LostTech.TensorFlow : API Documentation

Type KMeansClustering

Namespace tensorflow.contrib.factorization

Parent Estimator

Interfaces IKMeansClustering

An Estimator for K-Means clustering.

Example: ``` import numpy as np import tensorflow as tf

num_points = 100 dimensions = 2 points = np.random.uniform(0, 1000, [num_points, dimensions])

def input_fn(): return tf.compat.v1.train.limit_epochs( tf.convert_to_tensor(points, dtype=tf.float32), num_epochs=1)

num_clusters = 5 kmeans = tf.contrib.factorization.KMeansClustering( num_clusters=num_clusters, use_mini_batch=False)

# train num_iterations = 10 previous_centers = None for _ in xrange(num_iterations): kmeans.train(input_fn) cluster_centers = kmeans.cluster_centers() if previous_centers is not None: print 'delta:', cluster_centers - previous_centers previous_centers = cluster_centers print 'score:', kmeans.score(input_fn) print 'cluster centers:', cluster_centers

# map the input points to their clusters cluster_indices = list(kmeans.predict_cluster_index(input_fn)) for i, point in enumerate(points): cluster_index = cluster_indices[i] center = cluster_centers[cluster_index] print 'point:', point, 'is in cluster', cluster_index, 'centered at', center ```

The `SavedModel` saved by the `export_savedmodel` method does not include the cluster centers. However, the cluster centers may be retrieved by the latest checkpoint saved during training. Specifically, ``` kmeans.cluster_centers() ``` is equivalent to ``` tf.train.load_variable( kmeans.model_dir, KMeansClustering.CLUSTER_CENTERS_VAR_NAME) ```




Public static methods

KMeansClustering NewDyn(object num_clusters, object model_dir, ImplicitContainer<T> initial_clusters, ImplicitContainer<T> distance_metric, ImplicitContainer<T> random_seed, ImplicitContainer<T> use_mini_batch, ImplicitContainer<T> mini_batch_steps_per_iteration, ImplicitContainer<T> kmeans_plus_plus_num_retries, object relative_tolerance, object config, object feature_columns)

Creates an Estimator for running KMeans training and inference.

This Estimator implements the following variants of the K-means algorithm:

If `use_mini_batch` is False, it runs standard full batch K-means. Each training step runs a single iteration of K-Means and must process the full input at once. To run in this mode, the `input_fn` passed to `train` must return the entire input dataset.

If `use_mini_batch` is True, it runs a generalization of the mini-batch K-means algorithm. It runs multiple iterations, where each iteration is composed of `mini_batch_steps_per_iteration` steps. Each training step accumulates the contribution from one mini-batch into temporary storage. Every `mini_batch_steps_per_iteration` steps, the cluster centers are updated and the temporary storage cleared for the next iteration. Note that: * If `mini_batch_steps_per_iteration=1`, the algorithm reduces to the standard K-means mini-batch algorithm. * If `mini_batch_steps_per_iteration = num_inputs / batch_size`, the algorithm becomes an asynchronous version of the full-batch algorithm. However, there is no guarantee by this implementation that each input is seen exactly once per iteration. Also, different updates are applied asynchronously without locking. So this asynchronous version may not behave exactly like a full-batch version.
object num_clusters
An integer tensor specifying the number of clusters. This argument is ignored if `initial_clusters` is a tensor or numpy array.
object model_dir
The directory to save the model results and log files.
ImplicitContainer<T> initial_clusters
Specifies how the initial cluster centers are chosen. One of the following: * a tensor or numpy array with the initial cluster centers. * a callable `f(inputs, k)` that selects and returns up to `k` centers from an input batch. `f` is free to return any number of centers from `0` to `k`. It will be invoked on successive input batches as necessary until all `num_clusters` centers are chosen. * `KMeansClustering.RANDOM_INIT`: Choose centers randomly from an input batch. If the batch size is less than `num_clusters` then the entire batch is chosen to be initial cluster centers and the remaining centers are chosen from successive input batches. * `KMeansClustering.KMEANS_PLUS_PLUS_INIT`: Use kmeans++ to choose centers from the first input batch. If the batch size is less than `num_clusters`, a TensorFlow runtime error occurs.
ImplicitContainer<T> distance_metric
The distance metric used for clustering. One of: * `KMeansClustering.SQUARED_EUCLIDEAN_DISTANCE`: Euclidean distance between vectors `u` and `v` is defined as \(||u - v||_2\) which is the square root of the sum of the absolute squares of the elements' difference. * `KMeansClustering.COSINE_DISTANCE`: Cosine distance between vectors `u` and `v` is defined as \(1 - (u. v) / (||u||_2 ||v||_2)\).
ImplicitContainer<T> random_seed
Python integer. Seed for PRNG used to initialize centers.
ImplicitContainer<T> use_mini_batch
A boolean specifying whether to use the mini-batch k-means algorithm. See explanation above.
ImplicitContainer<T> mini_batch_steps_per_iteration
The number of steps after which the updated cluster centers are synced back to a master copy. Used only if `use_mini_batch=True`. See explanation above.
ImplicitContainer<T> kmeans_plus_plus_num_retries
For each point that is sampled during kmeans++ initialization, this parameter specifies the number of additional points to draw from the current distribution before selecting the best. If a negative value is specified, a heuristic is used to sample `O(log(num_to_sample))` additional points. Used only if `initial_clusters=KMeansClustering.KMEANS_PLUS_PLUS_INIT`.
object relative_tolerance
A relative tolerance of change in the loss between iterations. Stops learning if the loss changes less than this amount. This may not work correctly if `use_mini_batch=True`.
object config
See tf.estimator.Estimator.
object feature_columns
An optionable iterable containing all the feature columns used by the model. All items in the set should be feature column instances that can be passed to `tf.compat.v1.feature_column.input_layer`. If this is None, all features will be used.

Public properties

object ALL_DISTANCES_dyn get; set;

object CLUSTER_CENTERS_VAR_NAME_dyn get; set;

object CLUSTER_INDEX_dyn get; set;

object config get;

object config_dyn get;

object COSINE_DISTANCE_dyn get; set;

object KMEANS_PLUS_PLUS_INIT_dyn get; set;

object model_dir get;

object model_dir_dyn get;

object model_fn get;

object model_fn_dyn get;

object params get;

object params_dyn get;

object PythonObject get;

object RANDOM_INIT_dyn get; set;

object SCORE_dyn get; set;


Public fields

string SCORE

return string


return string


return string