Package: ztlearn
zeta-learnzeta-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.comPython versions:3.5 and aboveFree software:MIT licenseDependenciesnumpy >= 1.15.0matplotlib >= 2.0.0FeaturesKeras like Sequential API for building models.Built on Numpy and Matplotlib.Examples folder with readily implemented machine learning models.Installpip install ztlearnExamplesPrincipal 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
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:alt: digits pcaMNIST Dataset - PCA <https://github.com/jefkine/zeta-learn/blob/master/examples/mnist/mnist_pca.py>_.. image:: /examples/plots/results/pca/mnist_pca.png
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:alt: mnist pcaKMEANSK-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
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: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:: htmlDIGITS CNNInput 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: 835,338DIGITS Dataset Model Results.. image:: /examples/plots/results/cnn/digits_cnn_tiled_results.png
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:alt: digits cnn results tiledDIGITS Dataset Model Loss.. image:: /examples/plots/results/cnn/digits_cnn_loss_graph.png
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:alt: digits model lossDIGITS Dataset Model Accuracy.. image:: /examples/plots/results/cnn/digits_cnn_accuracy_graph.png
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:alt: digits model accuracyMNIST Dataset Model Summary <https://github.com/jefkine/zeta-learn/blob/master/examples/mnist/mnist_cnn.py>_.. code:: htmlMNIST CNNInput 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: 12,109,578MNIST Dataset Model Results.. image:: /examples/plots/results/cnn/mnist_cnn_tiled_results.png
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:alt: mnist cnn results tiledRegression
##########Linear Regression <https://github.com/jefkine/zeta-learn/blob/master/examples/boston/boston_linear_regression.py>_.. image:: /examples/plots/results/regression/linear_regression.png
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:alt: linear regressionPolynomial Regression <https://github.com/jefkine/zeta-learn/blob/master/examples/boston/boston_polynomial_regression.py>_.. image:: /examples/plots/results/regression/polynomial_regression.png
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:alt: polynomial regressionElastic Regression <https://github.com/jefkine/zeta-learn/blob/master/examples/boston/boston_elastic_regression.py>_.. image:: /examples/plots/results/regression/elastic_regression.png
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:alt: elastic regression