https://github.com/jefkine/zeta-learn
zeta-lean: minimalistic python machine learning library built on top of numpy and matplotlib
https://github.com/jefkine/zeta-learn
autoencoder cnn data-science datasets deep-learning gan gru k-means-clustering lstm machine-learning matplotlib neural-networks numpy pca perceptron python regression rnn
Last synced: 5 days ago
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zeta-lean: minimalistic python machine learning library built on top of numpy and matplotlib
- Host: GitHub
- URL: https://github.com/jefkine/zeta-learn
- Owner: jefkine
- License: mit
- Created: 2018-03-08T20:07:03.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2020-09-22T03:46:19.000Z (over 5 years ago)
- Last Synced: 2025-11-29T10:53:10.603Z (about 2 months ago)
- Topics: autoencoder, cnn, data-science, datasets, deep-learning, gan, gru, k-means-clustering, lstm, machine-learning, matplotlib, neural-networks, numpy, pca, perceptron, python, regression, rnn
- Language: Python
- Homepage: https://zeta-learn.com/
- Size: 10.6 MB
- Stars: 39
- Watchers: 1
- Forks: 6
- Open Issues: 1
-
Metadata Files:
- Readme: README.rst
- License: LICENSE
Awesome Lists containing this project
README
zeta-learn
----------
zeta-learn is a minimalistic python machine learning library designed to deliver
fast and easy model prototyping.
zeta-learn aims to provide an extensive understanding of machine learning through
the use of straightforward algorithms and readily implemented examples making
it a useful resource for researchers and students.
* **Documentation:** https://zeta-learn.com
* **Python versions:** 3.5 and above
* **Free software:** MIT license
Dependencies
------------
- numpy >= 1.15.0
- matplotlib >= 2.0.0
Features
--------
- Keras like Sequential API for building models.
- Built on Numpy and Matplotlib.
- Examples folder with readily implemented machine learning models.
Install
-------
- pip install ztlearn
Examples
--------
Principal Component Analysis (PCA)
##################################
`DIGITS Dataset - PCA `_
=====================
.. image:: /examples/plots/results/pca/digits_pca.png
:align: center
:alt: digits pca
`MNIST Dataset - PCA `_
====================
.. image:: /examples/plots/results/pca/mnist_pca.png
:align: center
:alt: mnist pca
KMEANS
######
`K-Means Clustering (4 Clusters) `_
================================
.. image:: /examples/plots/results/kmeans/k_means_4_clusters.png
:align: center
:alt: k-means (4 clusters)
Convolutional Neural Network (CNN)
##################################
`DIGITS Dataset Model Summary `_
=============================
.. code:: html
DIGITS CNN
Input Shape: (1, 8, 8)
+---------------------+---------+--------------+
¦ LAYER TYPE ¦ PARAMS ¦ OUTPUT SHAPE ¦
+---------------------+---------+--------------+
¦ Conv2D ¦ 320 ¦ (32, 8, 8) ¦
¦ Activation: RELU ¦ 0 ¦ (32, 8, 8) ¦
¦ Dropout ¦ 0 ¦ (32, 8, 8) ¦
¦ BatchNormalization ¦ 4,096 ¦ (32, 8, 8) ¦
¦ Conv2D ¦ 18,496 ¦ (64, 8, 8) ¦
¦ Activation: RELU ¦ 0 ¦ (64, 8, 8) ¦
¦ MaxPooling2D ¦ 0 ¦ (64, 7, 7) ¦
¦ Dropout ¦ 0 ¦ (64, 7, 7) ¦
¦ BatchNormalization ¦ 6,272 ¦ (64, 7, 7) ¦
¦ Flatten ¦ 0 ¦ (3,136,) ¦
¦ Dense ¦ 803,072 ¦ (256,) ¦
¦ Activation: RELU ¦ 0 ¦ (256,) ¦
¦ Dropout ¦ 0 ¦ (256,) ¦
¦ BatchNormalization ¦ 512 ¦ (256,) ¦
¦ Dense ¦ 2,570 ¦ (10,) ¦
+---------------------+---------+--------------+
TOTAL PARAMETERS: 835,338
DIGITS Dataset Model Results
============================
.. image:: /examples/plots/results/cnn/digits_cnn_tiled_results.png
:align: center
:alt: digits cnn results tiled
DIGITS Dataset Model Loss
=========================
.. image:: /examples/plots/results/cnn/digits_cnn_loss_graph.png
:align: center
:alt: digits model loss
DIGITS Dataset Model Accuracy
=============================
.. image:: /examples/plots/results/cnn/digits_cnn_accuracy_graph.png
:align: center
:alt: digits model accuracy
`MNIST Dataset Model Summary `_
============================
.. code:: html
MNIST CNN
Input Shape: (1, 28, 28)
+---------------------+------------+--------------+
¦ LAYER TYPE ¦ PARAMS ¦ OUTPUT SHAPE ¦
+---------------------+------------+--------------+
¦ Conv2D ¦ 320 ¦ (32, 28, 28) ¦
¦ Activation: RELU ¦ 0 ¦ (32, 28, 28) ¦
¦ Dropout ¦ 0 ¦ (32, 28, 28) ¦
¦ BatchNormalization ¦ 50,176 ¦ (32, 28, 28) ¦
¦ Conv2D ¦ 18,496 ¦ (64, 28, 28) ¦
¦ Activation: RELU ¦ 0 ¦ (64, 28, 28) ¦
¦ MaxPooling2D ¦ 0 ¦ (64, 27, 27) ¦
¦ Dropout ¦ 0 ¦ (64, 27, 27) ¦
¦ BatchNormalization ¦ 93,312 ¦ (64, 27, 27) ¦
¦ Flatten ¦ 0 ¦ (46,656,) ¦
¦ Dense ¦ 11,944,192 ¦ (256,) ¦
¦ Activation: RELU ¦ 0 ¦ (256,) ¦
¦ Dropout ¦ 0 ¦ (256,) ¦
¦ BatchNormalization ¦ 512 ¦ (256,) ¦
¦ Dense ¦ 2,570 ¦ (10,) ¦
+---------------------+------------+--------------+
TOTAL PARAMETERS: 12,109,578
MNIST Dataset Model Results
===========================
.. image:: /examples/plots/results/cnn/mnist_cnn_tiled_results.png
:align: center
:alt: mnist cnn results tiled
Regression
##########
`Linear Regression `_
==================
.. image:: /examples/plots/results/regression/linear_regression.png
:align: center
:alt: linear regression
`Polynomial Regression `_
======================
.. image:: /examples/plots/results/regression/polynomial_regression.png
:align: center
:alt: polynomial regression
`Elastic Regression `_
=================
.. image:: /examples/plots/results/regression/elastic_regression.png
:align: center
:alt: elastic regression