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

MNIST Dataset - PCA

KMEANS

K-Means Clustering (4 Clusters)

Convolutional Neural Network (CNN)

DIGITS Dataset Model Summary

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

DIGITS Dataset Model Loss

DIGITS Dataset Model Accuracy

MNIST Dataset Model Summary

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

Regression

Linear Regression

Polynomial Regression

Elastic Regression