Published May 2, 2016
| Version 0.4.1
Software
Open
mlxtend: v0.4.1
Creators
- 1. Michigan State University
- 2. FANSI Motorsport
- 3. Easy Solutions
- 4. SurveyMonkey
Description
Version 0.4.1 (2016-05-01) New Features
- New TensorFlow estimator for Linear Regression (tf_regressor.TfLinearRegression)
- New k-means clustering estimator (cluster.Kmeans)
- New TensorFlow k-means clustering estimator (tf_cluster.Kmeans)
- Due to refactoring of the estimator classes, the init_weights parameter of the fit methods was globally renamed to init_params
- Overall performance improvements of estimators due to code clean-up and refactoring
- Added several additional checks for correct array types and more meaningful exception messages
- Added optional dropout to the tf_classifier.TfMultiLayerPerceptron classifier for regularization
- Added an optional decay parameter to the tf_classifier.TfMultiLayerPerceptron classifier for adaptive learning via an exponential decay of the learning rate eta
- Replaced old NeuralNetMLP by more streamlined MultiLayerPerceptron (classifier.MultiLayerPerceptron); now also with softmax in the output layer and categorical cross-entropy loss.
- Unified init_params parameter for fit functions to continue training where the algorithm left off (if supported)
Files
mlxtend-0.4.1.zip
Files
(7.2 MB)
Name | Size | Download all |
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md5:9ff893c6bdedcb7b2f5dc943e4e707cd
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Additional details
Related works
- Is supplement to
- https://github.com/rasbt/mlxtend/tree/0.4.1 (URL)