https://github.com/abhishekyana/zframework

Z Framework for Easy Deep Learning
https://github.com/abhishekyana/zframework

deep-learning machine-learning numpy python tensorflow

Last synced: about 2 months ago
JSON representation

Z Framework for Easy Deep Learning

• Host: GitHub
• URL: https://github.com/abhishekyana/zframework
• Owner: abhishekyana
• Created: 2017-09-15T10:57:33.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2017-10-09T10:56:17.000Z (over 7 years ago)
• Last Synced: 2024-12-17T04:49:49.111Z (about 2 months ago)
• Topics: deep-learning, machine-learning, numpy, python, tensorflow
• Language: Python
• Homepage:
• Size: 8.79 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
Z Framework
Ver 0.2

Z for Easy Deep Learning and Machine Learning

  Z is an Easy Open-Source tool designed to run on top of Numpy(CPU) and Tensorflow(GPU-in Beta).It is capable of performing the Deep Learning training with minimal DL knowledge.Just follow the basic data preprocessing tips and train your own Neural Network from scratch.

Use Z if you need:



Easy environment to train you model with minimal lines of active code.

Friendly User-Interface without huge commands and syntax

Efficient Code with Multiple options with clean visualization.



Features Z provides




MLP Models-> Preprocessing, Training, Inference and Evaluation.

RNNs and LSTMs Models for Time Series-> Preprocessing, Training, Inference and Evaluation.(Beta)

CNNs for Image Processing-> Preprocessing, Training, Inference and Evaluation.(Beta)

GANs for Generative models.



Activation functions implemented




ReLU

Leaky ReLU

Sigmoid

Softmax

Tanh



Loss Evaluation Functions



Cross Entropy Loss

Mean Sqaure Error



Goals for the future update versions..



More Models are to be implemented like CNNs, RNNs, LSTMs, GANs, HyperGANs etc

More Loss Functions for effiecient evaluation

GPU Support implementation based on Tensorflow.

Visualizing Tools for on the verification.

Read the full documentaion Here(BETA).