Metadata-Version: 2.1
Name: tf-som
Version: 0.0.1
Summary: Self-organizing maps in tensorflow
Home-page: https://github.com/JacobFV/tf-som
Author: Jacob Valdez
Author-email: jacobfv@msn.com
License: MIT
Description: # tf-som
        Tensorflow self-organizing maps.
        ![](https://img.shields.io/badge/build-failing-red)
        ![](https://img.shields.io/badge/version-0.0.1-informational)
        
        Locally competitive algorithms demonstrate superior convergence to their supervised
        counterparts over a suite of tasks. Try them yourself:
        
        ```bash
        pip install tf-som
        ```
        
        ```python
        from .models import ConvNet
        
        # build unsupervised base
        unsupervised_base = ConvNet((H, W))
        
        # train unsupervised
        for x, _ in train_ds:
            unsupervised_base(x, training=True) 
        
        ...
        
        # build supervised head
        supervised_head = keras.Sequential([
            tfkl.Input(unsupervised_base.output_shape),
            tfkl.Conv2D(8, (3, 3), activation='relu'),
            tfkl.Flatten(),
            tfkl.Dense(N_classes, activation='softmax')
        ])
        
        # assemble full classifier
        unsupervised_base.trainable = False
        classifier = keras.Sequential([
            unsupervised_base,
            supervised_head
        ])
        classifier.compile('sgd', 'cross_entropy', 'accuracy')
        
        # train supervised
        classifier.fit(train_ds)
        
        # compare model sizes
        print(unsupervised_base.summary())
        print(supervised_head.summary())
        ```
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
