastic Gradient Descent and hence the function for gradient descent is named as sgd_update