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 <https://github.com/jefkine/zeta-learn/blob/master/examples/digits/digits_pca.py>_
.. image:: /examples/plots/results/pca/digits_pca.png :align: center :alt: digits pca
MNIST Dataset - PCA <https://github.com/jefkine/zeta-learn/blob/master/examples/mnist/mnist_pca.py>_
.. image:: /examples/plots/results/pca/mnist_pca.png :align: center :alt: mnist pca
KMEANS
K-Means Clustering (4 Clusters) <https://github.com/jefkine/zeta-learn/blob/master/examples/clusters/kmeans_cluestering.py>_
.. 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 <https://github.com/jefkine/zeta-learn/blob/master/examples/digits/digits_cnn.py>_
.. 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 <https://github.com/jefkine/zeta-learn/blob/master/examples/mnist/mnist_cnn.py>_
.. 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 <https://github.com/jefkine/zeta-learn/blob/master/examples/boston/boston_linear_regression.py>_
.. image:: /examples/plots/results/regression/linear_regression.png :align: center :alt: linear regression
Polynomial Regression <https://github.com/jefkine/zeta-learn/blob/master/examples/boston/boston_polynomial_regression.py>_
.. image:: /examples/plots/results/regression/polynomial_regression.png :align: center :alt: polynomial regression
Elastic Regression <https://github.com/jefkine/zeta-learn/blob/master/examples/boston/boston_elastic_regression.py>_
.. image:: /examples/plots/results/regression/elastic_regression.png :align: center :alt: elastic regression
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