TensorFlow contrib kernel_methods.

This module contains operations and estimators that enable the use of primal (explicit) kernel methods in TensorFlow. See also the tutorial on how to use this module to improve the quality of classification or regression tasks.

Kernel Mappers

Implement explicit kernel mapping Ops over tensors. Kernel mappers add Tensor-In-Tensor-Out (TITO) Ops to the TensorFlow graph. They can be used in conjunction with other layers or ML models.

Sample usage:

kernel_mapper = tf.contrib.kernel_methods.SomeKernelMapper(...)
out_tensor = kernel_mapper.map(in_tensor)
...  # code that consumes out_tensor.

Currently, there is a RandomFourierFeatureMapper implemented that maps dense input to dense output. More mappers are on the way.

Kernel-based Estimators

These estimators inherit from the tf.contrib.learn.Estimator class and use kernel mappers internally to discover non-linearities in the data. These canned estimators map their input features using kernel mapper Ops and then apply linear models to the mapped features. Combining kernel mappers with linear models and different loss functions leads to a variety of models: linear and non-linear SVMs, linear regression (with and without kernels) and (multinomial) logistic regression (with and without kernels).

Currently there is a KernelLinearClassifier implemented but more pre-packaged estimators are on the way.

Sample usage:

real_column_a = tf.contrib.layers.real_valued_column(name='real_column_a',...)
sparse_column_b = tf.contrib.layers.sparse_column_with_hash_bucket(...)
kernel_mappers = {real_column_a : [tf.contrib.kernel_methods.SomeKernelMapper(...)]}
optimizer = ...

kernel_classifier = tf.contrib.kernel_methods.KernelLinearClassifier(
    feature_columns=[real_column_a, sparse_column_b],
    model_dir=...,
    optimizer=optimizer,
    kernel_mappers=kernel_mappers)

# Construct input_fns
kernel_classifier.fit(...)
kernel_classifier.evaluate(...)